Skip to main content
BJR Open logoLink to BJR Open
. 2021 Sep 19;3(1):20210050. doi: 10.1259/bjro.20210050

Predicting factors for primary cervical cancer recurrence after definitive radiation therapy

Mitsuru Okubo 1,, Tomohiro Itonaga 1, Tatsuhiko Saito 1, Sachika Shiraishi 1, Daisuke Yunaiyama 1, Ryuji Mikami 1, Akira Sakurada 1, Shinji Sugahara 1, Koichi Tokuuye 1, Kazuhiro Saito 1
PMCID: PMC8611686  PMID: 34877461

Abstract

Objectives:

The study aimed to retrospectively investigate the apparent diffusion coefficient (ADC) of primary cervical cancer to examine the recurrence correlations in patients treated with radiotherapy (RT).

Methods:

The ADC of 31 patients with cervical cancer treated with RT were analyzed as possible risk factors for recurrence. A receiver operating characteristic (ROC) curve of the mean ADC (ADCmean) for the recurrence was generated to determine the cut-off value that yielded optimal sensitivity and specificity. The patient population was subdivided according to the risk factors for recurrence, and the disease-free survival (DFS) was analyzed. The following were investigated to explore the risk factors for recurrence: age, performance status, stage, pelvic lymph node metastasis, histologic tumor grade, maximal diameter of the primary tumor, chemotherapy, and ADCmean.

Results:

The median follow-up duration of the patients was 25 months. The recurrence was recognized in 9 (29%) of the 31 cases. The ROC analysis of recurrence showed that the area under the ADCmean curve was 0.889 (95% CI, 0.771–1.000; p = 0.001). The cut-off value of ADC mean was 0.900 × 10− 3 mm2/s, with a sensitivity of 86.4% and a specificity of 88.9%. By univariate analysis, the ADCmean was the only factor significantly associated with recurrence.

Conclusion:

The ADCmean of the primary tumor is a potential predictive factor for the recurrence in of cervical cancer.

Advances in knowledge:

The ADCmean of the primary tumor is a predictor of recurrence in patients with pre-treatment cervical cancer evaluation.

Introduction

Cervical cancer is the second most common gynecological malignancy. 1 The International Federation of Gynecology and Obstetrics (FIGO) reported that a 5 year recurrence rate of 28% and an overall mortality rate of 27.8% for females with cervical cancer. 2 Depending on the FIGO stage and histological subtype, the primary treatment consists of surgery, radiotherapy (RT), chemotherapy, or concurrent chemoradiation therapy. RT consisting of external-beam RT (EBRT), cisplatin-based chemotherapy, and intracavitary brachytherapy is the recommended standard treatment for locally advanced cervical cancer. 3 However, a substantial number of patients experience locoregional recurrence or distant metastasis despite treatment. 4,5 Poor prognostic factors for cervical cancer include pelvic lymph node metastasis, parametrial involvement, positive surgical margins, large tumor diameter, deep stromal invasion and the presence of tumor in capillary lymphatic spaces. 6 However, these parameters are not sufficient to accurately predict prognosis. It is now accepted that new approaches to cervical cancer are pivotal to improving this disease’s prognosis.

MRI has an essential role in diagnosing cervical cancer, particularly for local staging. Diffusion-weighted imaging (DWI) is a functional imaging technique that analyzes differences in extracellular water proton movement, allowing for discrimination between tissues with varying cellularity. 7 Additionally, this technique allows quantification of diffusion by calculating the apparent diffusion coefficient (ADC) values. In malignant tumors, the increased cellular density restricts water diffusion in the interstitial space, thus, lowering the ADC. Some studies showed that low ADC values are related with recurrence and a poor survival rate, 8,9 while some found low ADC values in patients with good treatment responses. 10,11 Other studies concluded that there is insufficient evidence to use pre-treatment ADC to predict the treatment efficacy. 12–14 Therefore, it has been suggested that the ADC may provide useful information on tumor cellularity, tumor aggressiveness, and subtype characterization. 15–18

In this study, we investigated the ADC of primary squamous cell cervical cancer to examine its correlation to recurrence in patients treated with RT.

Methods and materials

Study design and patients

From May 2012 to December 2019, 41 consecutive patients with pathologically diagnosed squamous cell uterine cervical cancer were treated with definitive RT at Tokyo Medical University Hachioji Medical Center. All patients provided written informed consent, and the Ethical Review Board approved this study of the authors' institution. Of the 41 patients, 31 patients who underwent MRI taken by the same machine within 30 days prior to the start of treatment were selected in this retrospective analysis. No patients enrolled in this study received any neoadjuvant chemotherapy before RT.

