Development and validation of a novel nomogram for individualized prediction of survival in cancer of unknown primary (CUP)

Abstract

Purpose:

Prognostic uncertainty is a major challenge for cancer of unknown primary (CUP). Current models limit a meaningful patient-provider dialogue. We aimed to establish a nomogram for predicting overall survival (OS) in CUP based on robust clinicopathological prognostic factors.

Experimental Design:

We evaluated 521 patients with CUP at MDACC [MD Anderson Cancer Center, Houston, USA] (2012–2016). Baseline variables were analyzed using Cox-regression and nomogram developed using significant predictors. Predictive accuracy and discriminatory performance were assessed by calibration curves, concordance probability estimate (CPE) (±standard error [SE]) and concordance statistic (C-index). The model was subjected to bootstrapping and multi-institutional external validations using two independent CUP cohorts: V1 (MDACC [2017], N=103) and V2 (BC Cancer, Vancouver, Canada and Sarah Cannon Cancer Center/Tennessee Oncology, USA, N=302).

Results:

Baseline characteristics of entire cohort (N=926) included: median age (63 years), women (51%), ECOG-PS 0–1 (64%), adenocarcinomas (52%), ≥3 sites of metastases (30%), median follow-up duration and OS of 40.1 and 14.7 months, respectively. Five independent prognostic factors were identified: gender, ECOG-PS, histology, number of metastatic sites and neutrophil-lymphocyte ratio. The resulting model predicted OS with CPE of 0.69 (SE: ±0.01) [C-index: 0.71 (95%CI:0.68–0.74)] outperforming Culine/Seve prognostic models (CPE: 0.59±0.01). CPE for external validation cohorts V1 and V2 were 0.67 (±0.02) and 0.70 (±0.01), respectively. Calibration curves for 1-year OS showed strong agreement between nomogram prediction and actual observations in all cohorts.

Conclusions:

Our user-friendly CUP nomogram integrating commonly available baseline factors provides robust personalized prognostication which can aid clinical decision making and selection/stratification for clinical trials.

Introduction

Cancer of unknown primary (CUP) is a formidable diagnosis, one that stirs a sense of apprehension in doctors and patients alike.(1–4) Although often seen as a “group of cancers” that at presentation share the common trait of being metastatic without an identifiable primary, most agree it is a very heterogeneous disease.(5) This patient and tumor heterogeneity often results in wide-ranging survival outcomes amplifying the ambiguity surrounding CUP and impeding personalized care of patients.

Understanding and predicting prognosis is vital to making informed decisions for management of advanced cancers.(6,7) Candid and realistic conversations about prognosis are desired by patients and endorsed by key guidelines.(8,9) Early prognostic discussions result in better patient education about goals of care and life expectancy.(10) Since baseline perceptions of prognosis can shape treatment decisions, prognostic inaccuracies can lead to overtreatment, patient and caregiver distress, poor quality of life and adverse medical and social outcomes.(7,11–14)

Accurate and individualized prediction of survival in CUP has been a key challenge in clinical care and trials, leading to “best guess” discussions and suboptimal study designs.(4,15) Consensus reference staging systems to inform prognosis, large scale outcome studies and prospective trials to draw approximations, which facilitate prognostication in other cancers, are all but lacking in CUP. Moreover, the relative infrequency with which oncologists encounter CUP patients in their practice, limits experience and intuition which physicians rely on for discussing prognosis.

Despite an abundance of studies evaluating individual prognostic factors, classification models in CUP are limited.(16) Culine and colleagues developed a prognostic model in 2002 with patient performance status (PS) and serum lactate dehydrogenase (LDH) (or if unavailable, presence of liver metastases).(17) This model assigned a good-risk and a poor-risk group with median survivals of 11.7 and 3.9 months, respectively.(17) Although, helpful with generalized projections, this and other prior categorical models lack individualization and are limited in enabling a meaningful dialogue between patients and providers about survival estimates.(17–19) This knowledge gap of personalized prognostication puts patients with CUP and oncologists that treat CUP at a huge disadvantage.(2,3) The resulting indecision instills significant anxiety in patients and trepidation in providers towards approaching discussions of prognosis and goals of care in CUP.(1–4)

