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Unanswered questions remain in determining which high-risk node-negative colon cancer (CC) cohorts benefit from adjuvant therapy and how it may differ in an equal access population. Machine-learned Bayesian Belief Networks (ml-BBNs) accurately estimate outcomes in CC, providing clinicians with Clinical Decision Support System (CDSS) tools to facilitate treatment planning. We evaluated ml-BBNs ability to estimate survival and recurrence in CC. We performed a retrospective analysis of registry data of patients with CC to train–test–crossvalidate ml-BBNs using the Department of Defense Automated Central Tumor Registry (January 1993 to December 2004). Cases with events or follow-up that passed quality control were stratified into 1-, 2-, 3-, and 5-year survival cohorts. ml-BBNs were trained using machine-learning algorithms and k-fold crossvalidation and receiver operating characteristic curve analysis used for validation. BBNs were comprised of 5301 patients and areas under the curve ranged from 0.85 to 0.90. Positive predictive values for recurrence and mortality ranged from 78 to 84 per cent and negative predictive values from 74 to 90 per cent by survival cohort. In the 12-month model alone, 1,132,462,080 unique rule sets allow physicians to predict individual recurrence/mortality estimates. Patients with Stage II (N0M0) CC benefit from chemotherapy at different rates. At one year, all patients older than 73 years of age with T2–4 tumors and abnormal carcinoembryonic antigen levels benefited, whereas at five years, all had relative reduction in mortality with the largest benefit amongst elderly, highest T-stage patients. ml-BBN can readily predict which high-risk patients benefit from adjuvant therapy. CDSS tools yield individualized, clinically relevant estimates of outcomes to assist clinicians in treatment planning.

Colorectal cancer (CRC) is the third most common cause of cancer-related death in the United States (U.S.), with estimates of 143,460 new cases and 51,690 deaths for the year 2012. Numerous organizations have published guidelines for CRC screening; however, these numerical estimates of incidence and disease-specific mortality have remained stable from years prior. Technological, genetic profiling, molecular and surgical advances in our modern era should allow us to improve risk stratification of patients with CRC and identify those who may benefit from preventive measures, early aggressive treatment, alternative treatment strategies, and/or frequent surveillance for the early detection of disease recurrence. To better negotiate future economic constraints and enhance patient outcomes, ultimately, we propose to apply the principals of personalized and precise cancer care to risk-stratify patients for CRC screening (Precision Risk Stratification-Based Screening, PRSBS). We believe that genetic, molecular, ethnic and socioeconomic disparities impact oncological outcomes in general, those related to CRC, in particular. This document highlights evidence-based screening recommendations and risk stratification methods in response to our CRC working group private-public consensus meeting held in March 2012. Our aim was to address how we could improve CRC risk stratification-based screening, and to provide a vision for the future to achieving superior survival rates for patients diagnosed with CRC.

Malignant peritoneal mesothelioma (MPM) is a rare disease treated with cytoreductive surgery (CRS) and hyperthermic intraperitoneal chemotherapy (HIPEC). Estimation of personalized survival times can potentially guide treatment and surveillance.We analyzed 104 patients who underwent CRS and cisplatin-based HIPEC for MPM. By means of 25 demographic, laboratory, operative, and histopathological variables, we developed a novel nomogram using machine-learned Bayesian belief networks with stepwise training, testing, and cross-validation.The mean peritoneal carcinomatosis index (PCI) was 15, and 66 % of patients had a completeness of cytoreduction (CC) score of 0 or 1. Eighty-seven percent of patients had epithelioid histology. The median follow-up time was 49 (1-195) months. The 3- and 5-year overall survivals (OS) were 58 and 46 %, respectively. The histological subtype, pre-CRS PCI, and preoperative serum CA-125 had the greatest impact on OS and were included in the nomogram. The mean areas under the receiver operating characteristic curve for the 10-fold cross-validation of the 3- and 5-year models were 0.77 and 0.74, respectively. The graphical calculator or nomogram uses color coding to assist the clinician in quickly estimating individualized patient-specific survival before surgery.Machine-learned Bayesian belief network analysis generated a novel nomogram predicting 3- and 5-year OS in patients treated with CRS and HIPEC for MPM. Pre-CRS estimation of survival times may potentially individualize patient care by influencing the use of systemic therapy and frequency of diagnostic imaging, and might prevent CRS in patients unlikely to achieve favorable outcomes despite surgical intervention.