Treatment

Three-dimensional conformal RT was planned and performed with the patient in the supine position. For treatment planning, all patients underwent pelvic CT at a 2.5 mm slice thickness. Typically, the patients underwent EBRT with a photon beam of 10 MV. RT consisted of a combination of whole pelvic (WP) EBRT and high-dose-rate intracavitary brachytherapy (HDR-ICBT). WP-EBRT was delivered for 5 days during a week to achieve a total dose of 50.4 Gy/28 fractions. The WP-EBRT was initially delivered without a midline block (MB) using a box technique. Subsequently, the next phase of WP-EBRT was administered through the same WP field with a MB width of 3 or 4 cm using anteroposterior opposite ports. The first HDR-ICBT was performed after the MB insertion. HDR-ICBT was performed once a week with a fraction dose of 6 Gy prescribed at point A using Ir-192 afterloading machines. HDR-ICBT was not allowed on the same day as the EBRT. The relationship between RT schedule and patient’s stage was shown in Table 1. The cumulative linear quadratic equivalent doses (EQD2) 19 at point A, which were the summation of the EBRT doses without the MB and HDR-ICBT doses. For patients who had an inadequate response to EBRT or failed tandem insertion, additional WP-EBRT without the MB was allowed to a total dose of 50.4 Gy. The total HDR-ICBT dose was 12 Gy per 2 fractions at point A.

Table 1.

The relationship between RT schedule and patients’ stage

EBRT HDR-ICBT Total EQD2 at point A Patients’ stage
WP WP (MB)
30.6 Gy/17 Fr 19.8 Gy/11 Fr 24 Gy/4 Fr 62 Gy Stage Ib/IIb
39.6 Gy/22 Fr 10.8 Gy/6 Fr 18 Gy/3 Fr 63 Gy Stage IIIa/IVA
50.4 Gy/28 Fr 0 y 12 Gy/2 Fr 66 Gy

EBRT, external-beam radiotherapy; EQD2, equivalent dose in 2 Gy per fraction; HDR-ICBT, high-dose-rate intracavitary brachytherapy; MB, midline block; WP, whole pelvic radiotherapy.

Weekly cisplatin at a dose of 40 mg/m2 was administered for five courses during the RT period. Of the 31 patients, 26 (84%) received concurrent cisplatin chemotherapy, however the remaining 5 (16%) patients did not receive concurrent chemotherapy due to the low stage or the presence of comorbidities.

MRI technique and image analysis

MRI was performed using a 1.5 T MR system (Magnetom Avanto; Siemens, Erlangen, Germany) with a 6-channel phased-array coil. Routine pelvic MRIs were acquired as follows: sagittal T 1 weighted fast spin-echo (FSE) images [repetition time (TR)/echo time (TE), 550/11 ms; flip angle, 180°; section thickness/intersection gap, 4/0.4 mm; a field of view (FOV), 250 × 250; matrix size, 230 × 384; the number of excitation, 4], and axial, sagittal, and coronal T 2 weighted FSE images [TR/TE, 4000/84 ms; flip angle, 150°; section thickness/intersection gap, 4/0.4 mm; FOV, 250 × 250; matrix size, 230 × 384; the number of excitation, 4]. Axial DW images were then obtained. Imaging parameters for DW imaging were as follows: TR/TE, 4000/75; flip angle, 90°; section thickness/intersection gap, 4/0.4 mm; FOV, 280 × 280 matrix size, 128 × 128; bandwidth, 2170 Hz/pixel; the number of excitation, 4, using a chemical shift-selective fat suppression technique. The corresponding b-values to the diffusion sensitizing gradient were 0 and 1000 s/mm2. The ADC values were calculated from the regions of interest (ROIs) by dividing the signal intensity by 1000 to obtain ADC values × 10−3 mm2/s. The ROI placements and ADC calculations were made in the tangible portions of the primary tumor’s maximum sectional diameter, avoiding cystic or necrotic portions. Polygonal ROIs were placed manually on the maximum axial section of the primary tumor on the ADC map. The mean ADC value (ADCmean) of all full pixels within the ROI was obtained. One radiation oncologist with 17 years of experience drew all ROIs referencing the T 2 weighted images. A typical ROI placement for a tumor is shown in Figure 1.

Figure 1.

Figure 1.

A typical ROI placement for tumors. (a) T 2 weighted sagittal image. The white line is the reference line for the axial image on (b–d.) Axial image for T 2 weighted and DW image, ADC map. ROIs were drawn manually along the edge of the lesions to cover as much tumor area as possible on a slice of the largest tumor area without excluding cystic or necrotic areas. ADC, apparent diffusion coefficient; DWI, diffusion-weighted imaging; ROI, region of interest

Evaluation of the local response and toxicity

Local response was estimated by physical examination at 1 month after completing RT. The regular follow-up visits were performed at 2–3 month intervals for the first 2 years, then every 4–6 months after that, in the absence of clinical symptoms. At each follow-up visit, evaluation consisted of medical history, physical examination, Papanicolaou smear, ultrasonography, CT scans, and tumor marker assessment. The toxicity data were collected retrospectively from patient files. In evaluating the acute or late effect, toxicity criteria of the Common Terminology Criteria for Adverse Events v. 3.0 was used.

Statistical analyses

The endpoint was disease-free survival (DFS), which was defined as the time from the start of RT to clinical progression or death for any cause. Statistical analyses were performed using the Mann–Whitney U test to compare the recurrence and non-recurrence followed by Fisher’s protected least significance test for all pairwise comparisons. The ROC curve of the ADCmean for the recurrence was generated to determine the cut-off value that yielded optimal sensitivity and specificity. The patient population was subdivided according to the risk factors for recurrence. Moreover, the DFS was analyzed using the Kaplan–Meier method. The log-rank tests were used to examine the differences between the survival curves. The following were investigated: age, performance status, stage, pelvic lymph node metastasis, histologic tumor grade, maximal diameter of the primary tumor, concurrent cisplatin chemotherapy, and ADCmean to explore the risk factors for recurrence. Univariate logistic regression analyses were performed to evaluate the data using IBM SPSS Statistics 20.0 (SPSS, Armonk, NY). Multivariate analysis was not performed owing to the limited data. A two-sided p-value < 0.05 was considered statistically significant for all statistical tests.

