Skip to main content
Springer logoLink to Springer
. 2023 Jan 19;128(2):222–233. doi: 10.1007/s11547-023-01596-8

Structured reporting of computed tomography in the polytrauma patient assessment: a Delphi consensus proposal

Vincenza Granata 1, Roberta Fusco 2, Diletta Cozzi 3,4, Ginevra Danti 3,4, Lorenzo Faggioni 5,, Duccio Buccicardi 6, Roberto Prost 7, Riccardo Ferrari 8, Margherita Trinci 8, Michele Galluzzo 8, Francesca Iacobellis 9, Mariano Scaglione 10, Michele Tonerini 11, Francesca Coppola 12, Chandra Bortolotto 13, Damiano Caruso 14, Eleonora Ciaghi 15, Michela Gabelloni 5, Marco Rengo 16, Giuliana Giacobbe 9, Francesca Grassi 17, Luigia Romano 9, Antonio Pinto 18, Ferdinando Caranci 17, Elena Bertelli 3, Paolo D’Andrea 19, Emanuele Neri 4,5, Andrea Giovagnoni 20,21, Roberto Grassi 4,17, Vittorio Miele 3,4
PMCID: PMC9938818  PMID: 36658367

Abstract

Objectives

To develop a structured reporting (SR) template for whole-body CT examinations of polytrauma patients, based on the consensus of a panel of emergency radiology experts from the Italian Society of Medical and Interventional Radiology.

Methods

A multi-round Delphi method was used to quantify inter-panelist agreement for all SR sections. Internal consistency for each section and quality analysis in terms of average inter-item correlation were evaluated by means of the Cronbach’s alpha (Cα) correlation coefficient.

Results

The final SR form included 118 items (6 in the “Patient Clinical Data” section, 4 in the “Clinical Evaluation” section, 9 in the “Imaging Protocol” section, and 99 in the “Report” section). The experts’ overall mean score and sum of scores were 4.77 (range 1–5) and 257.56 (range 206–270) in the first Delphi round, and 4.96 (range 4–5) and 208.44 (range 200–210) in the second round, respectively.

In the second Delphi round, the experts’ overall mean score was higher than in the first round, and standard deviation was lower (3.11 in the second round vs 19.71 in the first round), reflecting a higher expert agreement in the second round. Moreover, Cα was higher in the second round than in the first round (0.97 vs 0.87).

Conclusions

Our SR template for whole-body CT examinations of polytrauma patients is based on a strong agreement among panel experts in emergency radiology and could improve communication between radiologists and the trauma team.

Supplementary Information

The online version contains supplementary material available at 10.1007/s11547-023-01596-8.

Keywords: Radiology report, Structured report, Polytrauma, Computed tomography

Introduction

Trauma is the main cause of death for patients younger than 45 years old in Western Countries [1, 2], with a considerable proportion of all polytrauma due to car crashes. The number of casualties continues to rise, and according to the World Health Organization, every year the lives of approximately 1.3 million people are cut short because of a road traffic crash [3].

The optimal management of polytrauma patients requires a national trauma organization, which should define every level of the system, including all units of the trauma team [3, 4].

The suitable management of polytrauma patients can be challenging, and an efficient treatment requires that it begins at the accident site and should be maintained in all treatment phases. Therefore, treatment should be operated by a multidisciplinary team directed by a trauma surgeon to adequately manage severe injuries [4]. The multidisciplinary team should recognize as soon as possible life-threatening injuries or hemodynamically unstable patients according to vital parameters and classify them as polytrauma or non-polytrauma [5].

A decisive feature in the evolution of polytraumatized patient treatment is the possibility to obtain a rapid diagnosis, thanks to the opportunity to perform a whole-body computed tomography (CT) examination [57]. However, regarding which diagnostic tool to employ in polytrauma patients, the tenth edition of the Advanced Trauma Life Support (ATLS) suggests CT only if indicated, not by default, and only to assess specific body regions [8]. In contrast, the newest guidelines of the European Society of Emergency Radiology (ESER) endorse the approach proposed by ATLS in non-polytrauma patients and suggest whole-body CT as the diagnostic tool to use in polytrauma patients [9].

Currently, the optimal CT study protocol for polytrauma is a matter of debate [1018]. Another critical issue is an efficient communication of imaging data to the trauma team, given the need to report trauma-related injuries as soon as possible [1925]. The tool utilized to communicate radiological data to the trauma team is the radiological report. Traditionally, radiology reports are free text reports (FTR) based on descriptive communication, which could lead to confusion or even improper patient management if the report is unclear and difficult to understand for style and content [2632]. This can be especially true in the emergency setting, where time is crucial and life-threatening conditions are usually dealt with, highlighting the need to organize FTR into structured reports (SR) [3336]. SR allows to employ a checklist where all relevant items for a particular disease are reported, to avoid missing crucial data [3744]. This structure can improve communication with referring clinicians by favoring quality and standardization of radiological reports. In this context, several radiological societies [including the Radiological Society of North America (RSNA) and the Italian Society of Medical and Interventional Radiology (SIRM)] have promoted the use and diffusion of SR in clinical practice [45, 46].

The aim of our study is to develop a SR template for whole-body CT imaging of polytrauma patients.

Methods

Expert panel

We performed a multi-round consensus-building Delphi exercise to create a complete SR template based on whole-body CT examinations for the assessment of polytrauma patients.

Following extensive debate within a working team of seven SIRM radiologist members experienced in emergency radiology, a first draft of the SR template for the reporting of polytrauma CT examinations was created.

A panel of 18 experts was set up, including members from the Italian College of Emergency Radiologists and the Imaging Informatics Chapter of SIRM. The panelists revised the initial draft iteratively to obtain a final consensus on SR.

Selection of the Delphi domains and items

The panelists evaluated literature data on the main scientific databases, including PubMed, Scopus and Google Scholar, to assess papers published from December 2000 to June 2022 on: (a) polytrauma, (b) CT protocols in emergency settings, and (c) Structured Radiology Reports. In addition, they assessed the latest guidelines on the management of traumatized patients and on trauma classification by major scientific societies [47, 48]. Each panelist developed and shared the list of Delphi items via emails and/or teleconferences.

The working group divided the SR into four sections: (a) Patient Clinical Data, (b) Clinical Evaluation, (c) Imaging Protocol and (d) Report. The Report section included the template and a sub-section, where it was possible to add the most significant (key) images.

Two Delphi rounds were carried out. During the first round, each panelist independently contributed to refining the SR draft by means of online meetings or email exchanges. The level of panelists’ agreement for each SR model was tested in the second Delphi through a Google Forms® questionnaire shared by email. Each expert expressed individual comments for each specific template section by using a five-point Likert scale (1 = strongly disagree, 2 = slightly disagree, 3 = slightly agree, 4 = generally agree, 5 = strongly agree).