In this study, we aimed to establish and validate a novel prognostic model using a nomogram-based approach for predicting overall survival (OS) centered on robust and readily available baseline clinicopathological prognostic factors in CUP. We chose this nomogram-based approach due to its ability to condense a complex statistical model embracing diverse prognostic factors into a simple graphical representation.(20) A nomogram can generate a straightforward numerical probability of survival and is widely popular among oncologists due to its ability to generate individualized predictions and a user-friendly interface.(20) We envision that this CUP survival nomogram will enable accurate and effective communication between patients and oncologists empowering them in making personalized decisions with the ultimate goal of improving understanding and clinical outcomes in CUP.

Materials and Methods

Patient Population

Development and Internal Validation Cohort (Cohort A)

We identified a cohort of 521 consecutive patients with a diagnosis of CUP at The University of Texas MD Anderson Cancer Center (Houston, Texas, USA) (MDACC) over 5 years between January 2012 and December 2016 using a retrospective-prospective CUP database (Supplement Fig S1). This cohort (A) served as our discovery cohort and was used for the development of the model (nomogram) with internal validation. Eligible cases were defined as those with biopsy proven metastatic cancer without a detectable primary after an appropriate diagnostic work-up as per standard guidelines.(5) To minimize diagnostic variability which can occur with CUP, only cases reviewed and confirmed by CUP pathologists and oncologists at MDACC were included. Cases were excluded if they lacked complete history and physical, computed tomography scan of chest, abdomen and pelvis (alternate equivalent imaging if IV contrast contraindicated was allowed) and symptom/pathology directed endoscopy or additional imaging. Historically described “favorable subsets” such as adenocarcinoma in axillary lymph nodes in women (breast cancer), squamous cell carcinoma in neck nodes (head and neck cancer), papillary or serous tumors in the peritoneal cavity in women (ovarian cancer) were excluded from the MDACC CUP database and this development cohort since they are treated as their putative primaries and their natural history differs from the “unfavorable subset” of CUP.

Demographic and clinicopathological variables at baseline (age, gender, eastern cooperative oncology group - performance status [ECOG PS], tumor histology, sites of metastases (uniquely defined sites: liver, lung, peritoneum/retroperitoneum, bone, brain, lymph nodes, ovarian, adrenal, skin/subcutaneous, muscle), number of metastatic sites, laboratory values [specifically, LDH and NLR (neutrophil-lymphocyte ratio)] and survival data was retrieved from the database and electronic medical records. All patients received therapies in agreement with standard CUP guidelines as per recommendations of their treating physicians (Supplement Table S1). The study was performed under a MDACC institutional review board (IRB) approved protocol, waiving written informed consent by patients and in accordance with the Declaration of Helsinki.

External Validation Cohorts (Cohorts V1 and V2)

Three independent patient populations with CUP (similar eligibility criteria as cohort A) served as external validation cohorts (Supplement Fig S1). Cohort V1 of 103 consecutive MDACC patients with CUP between January 2017 and December 2017 was identified using the CUP database as above. Cohort V2 included a de-identified pooled dataset of 302 consecutive patients at 2 centers: British Columbia Cancer (BC Cancer), Vancouver, British Columbia (Canada) between January 2014 and September 2016 (N = 202) and Sarah Cannon Cancer Center/Tennessee Oncology (SCCC/TO), Nashville, U.S.A between January 2012 and December 2015 (N = 100). All patients in these cohorts met the eligibility criteria stated above for the development cohorts and data regarding all variables of interest for the nomogram were collected for these patients. The study was performed under BC Cancer and SCCC/TO IRB approved protocols, with waiver of written informed consent by patients and in accordance with the Declaration of Helsinki.

Statistical Methodology

Descriptive statistics were used to summarize patient characteristics. Randomization, blinding and power analysis is not relevant to this study. Fisher’s exact/Chi-squared test were used for comparisons between groups. Cox proportional models were fit to assess association between variables and OS and results were expressed in hazard ratios (HRs) and 95% confidence intervals (95%CIs). All tests were two-sided and P values of < 0.05 were considered statistically significant. Statistical analyses were carried out with R software (version 3.6.1) and GraphPad Prism version 8.00 (GraphPad [RRID:SCR_002798] software, La Jolla, CA) used for generating Kaplan-Meier curves.