A need exists for a breast cancer risk identification paradigm that utilizes relevant demographic, clinical, and other readily obtainable patient-specific data in order to provide individualized cancer risk assessment, direct screening efforts, and detect breast cancer at an early disease stage in historically underserved populations, such as younger women (under age 40) and minority populations, who represent a disproportionate number of military beneficiaries. Recognizing this unique need for military beneficiaries, a consensus panel was convened by the USA TATRC to review available evidence for individualized breast cancer risk assessment and screening in young (< 40), ethnically diverse women with an overall goal of improving care for military beneficiaries. In the process of review and discussion, it was determined to publish our findings as the panel believes that our recommendations have the potential to reduce health disparities in risk assessment, health promotion, disease prevention, and early cancer detection within and in other underserved populations outside of the military. This paper aims to provide clinicians with an overview of the clinical factors, evidence and recommendations that are being used to advance risk assessment and screening for breast cancer in the military.

The presence and number of nodal metastasis significantly impact colon cancer prognosis. Similarly, the number of resected/evaluated nodes impacts staging accuracy. This ratio of metastatic to examined nodes or lymph node ratio (LNR) may have independent prognostic value in colon carcinoma.: To evaluate the impact of LNR on overall survival in colon cancer patients with fewer than 12 or 12 examined nodes or more.Patients (n = 36,712) with node-positive nonmetastatic colon cancer diagnosed between 1992 and 2004 were identified from the Surveillance, Epidemiology, and End Results database and stratified according to LNR and number of nodes examined. Survival was estimated by Kaplan-Meier method, and differences analyzed by log-rank test. A Cox proportional hazards model was used for multivariate analysis.Patients with fewer than 12 nodes were older and male and had lower primary tumor stage, grade, and N stage (P0.01). Survival appeared greater with 12 total nodes examined or more (median 53 vs. 66 months, P0.001). Within each LNR stratum, survival with 12 nodes or more was improved for those with less than 10% of nodes positive for cancer, but was worse with higher LNRs (P0.01). Lymph node ratio was significantly associated with survival independent of total nodes (HR 1.24-5.12, P0.001). Other significant factors included age, race, tumor grade, stage, location, and N stage.Metastatic LNR independently estimates survival in Stage III colon cancer, irrespective of number of nodes examined. However, statistically significant differences in each LNR stratum between those with resection of fewer than 12 or 12 nodes or more would indicate that a 12-node minimum may still be necessary for accurate staging.

Transplant glomerulopathy (TG) is associated with rapid decline in glomerular filtration rate and poor outcome. We used low-density arrays with a novel probabilistic analysis to characterize relationships between gene transcripts and the development of TG in allograft recipients. Retrospective review identified TG in 10.8% of 963 core biopsies from 166 patients; patients with stable function were studied for comparison. The biopsies were analyzed for expression of 87 genes related to immune function and fibrosis by using real-time PCR, and a Bayesian model was generated and validated to predict histopathology based on gene expression. A total of 57 individual genes were increased in TG compared with stable function biopsies (P < 0.05). The Bayesian analysis identified critical relationships between ICAM-1, IL-10, CCL3, CD86, VCAM-1, MMP-9, MMP-7, and LAMC2 and allograft pathology. Moreover, Bayesian models predicted TG when derived from either immune function (area under the curve [95% confidence interval] of 0.875 [0.675 to 0.999], P = 0.004) or fibrosis (area under the curve [95% confidence interval] of 0.859 [0.754 to 0.963], P < 0.001) gene networks. Critical pathways in the Bayesian models were also analyzed by using the Fisher exact test and had P values

The Combat Wound Initiative (CWI) program is a collaborative, multidisciplinary, and interservice public-private partnership that provides personalized, state-of-the-art, and complex wound care via targeted clinical and translational research. The CWI uses a bench-to-bedside approach to translational research, including the rapid development of a human extracorporeal shock wave therapy (ESWT) study in complex wounds after establishing the potential efficacy, biologic mechanisms, and safety of this treatment modality in a murine model. Additional clinical trials include the prospective use of clinical data, serum and wound biomarkers, and wound gene expression profiles to predict wound healing/failure and additional clinical patient outcomes following combat-related trauma. These clinical research data are analyzed using machine-based learning algorithms to develop predictive treatment models to guide clinical decision-making. Future CWI directions include additional clinical trials and study centers and the refinement and deployment of our genetically driven, personalized medicine initiative to provide patient-specific care across multiple medical disciplines, with an emphasis on combat casualty care.