Results

Patient outcomes

The patients and tumor characteristics are presented in Table 2. The patients' median age was 62 (range, 25–87) years. Of the total, 94% of patients had Eastern Cooperative Oncology Group (ECOG) performance status of 0 or 1. The FIGO system defined the patients’ stage as follows: three stage Ib1 cancers; one stage Ib2 cancers; two stage IIa1 cancers; 15 stage IIb cancers; one stage IIIa cancers; six stage IIIb cancer; and, three stage IVa cancers. Three patients (10%) had double cancers. The patients' median follow-up duration was 25 months (range, 8–93). Overall survival probabilities at 1 and 3 years were 100 and 90%, respectively (Figure 2a). Two (6%) patients were identified with a cancer-related death at 21 and 25 months after RT. DFS probabilities at 1 and 3 years were 77 and 69%, respectively (Figure 2b). The recurrence was recognized in 9 (29%) of the 31 cases; local failure only in 3 cases, and local failure and distant metastasis in 2, distant metastasis only in 4. The median time for recurrence was 6 months (range, 3–21) after RT initiation.

Table 2.

Patient and tumor characteristics

No. of patients 31
Age (years), median (range) 62 (25–87)
Performance status
 0 27
 1 2
 2 2
FIGO stage
 Ib1 3 (10)
 Ib2 1 (3)
 IIa1 2 (6)
 IIb 15 (48)
 IIIa 1 (3)
 IIIb 6 (19)
 IVa 3 (10)
Pelvic lymph node metastasis (percentage)
 Yes 15 (48)
 No 16 (52)
Histologic tumor grade (percentage)
 Well differentiated 2 (6)
 Moderate differentiated 22 (71)
 Poor differentiated 7 (23)
Size of primary tumor (percentage)
 ≤4 cm 7 (23)
 >4 cm 24 (77)
Chemotherapy (percentage)
 Yes 26 (84)
 No 5 (16)

Figure 2.

Figure 2.

Overall survival and disease-free survival probabilities. (a) Overall survival probabilities at 1 and 3 years were 100 and 90%, respectively. Two (6%) were diagnosed identified as with a cancer-related death, at 21 and 25 months after the start of RT. (b) Disease-free survival probabilities at 1 and 3 years were 77 and 69%, respectively. The recurrence was recognized in 9 (29%) of the 31 cases.

Association between ADC mean and recurrence

The average values of ADCmean for the primary tumor of cervical cancer with recurrence and non-recurrence were found to be 0.840 ± 0.064×10−3 mm2/s and 0.949 ± 0.082×10−3 mm2/s, respectively. The difference in ADCmean between the two groups was statistically significant (p < 0.001), calculated with the Mann–Whitney U test. ROC analysis of recurrence showed that the area under the ADCmean curve was 0.889 (95% CI, 0.771–1.000; p = 0.001) (Figure 3). The cut-off value of ADCmean was 0.900 × 10− 3 mm2/s, with a sensitivity of 86.4% and a specificity of 88.9%.

Figure 3.

Figure 3.

Receiver operating characteristic analysis of recurrence The area under the curve for ADCmean was 0.889 (95% CI, 0.771–1.000; p = 0.001). The cut-off value of ADCmean was 0.900 × 10−3 mm2/s, with a sensitivity of 86.4% and a specificity of 88.9%. ADC, apparent diffusion coefficient CI, confidence interval.

Univariate analyses

The relationships between the risk factors and recurrence are summarized in Table 3. By univariate analysis, the ADCmean was the only factor significantly associated with recurrence (p < 0.001), calculated with the log-rank test. The 2-year DFS probabilities for patients with cervical cancer of ADCmean ≥ 0.900×10−3 mm2/s and <0.900 ×10−3 mm2/s were 95 and 24%, respectively (Figure 4). The DFS probability between these patients was calculated using the log-rank test and was found to be statistically significant (p < 0.001). The relationships between the ADCmean and the other risk factors, calculated with the Mann–Whitney U test, are shown in Table 4. ADCmean had been not related with the other risk factors.

Table 3.

Risk factors associated with recurrence

Recurrence
n = 9
p-value
Age
(≤60 y vs. >60 y)
15% (2/13) vs 39% (7/18) 0.168
PS
(0 vs. ≥1)
30% (8/27) vs 25% (1/4) 0.989
FIGO stage
(Ib1/IIb vs IIIa/IVa)
33% (7/21) vs 20% (2/10) 0.484
Histologic tumor grade
(well/moderate vs poor)
33% (8/24) vs 14% (1/7) 0.384
Pelvic lymph node metastasis
(negative vs positive)
25% (4/16) vs 33% (5/15) 0.581
Maximal diameter of primary tumor
(<4 cm vs. ≥4 cm)
29% (2/7) vs 29% (7/24) 0.893
Concurrent cisplatin chemotherapy
(negative vs positive)
40% (2/5) vs 27% (7/26) 0.556
ADCmean × 10−3 mm2/s
(<0.900 vs.≥0.900)
73% (8/11) vs 5% (1/20) <0.001

ADC, apparent diffusion coefficient;FIGO, International Federation of Gynecology and Obstetrics; PS, performance status.