Formulation of the CUP Nomogram

The primary outcome was overall survival (OS), defined as the time between date of diagnosis and death. Patients alive at last follow-up were censored. Baseline prognostic parameters and cutoffs for analyses were selected a priori based on prior research and evidence. Covariates included were age, gender, ECOG PS (0 vs. 1 vs. ≥ 2), NLR, presence of liver metastases (no vs. yes), number of metastatic sites (NMS: < 3 vs. ≥ 3) and tumor histology (non-adenocarcinoma vs. adenocarcinoma). NLR was modeled as a continuous variable using 3-knot restricted cubic spline with number of knots based on Akaike information criterion.(21–23) Due to high variability in reference range and high proportion of missing values, LDH (27% missing) was not included to ensure consistency. A nomogram was constructed using significant predictors based on multivariable Cox regression analysis (backward stepwise variable selection procedure) by R software (version 3.6.1) with the survival and rms packages.

Calibration and Validation of the Nomogram

Predictive accuracy and discrimination performance were assessed by calibration curve (graphic representations of agreement between observed outcomes and predicted probabilities), concordance probability estimate (CPE) (values range from 0.5 to 1.0, with 0.5 indicating random chance and 1.0 indicating a perfect ability to correctly discriminate the outcome with the model and are reported with their standard error [SE]) and concordance index (Harrell’s C-index).(24) The model was subjected to external validation using cohorts V1 and V2 that were not used to develop the model. Bootstrapping method (1,000 repetitions), which is based on random sampling with replacement, was used to calculate the confidence intervals of C-index. We also compared the performance between our prognostic model and the Culine and Seve prognostic models.(17)

Results

Baseline Characteristics

Baseline characteristics of all cohorts (N = 926) are shown in Table 1. Median age of the entire study population was 63 years (range 18 – 92). Fifty-one percent were women, 64% had ECOG PS of 0 or 1 and 52% had histology consistent with adenocarcinoma. Nearly one-third of patients had three or more sites of metastatic involvement (30%) and a high NLR (≥ 5) (35%). Overall cohorts A and V1 were similar, while cohort V2 was different in terms of baseline characteristics. Cohort V2 population appeared to have a higher rate of patients with poor PS, high NLR and liver metastases. In these 926 patients, a total of 583 (63.0%) events (deaths) occurred over a median follow-up duration of 40.1 months. Median OS of entire cohort was 14.7 months (95%CI: 13.0 – 16.5) (Supplement Fig S2).

Table 1.

Baseline characteristics of development and internal validation cohort (A) and external validation cohorts (V1/V2)

Charateristic1	Cohort A (N = 521)		Cohort V1 (N = 103)		P value2	Cohort V2 (N = 302)		P value2	Overall (N = 926)
	N	%	N	%	V1 vs. A	N	%	V2 vs. A	N	%
Age (years)
Median (range)	60	18–90	65	31–88	0.022	67	22–92	< 0.0001	63	18–92
< 60	253	48.6	40	38.8	0.084	79	26.2	< 0.0001	372	40.2
≥ 60	268	51.4	63	61.2		223	73.8		554	59.8
Gender
Female	284	54.5	48	46.6	0.162	141	46.7	0.0240	473	51.1
Male	237	45.5	55	53.4		161	53.3		453	48.9
ECOG performance status
0	127	27.3	30	31.3	0.566	50	16.7	< 0.0001	207	24.0
1	202	43.4	44	45.8		99	33.0		345	40.1
2	90	19.4	13	13.5		67	22.3		170	19.8
≥ 3	46	9.9	9	9.4		84	28.0		139	16.1
Histology
Adenocarcinoma	291	55.9	63	61.2	0.738	125	41.4	< 0.0001	479	51.7
Non-Adenocarcinoma	230	44.1	40	38.8		177	58.6		447	48.3
Carcinoma	156	29.9	24	23.3		46	17.7		226	25.6
Malignant neoplasm	45	8.6	9	8.7		16	6.2		70	7.9
Squamous cell carcinoma	20	3.8	5	4.9		48	18.5		73	8.3
Other	9	1.7	2	1.9		25	9.6		36	4.1
Number of metastatic sites
< 3	388	74.5	81	78.6	0.454	177	58.6	< 0.0001	646	69.8
≥ 3	133	25.5	22	21.4		125	41.4		280	30.2
Liver metastasis
Absent	363	69.7	77	74.8	0.345	158	52.3	< 0.0001	598	64.6
Present	158	30.3	26	25.2		144	47.7		328	35.4
Neutrophil-lymphocyte ratio
Median (range)	3.4	0.2–48	3.6	0.1–23	0.330	4.1	0.3–48	0.0010	3.6	0.1–48
Low (< 5)	319	69.3	62	68.9	0.999	174	58.6	0.0029	555	65.5
High (≥ 5)	141	30.7	28	31.1		123	41.4		292	34.5