Background: Improvement in staging accuracy is the principal aim of targeted nodal assessment in colorectal carcinoma. Technical factors independently predictive of false negative (FN) sentinel lymph node (SLN) mapping should be identified to facilitate operative decision making. Purpose: To define independent predictors of FN SLN mapping and to develop a predictive model that could support surgical decisions. Patients and Methods: Data was analyzed from 2 completed prospective clinical trials involving 278 patients with colorectal carcinoma undergoing SLN mapping. Clinical outcome of interest was FN SLN(s), defined as one(s) with no apparent tumor cells in the presence of non-SLN metastases. To assess the independent predictive effect of a covariate for a nominal response (FN SLN), a logistic regression model was constructed and parameters estimated using maximum likelihood. A probabilistic Bayesian model was also trained and cross validated using 10-fold train-and-test sets to predict FN SLN mapping. Area under the curve (AUC) from receiver operating characteristics curves of these predictions was calculated to determine the predictive value of the model. Results: Number of SLNs (

Return on investment (ROI) concerns related to Electronic Health Records (EHRs) are a major barrier to the technology's adoption. Physicians generally rely upon early adopters to vet new technologies prior to putting them into widespread use. Therefore, early adopters' experiences with EHRs play a major role in determining future adoption patterns. The paper's purposes are: (1) to map the EHR value streams that define the ROI calculation; and (2) to compare Current Users' and Intended Adopters' perceived value streams to identify similarities, differences and governing constructs. Primary data was collected by the Texas Medical Association, which surveyed 1,772 physicians on their use and perceptions of practice gains from EHR adoption. Using Bayesian Belief Network Modeling, value streams are constructed for both current EHR users and Intended Adopters. Current Users and Intended Adopters differ significantly in their perceptions of the EHR value stream. Intended Adopters' value stream displays complex relationships among the potential gains compared to the simpler, linear relationship that Current Users identified. The Current Users identify "Reduced Medical Records Costs" as the gain that governs the value stream while Intended Adopters believe "Reduced Charge Capture Costs" define the value stream's starting point. Current Users' versus Intended Adopters' assessments of EHR benefits differ significantly and qualitatively from one another.

The article presents the results of research on the adoption of electronic health record (EHR) systems, focusing on a cost-benefit analysis from the standpoint of physician practice. It is noted thta return on investment for EHR features direct costs and inderect savings for physicians, which has led to slow adoption rates. An overview of the research protocol, which used Bayesian Belief Network graphs to model the responses from a survey of physicians in Texas. It was found that existing users tended to emphasize the improved workflows which EHR provided, whereas those who had not yet adopted EHR thought of it primarily in terms of cost savings.

BackgroundClostridium difficile (C-Diff) infection following colorectal resection is an increasing source of morbidity and mortality. ObjectiveWe sought to determine if machine-learned Bayesian belief networks (ml-BBNs) could preoperatively provide clinicians with postoperative estimates of C-Diff risk. MethodsWe performed a retrospective modeling of the Nationwide Inpatient Sample (NIS) national registry dataset with independent set validation. The NIS registries for 2005 and 2006 were used for initial model training, and the data from 2007 were used for testing and validation. International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM) codes were used to identify subjects undergoing colon resection and postoperative C-Diff development. The ml-BBNs were trained using a stepwise process. Receiver operating characteristic (ROC) curve analysis was conducted and area under the curve (AUC), positive predictive value (PPV), and negative predictive value (NPV) were calculated. ResultsFrom over 24 million admissions, 170,363 undergoing colon resection met the inclusion criteria. Overall, 1.7% developed postoperative C-Diff. Using the ml-BBN to estimate C-Diff risk, model AUC is 0.75. Using only known a priori features, AUC is 0.74. The model has two configurations: a high sensitivity and a high specificity configuration. Sensitivity, specificity, PPV, and NPV are 81.0%, 50.1%, 2.6%, and 99.4% for high sensitivity and 55.4%, 81.3%, 3.5%, and 99.1% for high specificity. C-Diff has 4 first-degree associates that influence the probability of C-Diff development: weight loss, tumor metastases, inflammation/infections, and disease severity. ConclusionsMachine-learned BBNs can produce robust estimates of postoperative C-Diff infection, allowing clinicians to identify high-risk patients and potentially implement measures to reduce its incidence or morbidity.