Figure 4.

Figure 4.

Disease-free survival probabilities associated with ADC mean The 2–year DFS probabilities for patients with cervical cancer of ADCmean ≥ 0.900×10−3 mm2/s and <0.900 ×10−3 mm2/s were 95 and 24%, respectively. DFS probability difference between these patients was calculated using the log–rank test and was found to be statistically significant (p < 0.001). ADC, apparent diffusion coefficient; DFS, disease-free survival

Table 4.

Relationships between the ADC mean and the other risk factors

Median (range) ADC mean × 10−3 mm2/s p-value
Age 0.779
 ≤60 y 0.930 (0.692–1.018)
 >60 y 0.943 (0.726–1.069)
PS 0.616
 0 0.930 (0.692–1.069)
 ≥1 0.955 (0.831–1.000)
FIGO stage 0.597
 Ib1/IIb 0.930 (0.692–1.018)
 IIIa/IVa 0.951 (0.726–1.069)
Histologic tumor grade 0.171
 Well/moderate 0.934 (0.692–1.069)
 Poor 0.980 (0.834–1.058)
Pelvic lymph node metastasis 0.635
 Negative 0.943 (0.692–1.069)
 Positive 0.930 (0.726–1.018)
Maximal diameter of primary tumor 0.539
 <4 cm 0.947 (0.692–1.069)
 ≥4 cm 0.934 (0.726–1.058)

ADC, apparent diffusion coefficient;PS, performance status.

Complications

Table 5 shows the acute and late complications of irradiation. 5 (25%) of 31 patients had Grade 2 acute diarrhea. One patient had Grade 2 late proctitis, and the other had Grade 2 hematuria. No patients showed Grade 3 or greater acute and late toxicities. The clinical data and risk factors for all cases are shown in Table 6.

Table 5.

Acute and late toxicities

Grade 0 or 1 2 3 4
Acute toxicities
 Cystitis 31 0 0 0
 Diarrhea 26 5 0 0
Late toxicities
 Proctitis 30 1 0 0
 Urinary retention 31 0 0 0
 Hematuria 30 1 0 0

Table 6.

The clinical data and risk factors for all cases

No. Age PS FIGO stage Histologic tumor grade Pelvic lymph node metastasis Maximal diameter of the primary tumor (cm) Concurrent cisplatin chemotherapy ADC mean × 10−3 mm2/s Local control Local control duration (M)
1 64 0 4a moderate positive 8.5 negative 0.987 control 31
2 60 0 2b moderate negative 4.3 positive 0.960 control 93
3 65 0 4a moderate negative 4.4 positive 0.892 recurrence 5
4 70 0 2b moderate negative 4.1 positive 0.100 control 71
5 82 0 3b moderate negative 3 positive 0.107 control 66
6 77 0 3b poor negative 4.6 positive 0.106 control 79
7 64 0 2b well negative 5.1 positive 0.965 control 71
8 57 0 1b1 moderate negative 2.2 positive 0.692 control 63
9 66 0 3b moderate positive 5 positive 0.939 control 38
10 35 0 2b moderate positive 6 positive 0.942 control 28
11 62 0 3b well positive 6 positive 0.726 recurrence 10
12 63 0 2b moderate negative 3.5 positive 0.989 control 44
13 60 0 2b poor negative 4.8 positive 0.917 control 39
14 70 0 3b moderate positive 5 positive 0.828 control 41
15 44 0 2b poor positive 5.5 positive 0.980 control 40
16 62 0 2b moderate negative 5.8 positive 0.767 recurrence 21
17 35 0 2b moderate negative 5.4 positive 0.940 control 20
18 57 0 3b poor negative 5.5 positive 0.843 control 28
19 54 0 1b2 moderate positive 3.7 positive 0.918 recurrence 6
20 72 0 2b poor negative 4.2 positive 0.894 recurrence 3
21 86 1 2b moderate negative 3.2 negative 0.831 recurrence 19
22 25 0 2b moderate positive 6.8 positive 0.906 control 21
23 57 0 1b1 poor positive 2 positive 0.102 control 22
24 47 0 2b moderate positive 4.4 positive 0.805 recurrence 6
25 33 0 2b poor positive 6.1 positive 0.101 control 15
26 72 0 1b1 moderate positive 5.3 negative 0.855 recurrence 7
27 85 1 2a1 moderate negative 2.8 negative 0.947 control 16
28 79 2 4a moderate positive 5.9 negative 0.963 control 14
29 87 2 3a moderate negative 6 positive 0.100 control 8
30 59 0 2b moderate positive 4.7 positive 0.930 control 13
31 67 0 2a1 moderate positive 4.2 positive 0.870 recurrence 5

ADC, apparent diffusion coefficient; FIGO, The International Federation of Gynecology and Obstetrics; PS, performance status.