After the second Delphi round, the last version of the SR was generated on the dedicated RSNA website (www.radreport.org) by using a T-Rex template format, in line with IHE (Integrating Healthcare Enterprise) and the MRRT (Management of Radiology Report Templates) profiles, accessible as open source software, with the technical support of Exprivia® (Exprivia SpA, Bari, Italy). These determine both the format of radiology report templates using version 5 of Hypertext Markup Language (HTML5) and the transporting mechanism to request, retrieve and stock these schedules. The radiology report was structured by using a series of “codified queries” integrated in the preselected sections of the T-Rex editor [49].

Statistical analysis

The responses of each panel member were exported in Microsoft Excel® format to facilitate data collection and statistical analysis.

All panel member ratings for each section were analyzed with descriptive statistics measuring the mean score, standard deviation, and sum of scores. An average score of 3 was considered good and a score of 4 excellent.

To measure the internal consistency of panel member ratings for each section of the report, a quality analysis based on the mean correlation between items with Cronbach's correlation coefficient alpha (Cα) was performed [50, 51]. Cα provides a measure of the internal consistency of a test or scale and is expressed as a number between 0 and 1. Internal consistency describes the extent to which all items in a test measure the same concept. Cα was determined after each round.

The closer the coefficient Cα to 1.0, the greater the internal consistency of the elements of the scale. A coefficient alpha (α) > 0.9 was considered excellent, α > 0.8 good, α > 0.7 acceptable, α > 0.6 questionable, α > 0.5 poor, and α < 0.5 unacceptable. However, in the iterations an α of 0.8 was considered a reasonable target for internal reliability.

Data analysis was performed using Matlab Statistics Toolbox® (The MathWorks Inc., Natick, MA, USA).

Results

Structured report

The final SR form (Appendix 1) included 118 items, of which 6 in the “Patient Clinical Data” section, 4 in the “Clinical Evaluation” section, 9 in the “Imaging Protocol” section, and 99 in the “Report” section.

The “Patient Clinical Data” section should be automatically imported from the HIS/RIS and includes anthropometric data (such as weight, height, BMI, BSA, and age class).

The “Clinical Evaluation” section should also be automatically imported from the HIS/RIS and includes data on the type of trauma (such as road trauma, precipitation, burn). In addition, this section comprises data on pathophysiological criteria (e.g., GCS < 13, systolic arterial pressure < 90 mmHg, respiratory rate > 29/min, oxygen saturation in room air < 90%) and anatomical criteria (e.g., sensory/motor deficit, chest trauma with mobile parietal flap, penetrating injury, proximal fracture of 2 or more long bones, amputation proximal to elbow or knee).

The “Imaging Protocol” section consists of data on CT brand and model, contrast study protocol (including data on post-contrast acquisitions), contrast medium (active principle, commercial name, volume, flow rate, iodine concentration), and ongoing adverse events.

The “Report” section was created for vascular, thorax and abdominal lesions, according to the latest version of the American Association for the Surgery of Trauma (AAST) injury scoring scales [47], with the goal to provide an injury grade that reflects severity, guides management and aids in prognosis assessment. Regarding spinal trauma, the AO Spine Thoracolumbar and Subaxial Injury Classification systems [48], which are the result of a systematic assessment and revision of the Magerl classification [52], were used. This section includes all possible trauma-related findings and the injury grade. In addition, data on the type of vascular trauma (dissection, thrombosis, pseudoaneurysm, active bleeding) were included, as well as data on pleural effusion, pneumothorax, pneumomediastinum, pericardial effusion, peritoneal effusion, pneumoperitoneum, and other findings (fat embolism, foreign bodies, or incidental trauma-unrelated findings).

Consensus agreement

Table 1 reports the single scores and the sum of scores of panel experts for the SR in the first Delphi round, whereas Table 2 reports their single scores and sum of scores for the SR in the second round.

Table 1.

Single scores and sum of scores of SR panelists (first Delphi round)

Panelist # 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Mean SD
A1. Anthropometric data 5 5 5 5 3 5 5 5 5 5 5 2 3 5 5 5 5 4 4.56 0.92
B1. Clinical information—type of trauma 4 5 5 5 5 5 3 5 5 5 5 5 3 5 5 5 5 5 4.72 0.67
B2. Clinical information—pathophysiological criteria 4 5 5 5 2 5 2 5 5 4 5 4 2 2 2 5 5 5 4.00 1.33
B3. Clinical information—anatomical criteria 4 5 5 5 3 5 1 5 5 3 5 3 2 2 2 5 5 5 3.89 1.41
C1. Examination data 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4.94 0.24
C2. Contrast medium 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5.00 0.00
C3. Adverse events 5 5 5 5 5 5 3 5 5 5 5 5 5 5 5 5 5 5 4.89 0.47
D1. Cranial theca 5 5 5 5 5 5 3 3 5 5 5 5 5 5 5 5 5 4 4.72 0.67
D2. Extra-axial spaces 5 5 5 5 5 5 5 3 5 5 5 5 5 5 5 5 5 5 4.89 0.47
D3. Brain tissue 5 5 5 5 5 5 3 3 5 5 5 5 5 3 3 5 5 5 4.56 0.86
D4. Cerebellum and brainstem 5 5 5 5 5 5 4 3 5 5 5 5 5 3 3 5 5 5 4.61 0.78
D5. Midline 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5.00 0.00
D6. Ventricular system 5 5 5 5 5 5 4 3 5 5 5 5 5 5 5 5 5 5 4.83 0.51
D7. Associated findings 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5.00 0.00
E1. Vascular cervical injury 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 5 5 5 4.89 0.32
F1. Pleuro-pericardial effusion, pneumothorax/pneumomediastinum, tracheobronchial shaft lesions 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 5 5 4.83 0.38
F2. Chest wall injury 5 5 5 5 5 5 5 5 5 5 4 5 5 3 3 5 5 5 4.72 0.67
F3. Cardiac injury 4 5 5 5 5 5 2 5 5 5 5 5 4 2 2 5 5 5 4.39 1.14
F4. Lung injury 5 5 5 5 5 5 5 5 5 5 4 5 5 4 4 5 5 5 4.83 0.38
F5. Thoracic vascular injury 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3 5 5 5 4.78 0.65
F6. Esophageal injury 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3 5 5 5 4.78 0.65
F7. Diaphragmatic lesion 5 5 5 5 5 5 5 5 5 5 4 5 5 3 3 5 5 5 4.72 0.67
G1. Peritoneal effusion, pneumoperitoneum 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 4 5 5 4.83 0.38
G2. Abdominal vascular injury 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3 5 5 5 4.78 0.65
G3. Splenic injury 5 5 5 5 5 5 5 5 5 5 4 5 5 4 4 5 5 5 4.83 0.38
G4. Liver injury Peripheral vascular injury I4. Other findings and conclusions J1. Meaningful key images 5 5 5 5 5 5 5 5 5 5 4 5 5 4 4 5 5 5 4.83 0.38
G5. Extrahepatic biliary lesion 5 5 5 5 5 5 5 5 5 4 5 5 5 3 3 5 5 5 4.72 0.67
G6. Pancreatic lesion 5 5 5 5 5 5 5 5 5 5 4 5 5 4 3 5 5 5 4.78 0.55
G7. Gastric lesion 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3 5 5 5 4.78 0.65
G8. Duodenal injury 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 5 5 5 4.89 0.32
G9. Small intestine injury 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 5 5 5 4.89 0.32
G10. Colonic lesion 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 5 5 5 4.89 0.32
G11. Rectal injury 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 5 5 5 4.89 0.32
G12. Adrenal gland injury 5 5 5 5 5 5 4 5 5 5 4 5 5 3 3 5 5 5 4.67 0.69
G13. Kidney injury 5 5 5 5 5 5 5 5 5 5 0 5 5 3 3 5 5 5 4.50 1.29
G14. ureteral injury 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3 5 5 5 4.78 0.65
G15. Bladder injury 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 5 5 5 4.89 0.32
G16. Urethral injury 4 5 5 5 5 5 2 5 5 4 5 5 5 3 3 4 5 5 4.44 0.92
G17. Uterine lesion (non-pregnant patient) 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 5 5 5 4.89 0.32
G18. Uterine lesion (pregnant patient) 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 5 5 5 4.89 0.32
G19. Fallopian tube injury 5 5 5 5 5 5 5 5 5 5 5 5 5 2 2 5 5 5 4.67 0.97
G20. Ovarian lesion 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3 5 5 5 4.78 0.65
G21. Vaginal injury 4 5 5 5 5 5 5 5 5 5 5 5 5 3 3 5 5 5 4.72 0.67
G22. Vulvar lesion 4 5 5 5 5 5 5 5 5 4 5 4 4 5 5 5 5 5 4.78 0.43
G23. Testicular lesion 5 5 5 5 5 5 5 5 5 5 5 3 4 4 4 5 5 5 4.72 0.57
G24. Scrotal injury 5 5 5 5 5 5 5 5 5 5 5 3 4 4 4 5 5 5 4.72 0.57
G25. Penile lesion 5 5 5 5 5 5 5 5 5 5 5 3 4 4 4 5 5 5 4.72 0.57
H1. Cervical spine 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 5 5 5 4.89 0.32
H2. Dorso-lumbar spine 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 5 5 5 4.89 0.32
H3. Sacro-coccyx 5 5 5 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 4.94 0.24
H4. Pelvic girdle 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 5 5 4.94 0.24
I1. Upper limb fracture 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5.00 0.00
I2. Fracture of the lower limb 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5.00 0.00
I3. Peripheral vascular injury 5 5 5 5 5 5 5 5 5 5 5 5 5 3 3 5 5 5 4.78 0.65
Sum of scores 263 270 270 270 263 270 245 260 270 264 258 257 255 207 206 266 270 267 257.28 19.66