A need exists for a breast cancer risk identification paradigm that utilizes relevant demographic, clinical, and other readily obtainable patient-specific data in order to provide individualized cancer risk assessment, direct screening efforts, and detect breast cancer at an early disease stage in historically underserved populations, such as younger women (under age 40) and minority populations, who represent a disproportionate number of military beneficiaries. Recognizing this unique need for military beneficiaries, a consensus panel was convened by the USA TATRC to review available evidence for individualized breast cancer risk assessment and screening in young (< 40), ethnically diverse women with an overall goal of improving care for military beneficiaries. In the process of review and discussion, it was determined to publish our findings as the panel believes that our recommendations have the potential to reduce health disparities in risk assessment, health promotion, disease prevention, and early cancer detection within and in other underserved populations outside of the military. This paper aims to provide clinicians with an overview of the clinical factors, evidence and recommendations that are being used to advance risk assessment and screening for breast cancer in the military.

BACKGROUND predictive models permitting individualized prognostication for patients with fracture nonunion are lacking. The objective of this study was to train, test, and cross-validate a Bayesian classifier for predicting fracture-nonunion healing in a population treated with extracorporeal shock wave therapy. METHODS prospectively collected data from 349 patients with delayed fracture union or a nonunion were utilized to develop a naive Bayesian belief network model to estimate site-specific fracture-nonunion healing in patients treated with extracorporeal shock wave therapy. Receiver operating characteristic curve analysis and tenfold cross-validation of the model were used to determine the clinical utility of the approach. RESULTS predictors of fracture-healing at six months following shock wave treatment were the time between the fracture and the first shock wave treatment, the time between the fracture and the surgery, intramedullary stabilization, the number of bone-grafting procedures, the number of extracorporeal shock wave therapy treatments, work-related injury, and the bone involved (p < 0.05 for all comparisons). These variables were all included in the naive Bayesian belief network model. CONCLUSIONS a clinically relevant Bayesian classifier was developed to predict the outcome after extracorporeal shock wave therapy for fracture nonunions. The time to treatment and the anatomic site of the fracture nonunion significantly impacted healing outcomes. Although this study population was restricted to patients treated with shock wave therapy, Bayesian-derived predictive models may be developed for application to other fracture populations at risk for nonunion. LEVEL OF EVIDENCE prognostic Level II. See Instructions to Authors for a complete description of levels of evidence.

Background: Lifetime risk assessment tools are relatively limited in identifying breast cancer risk in younger women. The predictive value of mathematical models to estimate risk varies according to age, menopausal status, race/ethnicity, and family history. Current risk prediction models estimate population, not individual, levels of breast cancer risk; hence, individualized risk prediction models are needed to identify younger at-risk women who could benefit from timely risk reduction interventions. Clinical data collected as part of breast cancer screening studies may be modeled using Bayesian classification. Purpose: To train a proof-of-concept Bayesian classifier for breast cancer risk stratification. Patients and Methods: We trained a Bayesian belief network (BBN) model on cohort data (including risk factors, demographic, electrical impedance scanning (EIS), breast imaging, and biopsy data) from a prospective pilot screening trial in younger women (N = 591). Receiver operating characteristic curve analysis and cross-validation of the model were used to derive preliminary guidance on the robustness of this approach and to gain insights into what a cross-validation exercise could provide in terms of risk stratification in a larger population. Results: Independent predictors of biopsy outcome in the BBN model included personal breast disease history, breast size, EIS (low vs high risk) and imaging results, and Gail cutoff (5-year risk

Objective To review cutting-edge, novel, implemented and potential translational research and to provide a glimpse into rich, innovative, and brilliant approaches to everyday surgical problems. Data Sources Scientific literature and unpublished results. Study Selection Articles reviewed were chosen based on innovation and application to surgical diseases. Data Extraction Each section was written by a surgeon familiar with cutting-edge and novel research in their field of expertise and interest. Data Synthesis Articles that met criteria were summarized in the manuscript. Conclusions Multiple avenues have been used for the discovery of improved means of diagnosis, treatment, and overall management of patients with surgical diseases. These avenues have incorporated the use of genomics, electrical impedence, statistical and mathematical modeling, and immunology.

