Faculty Bibliography

BACKGROUND:Access to prostate MRI remains limited due to resource constraints and the need for expert interpretation. PURPOSE/OBJECTIVE:To develop machine learning (ML) models that enable risk-based triage for prostate MRI (ProMT-ML) in the evaluation of prostate cancer. STUDY TYPE/METHODS:Retrospective and prospective. POPULATION/METHODS:A total of 11,879 retrospective MRI scans for suspected prostate cancer from a multi-hospital health system, divided into training (N = 9504) and test (N = 2375) sets. A total of 4551 records for prospective validation. FIELD STRENGTH/SEQUENCE/UNASSIGNED:1.5T and 3T/Turbo-spin echo T2-weighted imaging (T2WI), diffusion-weighted imaging (DWI), and dynamic contrast-enhanced (DCE). ASSESSMENT/RESULTS:Prostate Imaging Reporting and Data System (PI-RADS) scores were retrieved from MRI reports. The Boruta algorithm was used to select final input features from candidate features. Two models were developed using supervised ML to estimate the likelihood of an abnormal MRI, defined as PI-RADS ≥ 3: Model A (with prostate volume) and Model B (without prostate volume). Models were compared to PSA. Prostate biopsy pathology was assessed to evaluate potential clinical impact. STATISTICAL TESTS/METHODS:Area under the receiver operating characteristic curve (AUC) was the primary performance metric. RESULTS:A total of 5580 (46.9%) subjects had a PI-RADS score ≥ 3. After feature selection, Model A included age, PSA, body mass index, and prostate volume, while Model B included age, PSA, body mass index, and systolic blood pressure. Both models A (AUC 0.711) and B (AUC 0.616) significantly outperformed PSA (AUC 0.593). Compared to PSA threshold > 4 ng/mL, Model A demonstrated significantly improved specificity (28.3% vs. 21.9%) and no significant difference in sensitivity (89.0% vs. 86.7%). Among false negatives (Model A: 8.0% (62/776); Model B: 16.8% (130/776)), most (Model A: 87%; Model B: 69%) had benign or clinically insignificant disease on biopsy. On prospective validation, both versions of ProMT-ML significantly outperformed PSA. DATA CONCLUSION/CONCLUSIONS:ProMT-ML provides personalized risk estimates of abnormal prostate MRI and can support triage of this test. TECHNICAL EFFICACY/UNASSIGNED:Stage 4.

PURPOSE/OBJECTIVE:Partial nephrectomy has been advocated as the preferred surgical approach for small kidney tumors over total nephrectomy. However, partial nephrectomy is associated with increased perioperative risk. Estimating renal function after nephrectomy can facilitate personalized patient counseling, guide surgical approach, and identify patients who could benefit from perioperative interventions. Existing prediction models have several limitations including the lack of external validation or a user-friendly tool or application, and most have used traditional statistical methods. METHODS:We used data from two academic medical institutions and machine learning (ML) methods to develop and externally validate renal function after nephrectomy-machine learning (RFAN-ML), a model to estimate long-term renal function after partial or total nephrectomy. Boruta feature selection was used to select four routinely available clinical features, specifically age, BMI, preoperative renal function, and nephrectomy type. In the training set of 1,932 patients, we compared six ML regression models representing a set of both ensemble and nonensemble ML algorithms and optimized for root mean squared error (RMSE). This model was evaluated in a test set of 1,995 patients, and the best performing model was selected as RFAN-ML. RESULTS:, and mean absolute error. CONCLUSION/CONCLUSIONS:We developed and externally validated RFAN-ML, a ML model to predict renal function after nephrectomy, and have deployed our model online. RFAN-ML has the potential to improve the care and outcomes in patients with kidney tumors by informing personalized patient counseling and guiding surgical planning.