Table 2.

Single scores and sum of scores of SR panelists (second Delphi round)

Panelist # 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Mean SD
A1. Anthropometric data 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4.94 0.24
B1. Clinical information—type of trauma 5 5 5 5 5 5 5 5 4 5 5 5 5 5 5 5 5 5 4.94 0.24
B2. Clinical information—pathophysiological criteria 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5.00 0.00
B3. Clinical Information—Anatomical Criteria on Physical Exam 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5.00 0.00
C1. Examination data 5 5 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 4.94 0.24
C2. Contrast medium 5 5 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 4.94 0.24
C3. Adverse events 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5.00 0.00
D1. Cranial theca 5 4 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 4.89 0.32
D2. Extra-axial spaces 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4.94 0.24
D3. Brain tissue 5 4 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4.94 0.24
D4. Midline 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5.00 0.00
D5. Ventricular system 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5.00 0.00
D6. Associated findings 5 5 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 4.94 0.24
E1. Vascular cervical injury 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 5 5 4.89 0.32
F1. Pleuro-pericardial effusion, pneumothorax/pneumomediastinum, tracheobronchial shaft lesions 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5.00 0.00
F2. Chest wall injury 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 5 5 4.94 0.24
F3. Cardiac injury 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5.00 0.00
F4. Lung injury 5 5 5 5 5 4 5 5 5 5 5 5 5 5 5 4 5 5 4.89 0.32
F5. Thoracic vascular injury 5 5 5 5 5 4 5 5 5 5 5 5 5 5 4 4 5 5 4.83 0.38
F6. Esophageal injury 5 5 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 4.94 0.24
F7. Diaphragmatic lesion 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5.00 0.00
G1. Peritoneal effusion, pneumoperitoneum 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 5 5 4.94 0.24
G2. Abdominal vascular injury 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 4 5 5 4.89 0.32
G3. Splenic injury 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5.00 0.00
G4. Liver injury 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5.00 0.00
G5. Extrahepatic biliary lesion 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5.00 0.00
G6. Pancreatic lesion 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 5 5 4.94 0.24
G7. Injury of hollow viscera 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5.00 0.00
G8. Adrenal lesion 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 5 5 4.94 0.24
G9. Kidney injury 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5.00 0.00
G10. Ureteral injury 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5.00 0.00
G11. Bladder injury 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5.00 0.00
G12. Urethral injury 5 5 5 5 5 4 5 5 5 5 5 5 5 5 5 4 5 5 4.89 0.32
G13. Uterine lesion (non-pregnant patient) 5 5 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 4.94 0.24
G14. Uterine lesion (pregnant patient) 5 5 5 5 5 4 5 5 5 5 5 5 5 5 5 5 5 5 4.94 0.24
H1. Cervical spine 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5.00 0.00
H2. Dorso-lumbar spine 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5.00 0.00
H3. Sacro-coccyx 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5.00 0.00
H4. Pelvic girdle 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5.00 0.00
I1. Upper limb fracture 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5.00 0.00
I2. Fracture of the lower limb 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5 5.00 0.00
I3. Peripheral vascular injury 5 5 5 5 5 5 5 5 5 5 5 5 5 5 4 5 5 5 4.94 0.24
Sum of scores 210 207 210 209 210 200 210 210 209 210 210 210 210 210 206 201 210 210 208.44 3.11

In both Delphi rounds, all sections received more than a good rating. In the first round, the overall mean score of the experts (n = 18) and the sum of scores for SR were 4.77 (range 1–5) and 257.56 (range 206–270), respectively (Table 1). In the second round, the overall mean score of the experts (n = 18) and the sum of scores for SR were 4.96 (range 4–5) and 208.44 (range 200–210), respectively (Table 2).

The experts’ overall mean score in the second Delphi round was higher than the overall mean score of the first round, along with a lower standard deviation value (19.71 in the first round versus 3.11 in the second round), highlighting the higher expert agreement on SR reached in the second round.

Cα was 0.87 in the first Delphi round and increased to 0.97 in the second round.

Discussion

To the best of our knowledge, this is the first comprehensive SR template proposed for the evaluation of polytrauma patients based on whole-body CT imaging. This template is currently the only one developed following the latest version of the AAST injury scoring scales [47] and the AO Spine Thoracolumbar and Subaxial Injury Classification systems [48]. The idea of using standardized scores (as per AAST and AO classifications) allows not only to use a common and easily understandable lexicon in the multidisciplinary team, but also to grade the level of injury and facilitate patient management.