Abbreviations: ECOG, eastern cooperative oncology group; N, number of patients

¹Some variables have missing values. Proportions are derived from available data.

²Fisher’s exact test/Chi-squared test as appropriate.

Development of Nomogram

Univariate and multivariable analyses were performed in cohort A and are summarized in Table 2. Five prognostic factors were identified to be independently associated with OS: gender, ECOG PS, histology, number of metastatic sites and NLR. Being male, having a poor ECOG PS, an adenocarcinoma, a high number of metastatic sites and a higher NLR were associated with worse survival in the Cox model. ECOG PS and NLR were the strongest predictors of OS (P < 0.001). We constructed a nomogram (a graphic depiction of the model) based on these significant prognostic variables (Fig 1). On the nomogram, each variable is assigned a score on a point scale based on the rank order of the effect estimates. By adding them and then assessing the total score of all variables on “total points” scale, one can draw a straight line down to derive the estimated probability of survival at either 1 or 2 year (Fig 1).

Table 2.

Univariate and multivariable analysis of factors predicting overall survival (OS) in the development cohort (A) patients (N = 521)

Variable	Univariate Analysis			Multivariable Analysis1
	HR	95%CI	P	HR	95%CI	P
Age	1.01	1.00–1.02	0.070	-	-	-
Gender
Female vs. Male	0.68	0.55–0.85	< 0.001	0.74	0.57–0.95	0.020
ECOG Performance status
1 vs. 0	1.84	1.35–2.50	< 0.001	1.65	1.19–2.28	0.002
2 vs. 0	2.57	1.80–3.67	< 0.001	2.33	1.60–3.40	< 0.001
> 2 vs. 0	4.12	2.68–6.33	< 0.001	3.43	2.15–5.47	< 0.001
Histology
Adeno vs. Non-Adeno	1.37	1.10–1.71	0.010	1.40	1.08–1.80	0.010
Liver Metastases
Present vs. Absent	1.75	1.39–2.21	< 0.001	-	-	-
Number of sites of metastases
≥ 3 vs. < 3	1.79	1.41–2.28	< 0.001	1.47	1.12–1.94	0.010
Neutrophil-lymphocyte ratio (NLR)2
NLR (linear term)	1.38	1.22–1.55	< 0.001	1.24	1.09–1.40	< 0.001
NLR (cubic spline)	0.66	0.55–0.79	< 0.001	0.83	0.68–1.01	0.060

Abbreviations: ECOG, eastern cooperative oncology group; HR, hazard ratio; 95%CI, 95% confidence interval

¹Multivariable analysis was performed using backward stepwise variable selection procedure

²NLR was modeled as a continuous variable using restricted cubic spline.²³ Relationship between OS and NLR is also depicted in the nomogram (Fig 1). The probability of OS declines as value of NLR increases, and the decline is sharper as NLR value increases from 0 to 5.

Figure 1.

Nomogram to predict the probability of 1-year or 2-year overall survival in patients with cancer of unknown primary (CUP).