PURPOSE/OBJECTIVE:While tobacco use has been associated with postoperative surgical complications, its specific impact on postoperative outcomes following partial nephrectomy remains incompletely understood. Therefore, we aim to investigate the association between tobacco use and the incidence and severity of 90-day postoperative complications in patients who underwent partial nephrectomy. MATERIALS AND METHODS/METHODS:A cross-sectional study was conducted using the Premier Healthcare Database (2010-2019). Our primary outcome was the occurrence of 90-day postoperative complications, classified according to the Dindo-Clavien system. Multinomial logistic regression, adjusted for confounders, estimated the association between tobacco use and the risk of postoperative complications, including 90-day mortality. Predicted probabilities of postoperative complications were calculated from the regression analysis. RESULTS:The study included 44,392 patients (median age 56.3; 66.1% males) who underwent elective partial nephrectomy. Among them, 16,048 (36.15%) were tobacco users. Tobacco users were generally younger, less frequently married, predominantly of white ethnicity, and had elevated Charlson Comorbidity Index scores as compared to nonusers. Multinomial logistic regression revealed that tobacco use was significantly associated with increased odds of 90-day postoperative minor complications (OR 1.19, 95% CI 1.14-1.24), major complications (OR 1.41, 95% CI 1.31-1.52), and mortality (OR 1.5, 95% CI 1.01-2.24). CONCLUSIONS:In our cohort, tobacco use significantly increased the risk of minor and major postoperative complications, as well as mortality, in patients undergoing partial nephrectomy. These findings emphasize the importance of tailored preoperative smoking cessation programs to mitigate these risks.

The primary practice of healthcare artificial intelligence (AI) starts with model development, often using state-of-the-art AI, retrospectively evaluated using metrics lifted from the AI literature like AUROC and DICE score. However, good performance on these metrics may not translate to improved clinical outcomes. Instead, we argue for a better development pipeline constructed by working backward from the end goal of positively impacting clinically relevant outcomes using AI, leading to considerations of causality in model development and validation, and subsequently a better development pipeline. Healthcare AI should be "actionable," and the change in actions induced by AI should improve outcomes. Quantifying the effect of changes in actions on outcomes is causal inference. The development, evaluation, and validation of healthcare AI should therefore account for the causal effect of intervening with the AI on clinically relevant outcomes. Using a causal lens, we make recommendations for key stakeholders at various stages of the healthcare AI pipeline. Our recommendations aim to increase the positive impact of AI on clinical outcomes.

BACKGROUND/UNASSIGNED:Accurate prediction of postoperative metastasis and mortality risks in patients undergoing lower-limb oncological resection and endoprosthetic reconstruction is essential for guiding adjuvant therapies and managing patient expectations. Current prediction methods are limited by variability in patient-specific factors. This study aims to develop and internally validate explainable machine learning (ML) models to predict the 1-year risk of new distant metastases and mortality in these patients. METHODS/UNASSIGNED:We performed a secondary analysis of data from the Prophylactic Antibiotic Regimens in Tumor Surgery trial, which included 604 patients. Candidate features were selected based on availability and clinical relevance and then narrowed using Least Absolute Shrinkage and Selection Operator (LASSO) regression and Boruta algorithms. Six ML classification algorithms were tuned and calibrated: logistic regression, support vector machines, random forest, Light Gradient Boosting Machine (LightGBM), eXtreme Gradient Boosting (XGBoost), and neural networks. Models were developed with and without including percent tumor necrosis due to its high missing data rate (>30%). Hyperparameters were tuned using Bayesian optimization. Internal validation was conducted using a 30% hold-out set. Model explainability was assessed using permutation-based feature importance and SHapley Additive exPlanations. RESULTS/UNASSIGNED:LightGBM was identified as the best-performing algorithm for both outcomes. For 1-year mortality prediction without percent necrosis, LightGBM achieved an area under the receiver operating characteristic curve (AUC-ROC) of 0.78 (95% confidence interval [CI] 0.70-0.86) during cross-validation and 0.72 on internal validation. For distant metastasis prediction, the LightGBM model without percent necrosis achieved an AUC-ROC of 0.77 (95% CI 0.71-0.84) during cross-validation and 0.77 on internal validation. Including percent necrosis did not significantly improve model performance. The top predictors identified were patient age, largest tumor dimension, and tumor stage. CONCLUSIONS/UNASSIGNED:Explainable ML models can effectively predict the 1-year risk of mortality and new distant metastases in patients undergoing lower-limb oncological resection and endoprosthetic reconstruction. Further external validation and consideration of other data modalities are required before integrating these ML-driven risk assessments into routine clinical practice. LEVEL OF EVIDENCE/UNASSIGNED:Level II, Prognostic Study. See Instructions for Authors for a complete description of levels of evidence.