Although the RSNA has proposed several SR templates for trauma, only the “CT Facial Bones—Trauma template [45] was reported on the RSNA website at the time of writing (7 January 2023) and is not based on internationally established classifications.

Our final template was divided into four sections, amounting to a total of as many as 118 items. Although this SR model may seem very long and prone to slowing down the reporting, it has been broadly demonstrated that SR can facilitate radiologist workflow by reducing reporting time [53]. Jorg et al. [53] found that SR for whole-body trauma CT examinations can add clinical value compared to FTR by reducing reporting time and increasing the level of detail for trauma CT studies. However, no data on how this model was built were provided [53].

Our template was established based on a multi-round Delphi method following in-depth discussion between expert radiologists in emergency imaging, leading to higher expert scores and internal consistency in the second Delphi round than in the first one. The panel experts showed a greater disagreement on the “Patient Clinical Data” and “Clinical Evaluation” sections, because of the opinion that in the emergency setting, time is critical and there may be no possibility to have all patient data available at the time of reporting [5461]. However, after call conferences and once the option to fill in SR section fields partially had been clarified, all panelists expressed their agreement on this point.

The expert panel expressed a greater agreement on the “Imaging Protocol” and “Report” sections. Theoretically, the dissemination and sharing of the study protocol (including post-contrast phases) should ease the standardization and optimization of CT protocols in the emergency setting. Although each patient is different from another (especially in terms of hemodynamic stability and in relation to his/her anthropometric characteristics), the ability to act according to the rules of good conduct in the emergency setting could improve the quality of CT examinations and facilitate the interpretation of the results [6274].

Regarding the “Report” section, the Delphi method has made it possible not only to collect key information based on established classifications, but also to address potentially critical issues. For instance, the information for the heart injury assessment (which, according to the AAST classification, also requires knowledge of data based on the electrocardiogram) may be unavailable at the time of the CT scan, so we decided to simplify the reporting of heart injury.

The chance of employing SR can assist the radiologist in the imaging assessment, allowing to detect imaging findings that might go unnoticed with FTR due to distraction [7582]. On the other hand, a SR template allows a systematic search pattern that should avoid diagnostic errors. A retrospective analysis of 3,000 MRI studies using a checklist-template permitted to detect extraspinal findings in 28.5% of patients, which were not reported in the original FTR [75]. Similarly, the use of SR showed to increase the rate of identification of trauma-unrelated findings on cervical CT examinations [76]. Dendl et al. [77] showed that by using a SR template for major trauma CT examinations, radiology residents reached a higher diagnostic accuracy in the study setting than radiologists with 3 and 7 years of experience after board certification, highlighting the usefulness of SR as a guide for learning to correctly interpret and report imaging examinations.

The present SR has several strengths, as it is based on standardized terminology and structures, and according to Weiss et al. [78], it is a third-level SR. Furthermore, it has been validated by expert emergency radiologists by using a multi-round consensus-building Delphi exercise, and it has been built following the latest version of the AAST injury scoring scales and the AO Spine Thoracolumbar and Subaxial Injury Classification systems, with the goal to utilize a standardized language and improve communication within the trauma team.

This study has several limitations. Firstly, the same nationality of the panelists reduces the possibility of a wider view that could increase the consistency of the SR. Secondly, this SR template does not arise from a multidisciplinary evaluation, but only from a panel of radiologists. Thirdly, we did not test the clinical impact of this SR in a real-life emergency setting, and we did not assess the actual turnaround time of polytrauma CT examinations using this SR template, which is quite lengthy due to the complex nature of polytrauma CT reporting and could therefore be rather time-consuming. However, the highly modular and standardized structure of this SR from head to toe could be expected to keep reporting times within acceptable limits after a proper learning period, and in the extreme, it could lend itself as a conceptual framework for the development of more streamlined SR models based on the same rigorous semantics.

Conclusion

We developed a comprehensive SR model for whole-body CT examinations of polytrauma patients, based on a multi-round Delphi consensus of emergency radiology experts and following current AAST and AO Spine criteria. It could be expected that this SR will set a standard for improving the quality of radiological reports and the communication between all physicians involved in the trauma team.

Supplementary Information

Below is the link to the electronic supplementary material.

Abbreviations

AAST

American Association for the Surgery of Trauma

ATLS

Advanced Trauma Life Support

BMI

Body Mass Index

BSA

Body Surface Area

Cα

Cronbach's correlation coefficient alpha

ESER

European Society of Emergency Radiology

FTR

Free text report

GCS

Glasgow Coma Score

HIS

Hospital Information System

HTML5

HyperText Markup Language version 5

IHE

Integrating Healthcare Enterprise

MRRT

Management of Radiology Report Templates

RIS

Radiology Information System

RSNA

Radiological Society of North America

SIRM

Italian Society of Medical and Interventional Radiology

SR

Structured reporting

Funding

Open access funding provided by Università di Pisa within the CRUI-CARE Agreement. The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.

Declarations

Conflict of interest

The authors have no relevant financial or non-financial interests to disclose.

Research involving human participants and/or animals and informed consent.

No human participants or animals were involved in this study. Therefore, informed consent was unnecessary.