Discussion

In the present study, the average values of ADCmean for the primary tumor of the cervical cancer with the recurrence and non-recurrence were found to be 0.840 ± 0.064×10–3mm2/s and 0.949 ± 0.082×10–3 mm2/s, respectively. The difference in ADCmean between the two groups was statistically significant (p < 0.001). Additionally, the 2-year DFS probabilities for patients with cervical cancer of ADCmean ≥ 0.900×10–3 mm2/s and <0.900 ×10–3 mm2/s were 95% and 24%, respectively (Figure 3). The difference in the DFS probability between patients with cervical cancer of ADCmean ≥ 0.900×10–3 mm2/s and <0.900 ×10–3 mm2/s, calculated using the log–rank test, was statistically significant (p < 0.001).

Although RT is the optimal therapy for cervical cancer with an appreciable outcome, treatment for a tumor relapse remains tough. Thus, we consider it clinically essential to find patients with a high-risk for recurrence within a short time and who might benefit from additional or novel therapies, such as targeted agents with chemotherapy or adjuvant consolidation chemotherapy after RT. 20,21 In previous studies, the stage, tumor size, histological type, histological grade, presence of lymphovascular space invasion and metastasis to regional lymph nodes at the time of treatment have been reported to be significant prognostic factors for cervical cancer. 22–24 However, these parameters are not sufficient to accurately predict prognosis. It is challenging to predict the prognosis of patients treated with RT without performing histopathological retrieval. Therefore, additional markers would help determine a patient’s risk of recurrence or death. It is now accepted that new approaches for pre-treatment of cervical cancers are pivotal to further the disease’s favorable prognosis.

Quantitative assessment is possible by calculating the ADC, which is measured by DWI. 25 It has been suspected that the decreased ADC values in malignant tumors may be caused by their increased tissue cellularity or cell density, larger nuclei with more abundant macromolecular proteins, and less extracellular space. 26–28 Table 7 summarizes published reports of the risk factor for primary cervical cancer recurrence associated with ADC. A few previous studies have reported that DWI has the potential for predicting disease control or survival in cervical cancer patients treated with curative intent. 11,29–34 Payne et al reported that the ADC values are expected to decrease when considering increasing tumor grades, as higher-grade tumors typically have a higher cellular density, resulting in restricted water diffusion in cervical cancer. Lower pre-treatment ADC values were associated with worse DFS in early-stage cervical cancer patients treated mostly with surgery. 11 Regarding patients treated with RT, a previous study demonstrated that a lower pre-treatment 95th percentile ADC was associated with worse DFS. 34 Ho et al found that pre-treatment ADC was an independent predictor of DFS in cervical cancer patients treated with RT. 32 Onal et al demonstrated that pre-treatment ADC in cervical cancer patients treated with RT was an independent prognostic factor for DFS and OS. 29 Although several values of ADC have been used for prognostic factors in cervical cancer, the complicated calculation methods were used for some factors. The method of calculation for prognostic factors should be possibly uncomplicated in daily clinical task. We consider that the measurement of ADCmean was uncomplicated method, and ADCmean was appropriate for prognostic factors in cervical cancer.

Table 7.

The summary for published reports of the risk factor for primary cervical cancer recurrence associated with ADC

Firsrt Author Pt No Median follow-up Timing of MRI Histology Endpoint Prognostic factor cut-off value p
Nakamura K (36) 80pts 32.0M Pretreatment Squamous cell carcinoma, all DFS ADCmean 0.852 × 10−3 mm2/s <0.001
ADCmin 0.670 × 10−3 mm2/s 0.0210
Onal C (29) 44pts 25.0M Pretreatment Posttreatment Squamous cell carcinoma, all DFS OS ADCmean DFS, 0.878 × 10−3 mm2/s 0.006
OS, 0.878 × 10−3 mm2/s 0.006
Park JJ (31) 67pts 32.4M Pretreatment During treatment Squamous cell carcinoma, 59pts Non-Squamous cell carcinoma, 8pts DFS Pre - during treatment /pretreatment ADCmean x 100 35.1% <0.001
Gu KW (33) 124pts 43.5M Pretreatment Posttreatment Squamous cell carcinoma, 103pts Adenocarcinoma/other, 21pts DFS CSS OS Post - pretreatment /pretreatment ADCmean x 100 DFS, 27.8% 0.001
CSS, 16.1% 0.002
OS, 16.1% <0.001
Ho JC (32) 69pts 16.7M Pretreatment Squamous cell carcinoma, 48pts Adenocarcinoma/other, 21pts DFS ADCmean 0.940 × 10−3 mm2/s 0.02
Our study 31pts 25M Pretreatment Squamous cell carcinoma, all DFS ADCmean 0.900 × 10−3 mm2/s <0.001

ADC, apparent diffusion coefficient; CSS, cancer Specific Survival; DFS, disease-free survival; OS, overall survival.