BACKGROUND:Prediction models based on machine learning (ML) methods are being increasingly developed and adopted in health care. However, these models may be prone to bias and considered unfair if they demonstrate variable performance in population subgroups. An unfair model is of particular concern in bladder cancer, where disparities have been identified in sex and racial subgroups. OBJECTIVE:This study aims (1) to develop a ML model to predict survival after radical cystectomy for bladder cancer and evaluate for potential model bias in sex and racial subgroups; and (2) to compare algorithm unfairness mitigation techniques to improve model fairness. METHODS:-score. The primary metric for model fairness was the equalized odds ratio (eOR). We compared 3 algorithm unfairness mitigation techniques to improve eOR. RESULTS:-scores of 0.86, 0.904, and 0.824 in the full, Black male, and Asian female test sets, respectively. CONCLUSIONS:The ML model predicting survival after radical cystectomy exhibited bias across sex and racial subgroups. By using algorithm unfairness mitigation techniques, we improved algorithmic fairness as measured by the eOR. Our study highlights the role of not only evaluating for model bias but also actively mitigating such disparities to ensure equitable health care delivery. We also deployed the first web-based fair ML model for predicting survival after radical cystectomy.

OBJECTIVE:To characterize differences in the management of small renal masses among disaggregated race/ethnic subgroups. MATERIAL AND METHODS/METHODS:We used the National Cancer Database to identify patients diagnosed with clinically localized kidney cancer and tumor size ≤4cm. We studied 16 predefined racial/ethnic subgroups and compared 1) the use of surveillance for tumors <2cm and 2) the use of radical nephrectomy for tumors ≤4cm. We used multivariable logistic regression to evaluate the independent association of race/ethnicity with management, adjusting for baseline characteristics. We also compared our disaggregated analyses to the 6 National Institute of Health aggregate race categories. RESULTS:We identified 286,063 patients that met inclusion criteria. For tumors <2cm, Black Non-Hispanic (aOR 1.43) and Mexican patients (aOR 1.29) were significantly more likely to undergo surveillance compared to White patients. For tumors ≤4cm, Black Non-Hispanic (aOR 1.43), Filipino (aOR 1.28), Japanese (aOR 1.28), Mexican (aOR 1.32), and Native Indian patients (aOR 1.15) were significantly more likely to undergo radical nephrectomy compared to White patients. When comparing our disaggregated analyses to the NIH categories, we found that many disaggregated race/ethnic subgroups had associations with management strategies that were not represented by their aggregated group. CONCLUSIONS:In this study, we found that the use of surveillance for tumors <2cm and radical nephrectomy for tumors ≤4cm varied significantly among certain race/ethnic subgroups. Our disaggregated approach provides information on differences in treatment patterns in particular subgroups that warrant further study to optimize kidney cancer care for all patients.