Footnotes

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  • 1.Turculeţ CŞ, Georgescu TF, Iordache F, Ene D, Gaşpar B, Beuran M. Polytrauma: the European paradigm. Chirurgia (Bucur) 2021;116:664–668. doi: 10.21614/chirurgia.116.6.664. [DOI] [PubMed] [Google Scholar]
  • 2.Ahmed N, Kuo YH. Prediction of trauma mortality incorporating pre-injury comorbidities into existing mortality scoring indices. Am Surg. 2022;88:2289–2301. doi: 10.1177/00031348221078980. [DOI] [PubMed] [Google Scholar]
  • 3.World Health Organization (2022) https://www.who.int/news-room/fact-sheets/detail/road-traffic-injuries. Accessed 7 Jan 2023
  • 4.von Rüden C, Bühren V, Perl M. Polytrauma management—Behandlung des Schwerverletzten in Schockraum und OP [Polytrauma management—treatment of severely injured patients in ER and OR] Z Orthop Unfall. 2017;155:603–622. doi: 10.1055/s-0042-124275. [DOI] [PubMed] [Google Scholar]
  • 5.Flammia F, Chiti G, Trinci M, Danti G, Cozzi D, Grassi R, Palumbo P, Bruno F, Agostini A, Fusco R, Granata V, Giovagnoni A, Miele V. Optimization of CT protocol in polytrauma patients: an update. Eur Rev Med Pharmacol Sci. 2022;26:2543–2555. doi: 10.26355/eurrev_202204_28491. [DOI] [PubMed] [Google Scholar]
  • 6.Miele V, Di Giampietro I (2014) Diagnostic imaging in emergency. Salute e società, (2EN), pp 127–138. 10.3280/SES2014-002010EN
  • 7.Di Giacomo V, Trinci M, van der Byl G, Catania VD, Calisti A, Miele V. Ultrasound in newborns and children suffering from non-traumatic acute abdominal pain: imaging with clinical and surgical correlation. J Ultrasound. 2014;18:385–393. doi: 10.1007/s40477-014-0087-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 8.Committee on Trauma . 10th edition of the advanced trauma life Support® (ATLS®) student course manual. Chicago: American College of Surgeons; 2018. [Google Scholar]
  • 9.Wirth S, Hebebrand J, Basilico R, Berger FH, Blanco A, Calli C, Dumba M, Linsenmaier U, Mück F, Nieboer KH, Scaglione M, Weber MA, Dick E (2020) European Society of Emergency Radiology—Guideline on Radiological Polytrauma Imaging and Service (full version). https://www.eser-society.org/app/uploads/ESER-Guideline-Long-Version-15.11.2020.pdf. Accessed 7 Jan 2023 [DOI] [PMC free article] [PubMed]
  • 10.Granata V, Fusco R, Bicchierai G, Cozzi D, Grazzini G, Danti G, De Muzio F, Maggialetti N, Smorchkova O, D'Elia M, Brunese MC, Grassi R, Giacobbe G, Bruno F, Palumbo P, Lacasella GV, Brunese L, Grassi R, Miele V, Barile A. Diagnostic protocols in oncology: workup and treatment planning Part 1: the optimization of CT protocol. Eur Rev Med Pharmacol Sci. 2021;25:6972–6994. doi: 10.26355/eurrev_202111_27246. [DOI] [PubMed] [Google Scholar]
  • 11.Regine G, Stasolla A, Miele V. Multidetector computed tomography of the renal arteries in vascular emergencies. Eur J Radiol. 2007;64:83–91. doi: 10.1016/j.ejrad.2007.06.007. [DOI] [PubMed] [Google Scholar]
  • 12.Trinci M, Piccolo CL, Ferrari R, Galluzzo M, Ianniello S, Miele V. Contrast-enhanced ultrasound (CEUS) in pediatric blunt abdominal trauma. J Ultrasound. 2019;22:27–40. doi: 10.1007/s40477-018-0346-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 13.Scapicchio C, Gabelloni M, Barucci A, Cioni D, Saba L, Neri E. A deep look into radiomics. Radiol Med. 2021;126:1296–1311. doi: 10.1007/s11547-021-01389-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 14.Benedetti G, Mori M, Panzeri MM, Barbera M, Palumbo D, Sini C, Muffatti F, Andreasi V, Steidler S, Doglioni C, Partelli S, Manzoni M, Falconi M, Fiorino C, De Cobelli F. CT-derived radiomic features to discriminate histologic characteristics of pancreatic neuroendocrine tumors. Radiol Med. 2021;126:745–760. doi: 10.1007/s11547-021-01333-z. [DOI] [PubMed] [Google Scholar]
  • 15.Laurelli G, Falcone F, Gallo MS, Scala F, Losito S, Granata V, Cascella M, Greggi S. Long-term oncologic and reproductive outcomes in young women with early endometrial cancer conservatively treated: a prospective study and literature update. Int J Gynecol Cancer. 2016;26:1650–1657. doi: 10.1097/IGC.0000000000000825. [DOI] [PubMed] [Google Scholar]
  • 16.Tagliafico AS, Campi C, Bianca B, Bortolotto C, Buccicardi D, Francesca C, Prost R, Rengo M, Faggioni L. Blockchain in radiology research and clinical practice: current trends and future directions. Radiol Med. 2022;127:391–397. doi: 10.1007/s11547-022-01460-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 17.Granata V, Simonetti I, Fusco R, Setola SV, Izzo F, Scarpato L, Vanella V, Festino L, Simeone E, Ascierto PA, Petrillo A. Management of cutaneous melanoma: radiologists challenging and risk assessment. Radiol Med. 2022;127:899–911. doi: 10.1007/s11547-022-01522-4. [DOI] [PubMed] [Google Scholar]
  • 18.Cappabianca S, Granata V, Di Grezia G, Mandato Y, Reginelli A, Di Mizio V, Grassi R, Rotondo A. The role of nasoenteric intubation in the MR study of patients with Crohn's disease: our experience and literature review. Radiol Med. 2011;116:389–406. doi: 10.1007/s11547-010-0605-1. [DOI] [PubMed] [Google Scholar]
  • 19.Sigl B, Herold C. Strukturierte Befundung in der Radiologie—Chance für die radiologische Jugend? [Structured reporting in radiology—a chance for young radiologists?] Radiologe. 2021;61:487–489. doi: 10.1007/s00117-021-00826-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 20.Davidson EM, Poon MTC, Casey A, Grivas A, Duma D, Dong H, Suárez-Paniagua V, Grover C, Tobin R, Whalley H, Wu H, Alex B, Whiteley W. The reporting quality of natural language processing studies: systematic review of studies of radiology reports. BMC Med Imaging. 2021;21:142. doi: 10.1186/s12880-021-00671-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 21.Olthof AW, Leusveld ALM, de Groot JC, Callenbach PMC, van Ooijen PMA. Contextual structured reporting in radiology: implementation and long-term evaluation in improving the communication of critical findings. J Med Syst. 2020;44:148. doi: 10.1007/s10916-020-01609-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 22.Nobel JM, van Geel K, Robben SGF. Structured reporting in radiology: a systematic review to explore its potential. Eur Radiol. 2022;32:2837–2854. doi: 10.1007/s00330-021-08327-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 23.Vosshenrich J, Nesic I, Cyriac J, Boll DT, Merkle EM, Heye T. Revealing the most common reporting errors through data mining of the report proofreading process. Eur Radiol. 2021;31:2115–2125. doi: 10.1007/s00330-020-07306-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 24.European Society of Radiology (ESR) ESR paper on structured reporting in radiology. Insights Imaging. 2018;9:1–7. doi: 10.1007/s13244-017-0588-8. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 25.US Government (2009) American Recovery and Reinvestment Act of 2009 Title XIII: Health Information Technology: Health Information Technology for Economic and Clinical Health Act (HITECH Act), pp 112–164. https://www.hhs.gov/sites/default/files/ocr/privacy/hipaa/understanding/coveredentities/hitechact.pdf. Accessed 7 Jan 2023