Predictor points are obtained from the points scale (top) according to the prognostic contribution of each variable subset. These, added together, give a total point score which can be translated into probability of survival at a specific time point (1-year or 2-year) by charting the score on total points scale (bottom) and projecting onto probability of survival scale. The survival estimates are given as probability of survival, for example, the 0.8 on the 1-year survival scale implies an 80% chance of survival at 1-year.

Abbreviations: ADCA, Adenocarcinoma; ECOG PS, Eastern Cooperative Oncology Group Performance Status; F, Female; M, Male; N-CA, Non-adenocarcinoma.

Internal and External Validation and Performance of Nomogram

The nomogram (our prognostic model) showed good performance characteristics in both the development cohort A and the validation cohorts. Discrimination assessed with CPE to predict OS was 0.69 (SE: ±0.01) in the internal cohort A. The CPE for the validation cohorts V1 and V2 were 0.67 (SE: ±0.02) and 0.70 (SE: ±0.01) respectively. The calibration curves for both 1-year and 2-year OS showed strong agreement between nomogram prediction and actual observation in both the development and validation cohorts (Fig 2: 2A–2B). In cohort A, the CPE for nomogram predictions was greater (0.69, SE: ±0.01) than the CPE for predictions based on Culine prognostic model (0.59, SE: ±0.01) and Seve prognostic model (0.59, SE: ±0.01). The corresponding Harrell’s C-index of the model for cohort A, V1 and V2 were 0.71 (95%CI: 0.68 – 0.74), 0.81 (95%CI: 0.74 – 0.87) and 0.76 (95%CI: 0.74 – 0.79), respectively. The C-index for nomogram predictions was greater than C-index for predictions based on Culine prognostic model (0.61, 95%CI: 0.58 – 0.64) and Seve prognostic model (0.61, 95%CI: 0.58 – 0.64) (Supplement Fig S3).

Figure 2.

Calibration plot of overall survival at 1 and 2 years for development and internal validation cohort (A) and external validation cohort (B).

Observed and predicted (as per our prognostic nomogram) survival is plotted on y-axis and x-axis, respectively. Means of survival predicted by our model was compared with the means of observed survival assessed by Kaplan-Meier estimates after grouping of equal sample sizes. The 45-degree line through the origin point represents perfect calibration model with identical actual and predicted survival outcome probabilities. Vertical arrows represent 95% confidence intervals for observed survival.

Discussion

“How long do I have, doctor?” is an important question for patients with CUP that clinicians often struggle to address. Patients, caregivers and providers, all value meaningful prognostic information and its importance in making a well-informed decision regarding treatment cannot be stressed enough.(6,7,25) However, reliable prognostication in CUP has been an inexact science and an unmet need.(1,3,4,26) Herein, using a large recent cohort of patients with CUP (N = 926) from three institutions, we have developed and validated, a simple tool for reliable prediction of survival at 1-year and 2-years in CUP. This nomogram uses readily available and objective baseline clinicopathological factors: gender, ECOG PS (0 or 1 or 2 or > 2), histology (non-adenocarcinoma or adenocarcinoma), number of sites of metastases (< 3 or ≥ 3) and NLR and provides patient-specific estimates of OS at diagnosis in CUP. Based on tertiles of nomogram total points, patients separated into low, intermediate and high nomogram risk-groups showed a median survival of 40.0, 15.1 and 4.1 months, respectively (P < 0.0001) (Supplement Fig S4).