PURPOSE/OBJECTIVE:To determine whether variance in kidney cancer surgery quality indicators (QIs) is most impacted by surgeon-level or hospital-level factors in order to inform quality improvement initiatives. MATERIALS AND METHODS/METHODS:The ICES and Veterans Affairs (VA) databases were queried for patients undergoing surgery for localized kidney cancer. Kidney cancer surgery QIs were defined within each cohort. Quality of care was benchmarked at a surgeon- vs. hospital-level to identify statistical outliers, using available clinicopathological data to adjust for differences in case-mix. Variance between surgeons and hospitals was calculated for each QI using a random-effects model. RESULTS:The QI with the greatest amount of variance explained by hospital and surgeon-level factors was proportion of cases performed with minimally invasive surgery (MIS). The majority of this variance was due to surgeon-level factors for both the VA and ICES cohorts. The proportion of cases performed using an MIS approach was also the QI with the greatest number of outlier hospitals and surgeons compared to the average performance. The proportion of partial nephrectomies performed for patients at risk of chronic kidney disease was the QI with the greatest amount of variance due to hospital-level factors for the ICES cohort. CONCLUSIONS:The proportion of localized kidney cancer cases performed using an MIS approach is the QI requiring the greatest attention. Quality improvement initiatives should focus on surgeon-level factors to increase the number of MIS cases being performed for patients with localized renal masses.

PURPOSE:The impact of anemia in postoperative complications following radical cystectomy (RC) is not completely elucidated and its association with direct hospital costs has not been characterized in depth. Our goal is to determine the association between anemia, 90-day surgical complications and the expenditure attributed to preoperative anemia in patients undergoing RC. MATERIALS AND METHODS:We captured all patients who underwent RC between 2003 and 2017 using the Premier Hospital Database (Premier Inc, Charlotte, NC). Patient, hospital and surgical characteristics were evaluated. Anemia was defined by a corresponding diagnostic code that was present on admission prior to RC. Unadjusted patients' demographic characteristics with and without anemia, hospital and surgeon characteristics were compared, and multivariable regression models were developed to evaluate 90-day complications and total direct hospital costs. RESULTS:The cohort included 83,470 patients that underwent RC between 2003 and 2017 and 11% were found to be anemic. On multivariable analysis, preoperative anemia more than doubled the odds of having a complication (odds ratio 2.19 (1.89-2.53)) and significantly increased the risk of major complications (odds ratio 1.51 (1.31-1.75)) at 90-days after RC. Anemic patients had significantly higher 90-days total direct costs due to higher laboratory, pharmacologic, radiology and operating room costs. CONCLUSIONS:Anemic cystectomy patients face a 50% increase in the risk of major complications within the first 90-days after surgery. This increased risk persisted after adjusting for patient, hospital and surgical factors. Our study suggests hematocrit level prior to RC may be used as a pre-exisitng condition for increased risk of surgical complications.

PURPOSE:Robust prediction of progression on active surveillance (AS) for prostate cancer can allow for risk-adapted protocols. To date, models predicting progression on AS have invariably used traditional statistical approaches. We sought to evaluate whether a machine learning (ML) approach could improve prediction of progression on AS. PATIENTS AND METHODS:We performed a retrospective cohort study of patients diagnosed with very-low or low-risk prostate cancer between 1997 and 2016 and managed with AS at our institution. In the training set, we trained a traditional logistic regression (T-LR) classifier, and alternate ML classifiers (support vector machine, random forest, a fully connected artificial neural network, and ML-LR) to predict grade-progression. We evaluated model performance in the test set. The primary performance metric was the F1 score. RESULTS:Our cohort included 790 patients. With a median follow-up of 6.29 years, 234 developed grade-progression. In descending order, the F1 scores were: support vector machine 0.586 (95% CI 0.579 - 0.591), ML-LR 0.522 (95% CI 0.513 - 0.526), artificial neural network 0.392 (95% CI 0.379 - 0.396), random forest 0.376 (95% CI 0.364 - 0.380), and T-LR 0.182 (95% CI 0.151 - 0.185). All alternate ML models had a significantly higher F1 score than the T-LR model (all p <0.001). CONCLUSION:In our study, ML methods significantly outperformed T-LR in predicting progression on AS for prostate cancer. While our specific models require further validation, we anticipate that a ML approach will help produce robust prediction models that will facilitate individualized risk-stratification in prostate cancer AS.