Background Thyroid nodules represent a common problem brought to medical attention. Four to seven percent of the United States adult population (10–18 million people) has a palpable thyroid nodule, however the majority (>95%) of thyroid nodules are benign. While, fine needle aspiration remains the most cost effective and accurate diagnostic tool for thyroid nodules in current practice, over 20% of patients undergoing FNA of a thyroid nodule have indeterminate cytology (follicular neoplasm) with associated malignancy risk prevalence of 20–30%. These patients require thyroid lobectomy/isthmusectomy purely for the purpose of attaining a definitive diagnosis. Given that the majority (70–80%) of these patients have benign surgical pathology, thyroidectomy in these patients is conducted principally with diagnostic intent. Clinical models predictive of malignancy risk are needed to support treatment decisions in patients with thyroid nodules in order to reduce morbidity associated with unnecessary diagnostic surgery. Methods Data were analyzed from a completed prospective cohort trial conducted over a 4-year period involving 216 patients with thyroid nodules undergoing ultrasound (US), electrical impedance scanning (EIS) and fine needle aspiration cytology (FNA) prior to thyroidectomy. A Bayesian model was designed to predict malignancy in thyroid nodules based on multivariate dependence relationships between independent covariates. Ten-fold cross-validation was performed to estimate classifier error wherein the data set was randomized into ten separate and unique train and test sets consisting of a training set (90% of records) and a test set (10% of records). A receiver-operating-characteristics (ROC) curve of these predictions and area under the curve (AUC) were calculated to determine model robustness for predicting malignancy in thyroid nodules. Results Thyroid nodule size, FNA cytology, US and EIS characteristics were highly predictive of malignancy. Cross validation of the model created with Bayesian Network Analysis effectively predicted malignancy [AUC = 0.88 (95%CI: 0.82–0.94)] in thyroid nodules. The positive and negative predictive values of the model are 83% (95%CI: 76%–91%) and 79% (95%CI: 72%–86%), respectively. Conclusion An integrated predictive decision model using Bayesian inference incorporating readily obtainable thyroid nodule measures is clinically relevant, as it effectively predicts malignancy in thyroid nodules. This model warrants further validation testing in prospective clinical trials.

9089 Background: Estimating the risk of recurrence in patients with melanoma is extremely challenging. Standard of care is AJCC staging system, but the accuracy and robustness of this method is still under development. We conducted a proof of concept study exploring the use of machine-learned Bayesian Belief Networks (ml-BBNs) using a miRNA profiled cohort of melanoma patients with extended follow up to create Bayesian Biological Systems Models (BBSMs). We sought to determine if ml-BBNs could describe the biological system and if we could use the model to identify new cases with higher risk of recurrence. Methods: Our study cohort consisted of 89 patients (42 of which recurred) with a median follow up time of 118 months, that were examined for 869 miRNAs. Prior to modeling we segmented the data into training data (72 cases/80%) and testing data (17 cases/20%) at random. We recursively trained ml-BBNs on the training set, using all miRNAs. We used the directed graph structure of the ml-BBNs to identify miRNAs that consistently had more connectivity and goodness of fit as determined by Bayesian Information Criteria (BIC) scoring. MiRNAs that were in the top 50 BIC-scoring nodes across all models were selected for use to train the recurrence BBSMs. To compensate for a small number, bootstrapping was used to increase the sample to 100 records. We then compared our test set cases to our recurrence model, and used a similarity scoring algorithm to evaluate the similarity of values in each test instance to our biological models. For comparison we also trained an ml-BBN using clinical data from the same cohort. We then evaluated the scores against known recurrence outcome using Receiver Operating Characteristic (ROC) curve analysis. Results: BIC-scoring analysis selected 35 miRNAs for use in BBSM modeling. Area Under the Curve (AUC) for detection of recurrence is 0.76 in the training set and 0.62 in the testing set, while the clinical data yielded an AUC of 0.5 in this cohort. Conclusions: Our data suggest that ml-BBNs can be used to describe a biological model of melanoma recurrence. Additional data and refinement need to be made using independent datasets to be of a level that is clinically useful.