Several groups have reported on CUP prognostic algorithms in the past using a few baseline variables. Culine and colleagues published a CUP prognostic model almost two decades ago to select patients for clinical trials.(17) It separated patient populations into good-risk (1-year OS: 45%) and poor-risk (1-year OS: 11%), using a classification scheme built on PS and LDH (or liver metastases) from a dataset of 150 CUP patients and a validation set of 130 patients. Similarly, Seve and colleagues published a model based on liver metastases and serum albumin, again separating patients into good-risk (1-year OS: 39%) and poor-risk (1-year OS: 12%).(18) Another CUP prognostic algorithm developed by Petrakis and colleagues classified patients in low-risk, intermediate-risk and high-risk groups with median survival of 36, 11–14 and 5–8 months, respectively.(19) Our nomogram, using a large sample size and diverse characteristics, offers major technical and functional improvements over these prior prognostic CUP models.(17,18) First, integrating multiple clinical and pathologic factors, as opposed to one or 2 factors in prior models, increases the accuracy and robustness of the nomogram and accommodates relative contribution of multiple prognostic factors.(16) Second, the data elements required at baseline are objective and universally available during routine work-up of CUP patients. We purposely excluded variables such as LDH or serum albumin, used in prior models, since they are often not available, their cutoffs are prone to inter-laboratory variations and they can be non-specifically influenced by cancer-unrelated factors such as liver functions, age and hydration. In fact, LDH and albumin were missing in 38–46% and 27% cases in the reference populations used for prior studies.(17,18) Likewise, in our development cohort, LDH was missing in 27% cases and albumin in 15% cases. Finally, the categorical nature of prior systems forces the transformation of continuous variables into uncompromising bracketed outcomes, good or bad, limiting predictive accuracy and extent which is overcome by a continuous nature of prediction by the nomogram.

Use of pretreatment NLR is a unique and important attribute of this model. NLR acts as a potential surrogate biomarker of tumor microenvironment, immune-milieu and systemic inflammatory state of malignancy and has been recognized as a prognostic factor for several tumors.(27–29) NLR has also been shown to have impact on prognosis in CUP patients.(30,31) It can be easily derived from a complete blood count with differential done as a part of routine initial CUP management. Furthermore, it is a ratio and therefore not affected by testing site or methodology. This makes it an ideal and objective prognostic parameter that does not add expense or resource utilization. In fact, all elements on the nomogram can be easily gathered at diagnosis without any added effort.

The fact that the source population for creating the nomogram was derived from a single referral institution may be seen as a limitation. Notably, the median OS of our cohort is higher when compared to published population-based reports of CUP. However, we believe that this selection ensured development based on a large and vigorous dataset which has consistent availability of detailed data elements.(20) This is necessary in light of the complexity of CUP diagnosis in general. In the development cohort, CUP diagnosis was rigorously evaluated by an experienced team and the primary outcome and prognostic variables to be included in the nomogram were defined a priori adding support to its performance. Importantly, we validated the model in multicenter cohorts, adding to the generalizability of the nomogram. Notably, even with differing baseline patient characteristics and outcomes between MDACC and external (BC Cancer and SCCC/TO) cohorts, which may reflect referral bias and varying treatment patterns, the nomogram performed exceptionally well in these validation cohorts. In a population-based cohort like the BC Cancer cohort, which comprised of consecutively diagnosed patients from an entire province in Canada, we had optimal prognostic discrimination (CPE: 0.70 ± 0.02). However, further efforts are however required to investigate the performance of the nomogram in a more diverse population (e.g., outside of North American population).

The merits discussed above not only make the nomogram distinctive but also enhance its performance. Our analysis using the asymptotically unbiased CPE showed good discriminatory power of the model in all cohorts (CPE of 0.69, 0.67 and 0.70 in cohorts A, V1 and V2, respectively) and performance superior to predictions based on the currently validated prognostic Culine (CPE: 0.59) and Seve (CPE: 0.59) prognostic models in the discovery cohort.(24) The commonly used Harrel’s C-indices of the nomogram were correspondingly 0.71, 0.81 and 0.76 in cohorts A, V1 and V2, respectively. This performance estimate was also superior to predictions based on Culine (0.61) and Seve (0.61) prognostic models in the discovery cohort (Supplement Fig S3). We also investigated the ability of the nomogram to dissect the heterogeneity of outcome within the Culine prognostic groups and other clinical and pathological subsets in CUP (Fig 3). Figure 3 shows distribution of nomogram predicted probabilities within each of these groups (Culine good- and poor-risk group, patients with > 1 site of metastases, presence of liver metastases, lymph node only presentation and immunophenotype (CK20+, CDX2+) consistent with a lower GI profile) and clearly the nomogram demonstrates the variations in predicted outcomes within these categories of CUP. For example, about 8% of all patients, that would be classified as poor-risk in the Culine model and deemed to have a 1-year OS of 45%, can have a 1-year OS predicted probability of 80–90% distinguishing patients that may do significantly better than others plausibly due to favorable response to therapy despite poor PS, high LDH or liver metastases.