The most common histopathology subtype is squamous cell carcinoma, while adenocarcinoma is relatively rare. 35 However, adenocarcinoma has the propensity to have a higher ADC than squamous cell carcinoma. 36 Therefore, we assessed the ADC values exclusively in patients with squamous cell carcinoma. Although ADCmean was selected as a risk factor for the recurrence in this study, the other values of such as minimum and maximum values of ADC were used for risk factors in the other studies. Because the minimum or maximum values of ADC are measured as very low or high for hematoma, cystic or necrotic portions of cervical tumor, measurement errors can occur. Nakamura et al reported that the ADC mean of primary cervical cancer was an independent predictive factor for disease recurrence by multivariate analysis due to evaluating whether pre-treatment ADCmax, ADCmean, ADCmin on MRI predicted the risk group of recurrence. 37 Therefore, we selected the ADC mean as a risk factor for recurrence.

We acknowledge that there are some limitations to our study. First, our study could not be free of measurement errors because ADC values were derived from manually drawn ROIs. Second, our study was a retrospective study in a single-center, with a relatively small patient population and a relatively short follow-up period. A larger number of patients and long-term follow-up would support the strength of our data, and further confirmation by a prospective trial could reinforce our findings.

Conclusion

Our findings suggest that ADCmean values of the primary tumor could serve as an indicator for the risk of disease recurrence in patients with pre-treatment assessment of cervical cancer.

Contributor Information

Mitsuru Okubo, Email: okubo@tokyo-med.ac.jp.

Tomohiro Itonaga, Email: ti19850704@yahoo.co.jp.

Tatsuhiko Saito, Email: saito19880704@gmail.com.

Sachika Shiraishi, Email: s-nogi@tokyo-med.ac.jp.

Daisuke Yunaiyama, Email: yuunai@tokyo-med.ac.jp.

Ryuji Mikami, Email: mikami-r@tokyo-med.ac.jp.

Akira Sakurada, Email: kouayu0306@yahoo.co.jp.

Shinji Sugahara, Email: ssuga@tokyo-med.ac.jp.

Koichi Tokuuye, Email: ktokuue@tokyo-med.ac.jp.

Kazuhiro Saito, Email: saito-k@tokyo-med.ac.jp.