  • 26.Sobez LM, Kim SH, Angstwurm M, Störmann S, Pförringer D, Schmidutz F, Prezzi D, Kelly-Morland C, Sommer WH, Sabel B, Nörenberg D, Berndt M, Galiè F. Creating high-quality radiology reports in foreign languages through multilingual structured reporting. Eur Radiol. 2019;29:6038–6048. doi: 10.1007/s00330-019-06206-8. [DOI] [PubMed] [Google Scholar]
  • 27.Segrelles JD, Medina R, Blanquer I, Martí-Bonmatí L. Increasing the efficiency on producing radiology reports for breast cancer diagnosis by means of structured reports: A comparative study. Methods Inf Med. 2017;56:248–260. doi: 10.3414/ME16-01-0091. [DOI] [PubMed] [Google Scholar]
  • 28.Granata V, Fusco R, De Muzio F, Cutolo C, Setola SV, Dell'Aversana F, Grassi F, Belli A, Silvestro L, Ottaiano A, Nasti G, Avallone A, Flammia F, Miele V, Tatangelo F, Izzo F, Petrillo A. Radiomics and machine learning analysis based on magnetic resonance imaging in the assessment of liver mucinous colorectal metastases. Radiol Med. 2022;127:763–772. doi: 10.1007/s11547-022-01501-9. [DOI] [PubMed] [Google Scholar]
  • 29.Sansone M, Marrone S, Di Salvio G, Belfiore MP, Gatta G, Fusco R, Vanore L, Zuiani C, Grassi F, Vietri MT, Granata V, Grassi R. Comparison between two packages for pectoral muscle removal on mammographic images. Radiol Med. 2022;127:848–856. doi: 10.1007/s11547-022-01521-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 30.Cozzi D, Bicci E, Cavigli E, Danti G, Bettarini S, Tortoli P, Mazzoni LN, Busoni S, Pradella S, Miele V. Radiomics in pulmonary neuroendocrine tumours (NETs) Radiol Med. 2022;127:609–615. doi: 10.1007/s11547-022-01494-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 31.Gurgitano M, Angileri SA, Rodà GM, Liguori A, Pandolfi M, Ierardi AM, Wood BJ, Carrafiello G. Interventional radiology ex-machina: impact of artificial intelligence on practice. Radiol Med. 2021;126:998–1006. doi: 10.1007/s11547-021-01351-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 32.Deandrea S, Cavazzana L, Principi N, Luconi E, Campoleoni M, Bastiampillai AJ, Bracchi L, Bucchi L, Pedilarco S, Piscitelli A, Sfondrini MS, Silvestri AR, Castaldi S. Screening of women with aesthetic prostheses in dedicated sessions of a population-based breast cancer screening programme. Radiol Med. 2021;126:946–955. doi: 10.1007/s11547-021-01357-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33.Granata V, Grassi R, Fusco R, Setola SV, Belli A, Ottaiano A, Nasti G, La Porta M, Danti G, Cappabianca S, Cutolo C, Petrillo A, Izzo F. Intrahepatic cholangiocarcinoma and its differential diagnosis at MRI: how radiologist should assess MR features. Radiol Med. 2021;126:1584–1600. doi: 10.1007/s11547-021-01428-7. [DOI] [PubMed] [Google Scholar]
  • 34.Granata V, Caruso D, Grassi R, Cappabianca S, Reginelli A, Rizzati R, Masselli G, Golfieri R, Rengo M, Regge D, Lo Re G, Pradella S, Fusco R, Faggioni L, Laghi A, Miele V, Neri E, Coppola F. Structured reporting of rectal cancer staging and restaging: a consensus proposal. Cancers (Basel) 2021;13:2135. doi: 10.3390/cancers13092135. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 35.Granata V, Morana G, D'Onofrio M, Fusco R, Coppola F, Grassi F, Cappabianca S, Reginelli A, Maggialetti N, Buccicardi D, Barile A, Rengo M, Bortolotto C, Urraro F, La Casella GV, Montella M, Ciaghi E, Bellifemine F, De Muzio F, Danti G, Grazzini G, Barresi C, Brunese L, Neri E, Grassi R, Miele V, Faggioni L. Structured reporting of computed tomography and magnetic resonance in the staging of pancreatic adenocarcinoma: a Delphi consensus proposal. Diagnostics (Basel) 2021;11:2033. doi: 10.3390/diagnostics11112033. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 36.Neri E, Granata V, Montemezzi S, Belli P, Bernardi D, Brancato B, Caumo F, Calabrese M, Coppola F, Cossu E, Faggioni L, Frigerio A, Fusco R, Petrillo A, Girardi V, Iacconi C, Marini C, Marino MA, Martincich L, Nori J, Pediconi F, Saguatti G, Sansone M, Sardanelli F, Scaperrotta GP, Zuiani C, Ciaghi E, Montella M, Miele V, Grassi R. Structured reporting of X-ray mammography in the first diagnosis of breast cancer: a Delphi consensus proposal. Radiol Med. 2022;127:471–483. doi: 10.1007/s11547-022-01478-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 37.Granata V, Coppola F, Grassi R, Fusco R, Tafuto S, Izzo F, Reginelli A, Maggialetti N, Buccicardi D, Frittoli B, Rengo M, Bortolotto C, Prost R, Lacasella GV, Montella M, Ciaghi E, Bellifemine F, De Muzio F, Danti G, Grazzini G, De Filippo M, Cappabianca S, Barresi C, Iafrate F, Stoppino LP, Laghi A, Grassi R, Brunese L, Neri E, Miele V, Faggioni L. Structured reporting of computed tomography in the staging of neuroendocrine neoplasms: a Delphi consensus proposal. Front Endocrinol (Lausanne) 2021;12:748944. doi: 10.3389/fendo.2021.748944. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 38.Granata V, Faggioni L, Grassi R, Fusco R, Reginelli A, Rega D, Maggialetti N, Buccicardi D, Frittoli B, Rengo M, Bortolotto C, Prost R, Lacasella GV, Montella M, Ciaghi E, Bellifemine F, De Muzio F, Grazzini G, De Filippo M, Cappabianca S, Laghi A, Grassi R, Brunese L, Neri E, Miele V, Coppola F. Structured reporting of computed tomography in the staging of colon cancer: a Delphi consensus proposal. Radiol Med. 2022;127:21–29. doi: 10.1007/s11547-021-01418-9. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 39.Granata V, Grassi R, Miele V, Larici AR, Sverzellati N, Cappabianca S, Brunese L, Maggialetti N, Borghesi A, Fusco R, Balbi M, Urraro F, Buccicardi D, Bortolotto C, Prost R, Rengo M, Baratella E, De Filippo M, Barresi C, Palmucci S, Busso M, Calandriello L, Sansone M, Neri E, Coppola F, Faggioni L. Structured reporting of lung cancer staging: a consensus proposal. Diagnostics (Basel) 2021;11:1569. doi: 10.3390/diagnostics11091569. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 40.Granata V, Pradella S, Cozzi D, Fusco R, Faggioni L, Coppola F, Grassi R, Maggialetti N, Buccicardi D, Lacasella GV, Montella M, Ciaghi E, Bellifemine F, De Filippo M, Rengo M, Bortolotto C, Prost R, Barresi C, Cappabianca S, Brunese L, Neri E, Grassi R, Miele V. Computed tomography structured reporting in the staging of lymphoma: a Delphi consensus proposal. J Clin Med. 2021;10:4007. doi: 10.3390/jcm10174007. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 41.Neri E, Coppola F, Larici AR, Sverzellati N, Mazzei MA, Sacco P, Dalpiaz G, Feragalli B, Miele V, Grassi R. Structured reporting of chest CT in COVID-19 pneumonia: a consensus proposal. Insights Imaging. 2020;11:92. doi: 10.1186/s13244-020-00901-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 42.Granata V, Fusco R, Avallone A, Filice F, Tatangelo F, Piccirillo M, Grassi R, Izzo F, Petrillo A. Critical analysis of the major and ancillary imaging features of LI-RADS on 127 proven HCCs evaluated with functional and morphological MRI: lights and shadows. Oncotarget. 2017;8:51224–51237. doi: 10.18632/oncotarget.17227. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 43.Granata V, Fusco R, Avallone A, Catalano O, Filice F, Leongito M, Palaia R, Izzo F, Petrillo A. Major and ancillary magnetic resonance features of LI-RADS to assess HCC: an overview and update. Infect Agent Cancer. 2017;12:23. doi: 10.1186/s13027-017-0132-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 44.Larson DB, Towbin AJ, Pryor RM, Donnelly LF. Improving consistency in radiology reporting through the use of department-wide standardized structured reporting. Radiology. 2013;267:240–250. doi: 10.1148/radiol.12121502. [DOI] [PubMed] [Google Scholar]
  • 45.Radiological Society of North America. RadReport reporting templates. https://www.rsna.org/practice-tools/data-tools-and-standards/radreport-reporting-templates. Accessed 7 Jan 2023
  • 46.Società Italiana di Radiologia Medica e Interventistica. https://sirm.org. Accessed 7 Jan 2023
  • 47.The American Association for the Surgery of Trauma. Injury scoring scale. A resource for trauma care professionals. https://www.aast.org/resources-detail/injury-scoring-scale. Accessed 7 January 2023
  • 48.AO Foundation. AO spine injury classification systems. https://www.aofoundation.org/spine/clinical-library-and-tools/aospine-classification-systems. Accessed 7 Jan 2023
  • 49.Kahn CE, Jr, Genereaux B, Langlotz CP. Conversion of radiology reporting templates to the MRRT standard. J Digit Imaging. 2015;28:528–536. doi: 10.1007/s10278-015-9787-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 50.Becker G. Creating comparability among reliability coefficients: the case of Cronbach Alpha and Cohen Kappa. Psychol Rep. 2000;87:1171. doi: 10.2466/pr0.2000.87.3f.1171. [DOI] [PubMed] [Google Scholar]
  • 51.Cronbach LJ. Coefficient alpha and the internal structure of tests. Psychometrika. 1951;16:297–334. doi: 10.1007/BF02310555. [DOI] [Google Scholar]
  • 52.Magerl F, Aebi M, Gertzbein SD, Harms J, Nazarian S. A comprehensive classification of thoracic and lumbar injuries. Eur Spine J. 1994;3:184–201. doi: 10.1007/BF02221591. [DOI] [PubMed] [Google Scholar]
  • 53.Jorg T, Heckmann JC, Mildenberger P, Hahn F, Düber C, Mildenberger P, Kloeckner R, Jungmann F. Structured reporting of CT scans of patients with trauma leads to faster, more detailed diagnoses: An experimental study. Eur J Radiol. 2021;144:109954. doi: 10.1016/j.ejrad.2021.109954. [DOI] [PubMed] [Google Scholar]
  • 54.Miele V, Andreoli C, Grassi R. The management of emergency radiology: key facts. Eur J Radiol. 2006;59:311–314. doi: 10.1016/j.ejrad.2006.04.020. [DOI] [PubMed] [Google Scholar]
  • 55.Mazzei MA, Mazzei FG, Marrelli D, Imbriaco G, Guerrini S, Vindigni C, Civitelli S, Roviello F, Grassi R, Volterrani L. Computed tomographic evaluation of mesentery: diagnostic value in acute mesenteric ischemia. J Comput Assist Tomogr. 2012;36:1–7. doi: 10.1097/RCT.0b013e31823b4465. [DOI] [PubMed] [Google Scholar]
  • 56.Buffa V, Solazzo A, D'Auria V, Del Prete A, Vallone A, Luzietti M, Madau M, Grassi R, Miele V. Dual-source dual-energy CT: dose reduction after endovascular abdominal aortic aneurysm repair. Radiol Med. 2014;119:934–941. doi: 10.1007/s11547-014-0420-1. [DOI] [PubMed] [Google Scholar]
  • 57.Coppola F, Faggioni L, Regge D, Giovagnoni A, Golfieri R, Bibbolino C, Miele V, Neri E, Grassi R. Artificial intelligence: radiologists' expectations and opinions gleaned from a nationwide online survey. Radiol Med. 2021;126:63–71. doi: 10.1007/s11547-020-01205-y. [DOI] [PubMed] [Google Scholar]
  • 58.Agostini A, Borgheresi A, Mari A, Floridi C, Bruno F, Carotti M, Schicchi N, Barile A, Maggi S, Giovagnoni A. Dual-energy CT: theoretical principles and clinical applications. Radiol Med. 2019;124:1281–1295. doi: 10.1007/s11547-019-01107-8. [DOI] [PubMed] [Google Scholar]
  • 59.Masciocchi C, Sparvoli L, Barile A. Diagnostic imaging of malignant cartilage tumors. Eur J Radiol. 1998;27(Suppl 1):S86–90. doi: 10.1016/s0720-048x(98)00048-5. [DOI] [PubMed] [Google Scholar]
  • 60.Barile A, Lanni G, Conti L, Mariani S, Calvisi V, Castagna A, Rossi F, Masciocchi C. Lesions of the biceps pulley as cause of anterosuperior impingement of the shoulder in the athlete: potentials and limits of MR arthrography compared with arthroscopy. Radiol Med. 2013;118:112–122. doi: 10.1007/s11547-012-0838-2. [DOI] [PubMed] [Google Scholar]
  • 61.Masciocchi C, Lanni G, Conti L, Conchiglia A, Fascetti E, Flamini S, Coletti G, Barile A. Soft-tissue inflammatory myofibroblastic tumors (IMTs) of the limbs: potential and limits of diagnostic imaging. Skeletal Radiol. 2012;41:643–649. doi: 10.1007/s00256-011-1263-7. [DOI] [PubMed] [Google Scholar]
  • 62.Fusco R, Setola SV, Raiano N, Granata V, Cerciello V, Pecori B, Petrillo A. Analysis of a monocentric computed tomography dosimetric database using a radiation dose index monitoring software: dose levels and alerts before and after the implementation of the adaptive statistical iterative reconstruction on CT images. Radiol Med. 2022;127:733–742. doi: 10.1007/s11547-022-01481-w. [DOI] [PubMed] [Google Scholar]
  • 63.Chiti G, Grazzini G, Flammia F, Matteuzzi B, Tortoli P, Bettarini S, Pasqualini E, Granata V, Busoni S, Messserini L, Pradella S, Massi D, Miele V. Gastroenteropancreatic neuroendocrine neoplasms (GEP-NENs): a radiomic model to predict tumor grade. Radiol Med. 2022;127:928–938. doi: 10.1007/s11547-022-01529-x. [DOI] [PubMed] [Google Scholar]
  • 64.Barile A, Limbucci N, Splendiani A, Gallucci M, Masciocchi C. Spinal injury in sport. Eur J Radiol. 2007;62:68–78. doi: 10.1016/j.ejrad.2007.01.017. [DOI] [PubMed] [Google Scholar]