Figure 3.

Histogram of nomogram-predicted overall survival (OS) in various clinicopathological settings: culine prognostic groups, patent with > 1 site of metastases, presence of liver metastases, lymph node only disease and immunohistochemistry consistent with a lower gastrointestinal (GI) profile [CK20+, CDX2+].

The heterogeneity within each clinicopathological setting is apparent by the range of predicted probabilities of OS as calculated using the nomogram. The Y axis stands for number of patients (frequency) and X axis stands for OS probability at 1 year, which ranges from 0 to 1 and was divided by 0.1 (10%). For example, the third bin (*) in culine poor group represents that there were 18 patients with OS probability from 20% to 30%.

A large body of evidence has established that frank discussions regarding prognosis in advanced cancer results in realistic patient expectations and enhance emotional well-being of patients and doctor-patient relationship.(32–34) However, studies have shown that clinical prediction of survival by healthcare professionals is inaccurate with at best ‘moderate’ agreement between predicted and actual patient survival. In most cases, no group accurately predicted the length of patient survival more than 50% of the time.(26,35) Prognostic uncertainty in CUP is greater compared to other cancers and causes apprehension patients and providers.(2–4) This nomogram can lessen this uncertainty and improve patients and provider understanding regarding CUP prognosis. However, it should be recognized that prognosis in any advanced cancer is a dynamic phenomenon. In this era, increasing use of molecular diagnostics and targeted/immune therapies can alter the course of any disease and make prognostic accuracy a moving target and a challenge.(36–39) Any prognostic model, including this nomogram, has to evolve over time to account for these changes. Early evidence suggests that certain genomic alterations (such as KRAS mutations and CDKN2A deletions) may be prognostic and others are druggable using targeted agents that are highly effective (such as NTRK fusions). However, the lack of universal availability of molecular profiling for CUP and validation of these biomarkers, limits current inclusion in a clinic ready nomogram.(37,40) Additionally, due to low prevalence of these biomarkers and limited access to testing and therapy in CUP, we believe that the nomogram will play a critical role in management of a substantial subset of patients with CUP. Ongoing efforts will be needed to study continued performance and refinement as understanding of molecular biology improves and as more therapies become available for CUP patients. Integration into prospective trials for risk-stratification will be key to understanding this impact.

In summary, this novel CUP survival nomogram is a user-friendly tool comprised of readily available baseline objective data elements that allows robust estimates for survival in CUP patients, overcoming the epistemic uncertainty of prognostication in this disease. The nomogram is publicly accessible for use in a user-friendly web-based application at (https://cupnomogram.shinyapps.io/Nomogram/) (Supplement Fig S5). Besides facilitating a meaningful dialogue for optimizing routine clinical management of CUP patients, the ability to generate individualized predictions, enables its use in the identification and stratification of CUP patients for clinical trials. While, communicating prognosis will always remain a multifaceted and arduous undertaking, we believe this nomogram will substantially lessen the fear and ambiguity that accompanies a diagnosis of CUP for both our patients and our providers.

Statement of Translational Relevance.

Cancer of unknown primary (CUP) is a rare and challenging clinical diagnosis, fraught with uncertainties for both patients and oncologists. Although realistic conversations about prognosis can aid informed decision-making regarding patient care, models that can actually provide reliable and individualized estimates of survival for patients with CUP are lacking. Traditional risk stratification models pool patients at either good or poor risk which limit a meaningful patient-provider dialogue. Using this large multicenter cohort of 926 patients with CUP, we developed and validated a robust prognostic model and nomogram to predict overall survival with superior performance (concordance probability estimate of 0.69 and concordance index of 0.71) compared to traditional prognostic classifiers. This model uses universally available baseline factors to ensure feasibility of use in diverse clinical settings and is available as a web-based application. Personalized prognostication with this CUP nomogram can not only aid informed clinical decisions but also trial selection/stratification.