REFERENCES

  • 1. Siegel R, Naishadham D, Jemal A. Cancer statistics, 2012. CA Cancer J Clin 2012; 62: 10–29. doi: 10.3322/caac.20138 [DOI] [PubMed] [Google Scholar]
  • 2. Benedet JL, Odicino F, Maisonneuve P, Beller U, Creasman WT, Heintz AP, et al. Carcinoma of the cervix uteri. J Epidemiol Biostat 2001; 6: 7–43. [PubMed] [Google Scholar]
  • 3. Marth C, Landoni F, Mahner S, McCormack M, Gonzalez-Martin A, Colombo N, et al. Cervical cancer: ESMO clinical practice guidelines for diagnosis, treatment and follow-up. Ann Oncol 2017; 28(suppl_4): iv72–83. doi: 10.1093/annonc/mdx220 [DOI] [PubMed] [Google Scholar]
  • 4. Friedlander M, Grogan M, .U.S. Preventative Services Task Force . Guidelines for the treatment of recurrent and metastatic cervical cancer. Oncologist 2002; 7: 342–7. doi: 10.1634/theoncologist.2002-0342 [DOI] [PubMed] [Google Scholar]
  • 5. Rose PG, Bundy BN, Watkins EB, Thigpen JT, Deppe G, Maiman MA, et al. Concurrent cisplatin-based radiotherapy and chemotherapy for locally advanced cervical cancer. N Engl J Med 1999; 340: 1144–53. doi: 10.1056/NEJM199904153401502 [DOI] [PubMed] [Google Scholar]
  • 6. Delgado G, Bundy BN, Fowler WC, Stehman FB, Sevin B, Creasman WT, et al. A prospective surgical pathological study of stage I squamous carcinoma of the cervix: a gynecologic Oncology Group study. Gynecol Oncol 1989; 35: 314–20. doi: 10.1016/0090-8258(89)90070-X [DOI] [PubMed] [Google Scholar]
  • 7. Bammer R. Basic principles of diffusion-weighted imaging. Eur J Radiol 2003; 45: 169–84. doi: 10.1016/S0720-048X(02)00303-0 [DOI] [PubMed] [Google Scholar]
  • 8. Kuang F, Ren J, Zhong Q, Liyuan F, Huan Y, Chen Z. The value of apparent diffusion coefficient in the assessment of cervical cancer. Eur Radiol 2013; 23: 1050–8. doi: 10.1007/s00330-012-2681-1 [DOI] [PubMed] [Google Scholar]
  • 9. Miccò M, Vargas HA, Burger IA, Kollmeier MA, Goldman DA, Park KJ, et al. Combined pre-treatment MRI and 18F-FDG PET/CT parameters as prognostic biomarkers in patients with cervical cancer. Eur J Radiol 2014; 83: 1169–76. doi: 10.1016/j.ejrad.2014.03.024 [DOI] [PubMed] [Google Scholar]
  • 10. Liu Y, Bai R, Sun H, Liu H, Zhao X, Li Y. Diffusion-Weighted imaging in predicting and monitoring the response of uterine cervical cancer to combined chemoradiation. Clin Radiol 2009; 64: 1067–74. doi: 10.1016/j.crad.2009.07.010 [DOI] [PubMed] [Google Scholar]
  • 11. Himoto Y, Fujimoto K, Kido A, Baba T, Tanaka S, Morisawa N, et al. Pretreatment mean apparent diffusion coefficient is significantly correlated with event-free survival in patients with international Federation of gynecology and obstetrics stage Ib to IIIB cervical cancer. Int J Gynecol Cancer 2015; 25: 1079–85. doi: 10.1097/IGC.0000000000000445 [DOI] [PubMed] [Google Scholar]
  • 12. Zhang Y, Chen J-Y, Xie C-M, Mo Y-X, Liu X-W, Liu Y, et al. Diffusion-Weighted magnetic resonance imaging for prediction of response of advanced cervical cancer to chemoradiation. J Comput Assist Tomogr 2011; 35: 102–7. doi: 10.1097/RCT.0b013e3181f6528b [DOI] [PubMed] [Google Scholar]
  • 13. Rizzo S, Summers P, Raimondi S, Belmonte M, Maniglio M, Landoni F, et al. Diffusion-Weighted MR imaging in assessing cervical tumour response to nonsurgical therapy. Radiol Med 2011; 116: 766–80. doi: 10.1007/s11547-011-0650-4 [DOI] [PubMed] [Google Scholar]
  • 14. Harry VN, Gilbert FJ, Parkin DE. Predicting the response of advanced cervical and ovarian tumors to therapy. Obstet Gynecol Surv 2009; 64: 548–60. doi: 10.1097/OGX.0b013e3181abc114 [DOI] [PubMed] [Google Scholar]
  • 15. Zhang J, Tehrani YM, Wang L, Ishill NM, Schwartz LH, Hricak H. Renal masses: characterization with diffusion-weighted MR imaging--a preliminary experience. Radiology 2008; 247: 458–64. doi: 10.1148/radiol.2472070823 [DOI] [PubMed] [Google Scholar]
  • 16. Hayashida Y, Hirai T, Morishita S, Kitajima M, Murakami R, Korogi Y, et al. Diffusion-Weighted imaging of metastatic brain tumors: comparison with histologic type and tumor cellularity. AJNR Am J Neuroradiol 2006; 27: 1419–25. [PMC free article] [PubMed] [Google Scholar]
  • 17. Kanauchi N, Oizumi H, Honma T, Kato H, Endo M, Suzuki J, et al. Role of diffusion-weighted magnetic resonance imaging for predicting of tumor invasiveness for clinical stage Ia non-small cell lung cancer. Eur J Cardiothorac Surg 2009; 35: 706–11. doi: 10.1016/j.ejcts.2008.12.039 [DOI] [PubMed] [Google Scholar]
  • 18. Taouli B, Thakur RK, Mannelli L, Babb JS, Kim S, Hecht EM, et al. Renal lesions: characterization with diffusion-weighted imaging versus contrast-enhanced MR imaging. Radiology 2009; 251: 398–407. doi: 10.1148/radiol.2512080880 [DOI] [PubMed] [Google Scholar]
  • 19. Pötter R, Haie-Meder C, Van Limbergen E, Barillot I, De Brabandere M, Dimopoulos J, et al. Recommendations from gynaecological (GYN) GEC ESTRO Working Group (II): concepts and terms in 3D image-based treatment planning in cervix cancer brachytherapy-3D dose volume parameters and aspects of 3D image-based anatomy, radiation physics, radiobiology. Radiother Oncol 2006; 78: 67–77. doi: 10.1016/j.radonc.2005.11.014 [DOI] [PubMed] [Google Scholar]