  • 65.Di Cesare E, Gennarelli A, Di Sibio A, Felli V, Splendiani A, Gravina GL, Barile A, Masciocchi C. Assessment of dose exposure and image quality in coronary angiography performed by 640-slice CT: a comparison between adaptive iterative and filtered back-projection algorithm by propensity analysis. Radiol Med. 2014;119:642–649. doi: 10.1007/s11547-014-0382-3. [DOI] [PubMed] [Google Scholar]
  • 66.Fusco R, Sansone M, Granata V, Setola SV, Petrillo A. A systematic review on multiparametric MR imaging in prostate cancer detection. Infect Agent Cancer. 2017;30(12):57. doi: 10.1186/s13027-017-0168-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 67.Petrillo A, Fusco R, Petrillo M, Granata V, Delrio P, Bianco F, Pecori B, Botti G, Tatangelo F, Caracò C, Aloj L, Avallone A, Lastoria S. Standardized Index of Shape (DCE-MRI) and Standardized Uptake Value (PET/CT): two quantitative approaches to discriminate chemo-radiotherapy locally advanced rectal cancer responders under a functional profile. Oncotarget. 2017;8:8143–8153. doi: 10.18632/oncotarget.14106. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 68.Grassi R, Cappabianca S, Urraro F, Feragalli B, Montanelli A, Patelli G, Granata V, Giacobbe G, Russo GM, Grillo A, De Lisio A, Paura C, Clemente A, Gagliardi G, Magliocchetti S, Cozzi D, Fusco R, Belfiore MP, Grassi R, Miele V. Chest CT computerized aided quantification of PNEUMONIA lesions in COVID-19 infection: a comparison among three commercial software. Int J Environ Res Public Health. 2020;17:6914. doi: 10.3390/ijerph17186914. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 69.Fusco R, Granata V, Mazzei MA, Meglio ND, Roscio DD, Moroni C, Monti R, Cappabianca C, Picone C, Neri E, Coppola F, Montanino A, Grassi R, Petrillo A, Miele V. Quantitative imaging decision support (QIDSTM) tool consistency evaluation and radiomic analysis by means of 594 metrics in lung carcinoma on chest CT scan. Cancer Control. 2021;28:1073274820985786. doi: 10.1177/1073274820985786. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 70.Bracco S, Zanoni M, Casseri T, Castellano D, Cioni S, Vallone IM, Gennari P, Mazzei MA, Romano DG, Piano M, Comelli C, Tassi R, Ciceri EFM. Endovascular treatment of acute ischemic stroke due to tandem lesions of the anterior cerebral circulation: a multicentric Italian observational study. Radiol Med. 2021;126:804–817. doi: 10.1007/s11547-020-01331-7. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 71.Moroni C, Cozzi D, Albanesi M, Cavigli E, Bindi A, Luvarà S, Busoni S, Mazzoni LN, Grifoni S, Nazerian P, Miele V. Chest X-ray in the emergency department during COVID-19 pandemic descending phase in Italy: correlation with patients' outcome. Radiol Med. 2021;126:661–668. doi: 10.1007/s11547-020-01327-3. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 72.Giurazza F, Contegiacomo A, Calandri M, Mosconi C, Modestino F, Corvino F, Scrofani AR, Marra P, Coniglio G, Failla G, Lucarelli N, Femia M, Semeraro V, Ierardi AM. IVC filter retrieval: a multicenter proposal of two score systems to predict application of complex technique and procedural outcome. Radiol Med. 2021;126:1007–1016. doi: 10.1007/s11547-021-01356-6. [DOI] [PubMed] [Google Scholar]
  • 73.Cobianchi Bellisari F, De Marino L, Arrigoni F, Mariani S, Bruno F, Palumbo P, De Cataldo C, Sgalambro F, Catallo N, Zugaro L, Di Cesare E, Splendiani A, Masciocchi C, Giovagnoni A, Barile A. T2-mapping MRI evaluation of patellofemoral cartilage in patients submitted to intra-articular platelet-rich plasma (PRP) injections. Radiol Med. 2021;126:1085–1094. doi: 10.1007/s11547-021-01372-6. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 74.Rengo M, Boru CE, Badia S, Iossa A, Bellini D, Picchia S, Panvini N, Carbone I, Silecchia G, Laghi A. Preoperative measurement of the hiatal surface with MDCT: impact on surgical planning. Radiol Med. 2021;126:1508–1517. doi: 10.1007/s11547-021-01413-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 75.Quattrocchi CC, Giona A, Di Martino AC, Errante Y, Scarciolla L, Mallio CA, Denaro V. Zobel BB (2013) Extra-spinal incidental findings at lumbar spine MRI in the general population: a large cohort study. Insights Imaging. 2013;4:301–308. doi: 10.1007/s13244-013-0234-z. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 76.Lin E, Powell DK, Kagetsu NJ. Efficacy of a checklist-style structured radiology reporting template in reducing resident misses on cervical spine computed tomography examinations. J Digit Imaging. 2014;27:588–593. doi: 10.1007/s10278-014-9703-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 77.Dendl LM, Pausch AM, Hoffstetter P, Dornia C, Höllthaler J, Ernstberger A, Becker R, Kopf S, Schleder S, Schreyer AG. Structured reporting of whole-body trauma CT scans using checklists: diagnostic accuracy of reporting radiologists depending on their level of experience. Rofo. 2021;193:1451–1460. doi: 10.1055/a-1541-8265. [DOI] [PubMed] [Google Scholar]
  • 78.Weiss DL, Bolos PR. Reporting and dictation. In: Branstetter BF, editor. Practical imaging informatics: foundations and applications for PACS professionals. Heidelberg: Springer; 2009. [Google Scholar]
  • 79.Regine G, Atzori M, Miele V, Buffa V, Galluzzo M, Luzietti M, Adami L. Second-generation sonographic contrast agents in the evaluation of renal trauma. Radiol Med. 2007;112:581–587. doi: 10.1007/s11547-007-0164-2. [DOI] [PubMed] [Google Scholar]
  • 80.Santone A, Brunese MC, Donnarumma F, Guerriero P, Mercaldo F, Reginelli A, Miele V, Giovagnoni A, Brunese L. Radiomic features for prostate cancer grade detection through formal verification. Radiol Med. 2021;126:688–697. doi: 10.1007/s11547-020-01314-8. [DOI] [PubMed] [Google Scholar]
  • 81.Piccolo CL, Galluzzo M, Ianniello S, Trinci M, Russo A, Rossi E, Zeccolini M, Laporta A, Guglielmi G, Miele V. Pediatric musculoskeletal injuries: role of ultrasound and magnetic resonance imaging. Musculoskelet Surg. 2017;101(Suppl 1):85–102. doi: 10.1007/s12306-017-0452-5. [DOI] [PubMed] [Google Scholar]
  • 82.Granata V, Fusco R, Setola SV, Galdiero R, Picone C, Izzo F, D'Aniello R, Miele V, Grassi R, Grassi R, Petrillo A. Lymphadenopathy after BNT162b2 Covid-19 vaccine: preliminary ultrasound findings. Biology (Basel) 2021;10(3):214. doi: 10.3390/biology10030214. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials


Articles from La Radiologia Medica are provided here courtesy of Springer

RESOURCES