Background Accurate estimations of life expectancy are important in the management of patients with metastatic cancer affecting the extremities, and help set patient, family, and physician expectations. Clinically, the decision whether to operate on patients with skeletal metastases, as well as the choice of surgical procedure, are predicated on an individual patient's estimated survival. Currently, there are no reliable methods for estimating survival in this patient population. Bayesian classification, which includes Bayesian belief network (BBN) modeling, is a statistical method that explores conditional, probabilistic relationships between variables to estimate the likelihood of an outcome using observed data. Thus, BBN models are being used with increasing frequency in a variety of diagnoses to codify complex clinical data into prognostic models. The purpose of this study was to determine the feasibility of developing Bayesian classifiers to estimate survival in patients undergoing surgery for metastases of the axial and appendicular skeleton. Methods We searched an institution-owned patient management database for all patients who underwent surgery for skeletal metastases between 1999 and 2003. We then developed and trained a machine-learned BBN model to estimate survival in months using candidate features based on historical data. Ten-fold cross-validation and receiver operating characteristic (ROC) curve analysis were performed to evaluate the BNN model's accuracy and robustness. Results A total of 189 consecutive patients were included. First-degree predictors of survival differed between the 3-month and 12-month models. Following cross validation, the area under the ROC curve was 0.85 (95% CI: 0.80–0.93) for 3-month probability of survival and 0.83 (95% CI: 0.77–0.90) for 12-month probability of survival. Conclusions A robust, accurate, probabilistic naive BBN model was successfully developed using observed clinical data to estimate individualized survival in patients with operable skeletal metastases. This method warrants further development and must be externally validated in other patient populations.

e14500 Background: Unanswered questions remain regarding treatment efficacy in colon cancer (CC), especially those determining high-risk node-negative cohorts that may benefit from adjuvant therapy. We sought to evaluate the use of machine learning and classification modeling to estimate survival and recurrence in CC. Methods: We used the Department of Defense Automated Central Tumor Registry (ACTUR) to identify primary CC patients treated between January 1993 and December 2004. Cases with events or follow-up that passed quality control were stratified into one-, two-, three-, and five-year survival cohorts. ml-BBNs were trained using machine-learning algorithms and k-fold cross-validation, and receiver operating characteristic (ROC) curve analysis used for validation. Results: There were 5,301 cases stratified into cohorts. Survival cohort Areas-Under-the-Curve (AUCs) ranged from 0.85–0.90, positive-predictive-values (PPVs) for recurrence and mortality ranged from 78-84% and negative-predictive-values (NPVs) from 74-90%. Cross-validation showed that the ml-BBNs produce robust individual estimates of recurrence (p

Multimodality therapy in selected patients with peritoneal carcinomatosis is gaining acceptance. Treatment-directing decision support tools are needed to individualize care and select patients best suited for cytoreductive surgery ± hyperthermic intraperitoneal chemotherapy (CRS ± HIPEC). The purpose of this study is to develop a predictive model that could support surgical decisions in patients with colon carcinomatosis. Fifty-three patients were enrolled in a prospective study collecting 31 clinical-pathological, treatment-related, and outcome data. The population was characterized by disease presentation, performance status, extent of peritoneal cancer (Peritoneal Cancer Index, PCI), primary tumor histology, and nodal staging. These preoperative parameters were analyzed using step-wise machine-learned Bayesian Belief Networks (BBN) to develop a predictive model for overall survival (OS) in patients considered for CRS ± HIPEC. Area-under-the-curve from receiver-operating-characteristics curves of OS predictions was calculated to determine the model's positive and negative predictive value. Model structure defined three predictors of OS: severity of symptoms (performance status), PCI, and ability to undergo CRS ± HIPEC. Patients with PCI < 10, resectable disease, and excellent performance status who underwent CRS ± HIPEC had 89 per cent probability of survival compared with 4 per cent for those with poor performance status, PCI > 20, who were not considered surgical candidates. Cross validation of the BBN model robustly classified OS (area-under-the-curve = 0.71). The model's positive predictive value and negative predictive value are 63.3 per cent and 68.3 per cent, respectively. This exploratory study supports the utility of Bayesian classification for developing decision support tools, which assess case-specific relative risk for a given patient for oncological outcomes based on clinically relevant classifiers of survival. Further prospective studies to validate the BBN model-derived prognostic assessment tool are warranted.