  • 20. Dueñas-González A, Zarbá JJ, Patel F, Alcedo JC, Beslija S, Casanova L, et al. Phase III, open-label, randomized study comparing concurrent gemcitabine plus cisplatin and radiation followed by adjuvant gemcitabine and cisplatin versus concurrent cisplatin and radiation in patients with stage IIb to IVA carcinoma of the cervix. J Clin Oncol 2011; 29: 1678–85. doi: 10.1200/JCO.2009.25.9663 [DOI] [PubMed] [Google Scholar]
  • 21. Yavas G, Yavas C, Sen E, Oner I, Celik C, Ata O. Adjuvant carboplatin and paclitaxel after concurrent cisplatin and radiotherapy in patients with locally advanced cervical cancer. Int J Gynecol Cancer 2019; 29: 42–7. doi: 10.1136/ijgc-2018-000022 [DOI] [PubMed] [Google Scholar]
  • 22. Boyce J, Fruchter RG, Nicastri AD, Ambiavagar PC, Reinis MS, Nelson JH. Prognostic factors in stage I carcinoma of the cervix. Gynecol Oncol 1981; 12(2 Pt 1): 154–65. doi: 10.1016/0090-8258(81)90145-1 [DOI] [PubMed] [Google Scholar]
  • 23. Burghardt E, Pickel H, Haas J, Lahousen M. Prognostic factors and operative treatment of stages Ib to IIb cervical cancer. Am J Obstet Gynecol 1987; 156: 988–96. doi: 10.1016/0002-9378(87)90374-7 [DOI] [PubMed] [Google Scholar]
  • 24. van Bommel PF, van Lindert AC, Kock HC, Leers WH, Neijt JP. A review of prognostic factors in early-stage carcinoma of the cervix (FIGO I B and II a) and implications for treatment strategy. Eur J Obstet Gynecol Reprod Biol 1987; 26: 69–84. doi: 10.1016/0028-2243(87)90010-4 [DOI] [PubMed] [Google Scholar]
  • 25. Provenzale JM, Mukundan S, Barboriak DP. Diffusion-Weighted and perfusion MR imaging for brain tumor characterization and assessment of treatment response. Radiology 2006; 239: 632–49. doi: 10.1148/radiol.2393042031 [DOI] [PubMed] [Google Scholar]
  • 26. Matsushima N, Maeda M, Takamura M, Takeda K. Apparent diffusion coefficients of benign and malignant salivary gland tumors. Comparison to histopathological findings. J Neuroradiol 2007; 34: 183–9. doi: 10.1016/j.neurad.2007.04.002 [DOI] [PubMed] [Google Scholar]
  • 27. Abdel Razek AAK, Soliman NY, Elkhamary S, Alsharaway MK, Tawfik A. Role of diffusion-weighted MR imaging in cervical lymphadenopathy. Eur Radiol 2006; 16: 1468–77. doi: 10.1007/s00330-005-0133-x [DOI] [PubMed] [Google Scholar]
  • 28. Humphries PD, Sebire NJ, Siegel MJ, Olsen Øystein E, Olsen Ø E. Tumors in pediatric patients at diffusion-weighted MR imaging: apparent diffusion coefficient and tumor cellularity. Radiology 2007; 245: 848–54. doi: 10.1148/radiol.2452061535 [DOI] [PubMed] [Google Scholar]
  • 29. Onal C, Erbay G, Guler OC. Treatment response evaluation using the mean apparent diffusion coefficient in cervical cancer patients treated with definitive chemoradiotherapy. J Magn Reson Imaging 2016; 44: 1010–9. doi: 10.1002/jmri.25215 [DOI] [PubMed] [Google Scholar]
  • 30. Payne GS, Schmidt M, Morgan VA, Giles S, Bridges J, Ind T, et al. Evaluation of magnetic resonance diffusion and spectroscopy measurements as predictive biomarkers in stage 1 cervical cancer. Gynecol Oncol 2010; 116: 246–52. doi: 10.1016/j.ygyno.2009.09.044 [DOI] [PubMed] [Google Scholar]
  • 31. Park JJ, Kim CK, Park BK. Prognostic value of diffusion-weighted magnetic resonance imaging and 18F-fluorodeoxyglucose-positron emission tomography/computed tomography after concurrent chemoradiotherapy in uterine cervical cancer. Radiother Oncol 2016; 120: 507–11. doi: 10.1016/j.radonc.2016.02.014 [DOI] [PubMed] [Google Scholar]
  • 32. Ho JC, Allen PK, Bhosale PR, Rauch GM, Fuller CD, Mohamed ASR, et al. Diffusion-Weighted magnetic resonance imaging as a predictor of outcome in cervical cancer after chemoradiation. Int J Radiat Oncol Biol Phys 2017; 97: 546–53. doi: 10.1016/j.ijrobp.2016.11.015 [DOI] [PMC free article] [PubMed] [Google Scholar]
  • 33. Gu K-W, Kim CK, Choi CH, Yoon YC, Park W. Prognostic value of ADC quantification for clinical outcome in uterine cervical cancer treated with concurrent chemoradiotherapy. Eur Radiol 2019; 29: 6236–44. doi: 10.1007/s00330-019-06204-w [DOI] [PubMed] [Google Scholar]
  • 34. Gladwish A, Milosevic M, Fyles A, Xie J, Halankar J, Metser U, et al. Association of apparent diffusion coefficient with disease recurrence in patients with locally advanced cervical cancer treated with radical chemotherapy and radiation therapy. Radiology 2016; 279: 158–66. doi: 10.1148/radiol.2015150400 [DOI] [PubMed] [Google Scholar]
  • 35. Intaraphet S, Kasatpibal N, Siriaunkgul S, Sogaard M, Patumanond J, Khunamornpong S, et al. Prognostic impact of histology in patients with cervical squamous cell carcinoma, adenocarcinoma and small cell neuroendocrine carcinoma. Asian Pac J Cancer Prev 2013; 14: 5355–60. doi: 10.7314/APJCP.2013.14.9.5355 [DOI] [PubMed] [Google Scholar]
  • 36. Liu Y, Bai R, Sun H, Liu H, Wang D. Diffusion-Weighted magnetic resonance imaging of uterine cervical cancer. J Comput Assist Tomogr 2009; 33: 858–62. doi: 10.1097/RCT.0b013e31819e93af [DOI] [PubMed] [Google Scholar]
  • 37. Nakamura K, Joja I, Nagasaka T, Fukushima C, Kusumoto T, Seki N, et al. The mean apparent diffusion coefficient value (ADCmean) on primary cervical cancer is a predictive marker for disease recurrence. Gynecol Oncol 2012; 127: 478–83. doi: 10.1016/j.ygyno.2012.07.123 [DOI] [PubMed] [Google Scholar]

Articles from BJR Open are provided here courtesy of Oxford University Press

RESOURCES