CASCADE: Context-Aware Data-Driven AI for Streamlined Multidisciplinary Tumor Board Recommendations in Oncology.

This study addresses the potential of machine learning in predicting treatment recommendations for patients with hepatocellular carcinoma (HCC). Using an IRB-approved retrospective study of patients discussed at a multidisciplinary tumor board, clinical and imaging variables were extracted and used in a gradient-boosting machine learning algorithm, XGBoost. The algorithm's performance was assessed using confusion matrix metrics and the area under the Receiver Operating Characteristics (ROC) curve. The study included 140 patients (mean age 67.7 ± 8.9 years), and the algorithm was found to be predictive of all eight treatment recommendations made by the board. The model's predictions were more accurate than those based on published therapeutic guidelines by ESMO and NCCN. The study concludes that a machine learning model incorporating clinical and imaging variables can predict treatment recommendations made by an expert multidisciplinary tumor board, potentially aiding clinical decision-making in settings lacking subspecialty expertise.

Disparities in access to endovenous treatment options in chronic lower extremity superficial venous insufficiency: A national 7-year analysis.

OBJECTIVE: The goal of this study was to analyze trends in treatment access for chronic superficial venous disease and to identify disparities in care. METHODS: This retrospective study was exempt from institutional review board approval. The American College of Surgeon National Surgical Quality Improvement Program database was used to identify patients who underwent vein stripping (VS) and endovenous procedures for treatment of chronic superficial venous disease. Endovenous options included radiofrequency ablation (RFA) and laser ablation. Data was available from 2011 to 2018 and demographic information was extracted for each patient identified by Current Procedural Terminology codes. For all racial and ethnic groups, trend lines were plotted, and the relative rate of change was determined within each specified demographic. RESULTS: There were 21,025 patients included in the analysis. The overall mean age was 54.2 years, and the majority of patients were female (64.8%). In total, 27.9%, 55.2%, and 16.9% patients underwent VS, RFA, and laser ablation, respectively. Patients who received laser ablation were older (P < .001). Hispanic ethnicity was associated with significantly lower odds of receiving endovascular thermal ablation (EVTA) over VS (odds ratio [OR], 0.71; 95% confidence interval [CI], 0.64-0.78; P < .001). American Indian/Alaska Native patients were more likely to receive EVTA over VS (OR, 4.02; 95% CI, 2.48-6.86); similarly, Native Hawaiian/Pacific Islander patients were more likely to receive EVTA over VS, although this difference was not statistically significant (OR, 1.44; 95% CI, 0.93-2.27). On multinomial regression, Hispanic patients were less likely to receive RFA over VS, whereas American Indian/Alaskan Native patients were more likely to receive RFA over VS. In all racial and ethnic groups, the percentage of endovenous procedures increased, whereas vein stripping decreased. CONCLUSIONS: Based on a hospital-based dataset, demographic indicators, including age, sex, race, and ethnicity, are associated with differences in endovenous treatments for chronic superficial venous insufficiency suggesting disparities in obtaining minimally invasive treatment options among certain patient groups.

Machine Learning Improves the Prediction of Responses to Immune Checkpoint Inhibitors in Metastatic Melanoma.

Pretreatment LDH is a standard prognostic biomarker for advanced melanoma and is associated with response to ICI. We assessed the role of machine learning-based radiomics in predicting responses to ICI and in complementing LDH for prognostication of metastatic melanoma. From 2008-2022, 79 patients with 168 metastatic hepatic lesions were identified. All patients had arterial phase CT images 1-month prior to initiation of ICI. Response to ICI was assessed on follow-up CT at 3 months using RECIST criteria. A machine learning algorithm was developed using radiomics. Maximum relevance minimum redundancy (mRMR) was used to select features. ROC analysis and logistic regression analyses evaluated performance. Shapley additive explanations were used to identify the variables that are the most important in predicting a response. mRMR selection revealed 15 features that are associated with a response to ICI. The machine learning model combining both radiomics features and pretreatment LDH resulted in better performance for response prediction compared to models that included radiomics or LDH alone (AUC of 0.89 (95% CI: [0.76-0.99]) vs. 0.81 (95% CI: [0.65-0.94]) and 0.81 (95% CI: [0.72-0.91]), respectively). Using SHAP analysis, LDH and two GLSZM were the most predictive of the outcome. Pre-treatment CT radiomic features performed equally well to serum LDH in predicting treatment response.

An interpretable machine learning model for the prevention of contrast-induced nephropathy in patients undergoing lower extremity endovascular interventions for peripheral arterial disease.

BACKGROUND: Contrast-induced nephropathy (CIN) is a postprocedural complication associated with increased morbidity and mortality. An important risk factor for development of CIN is renal impairment. Identification of patients at risk for acute renal failure will allow physicians to make appropriate decisions to minimize the incidence of CIN. We developed a machine learning model to stratify risk of acute renal failure that may assist in mitigating risk for CIN in patients with peripheral artery disease (PAD) undergoing endovascular interventions. METHODS: We utilized the American College of Surgeons National Surgical Quality Improvement Program database to extract clinical and laboratory information associated with 14,444 patients who underwent lower extremity endovascular procedures between 2011 and 2018. Using 11,604 cases from 2011 to 2017 for training and 2840 cases from 2018 for testing, we developed a random forest model to predict risk of 30-day acute renal failure following infra-inguinal endovascular procedures. RESULTS: Eight variables were identified as contributing optimally to model predictions, the most important being diabetes, preoperative BUN, and claudication. Using these variables, the model achieved an area under the receiver-operating characteristic (AU-ROC) curve of 0.81, accuracy of 0.83, sensitivity of 0.67, and specificity of 0.74. The model performed equally well on white and nonwhite patients (Delong p-value = 0.955) and patients age < 65 and patients age ≥ 65 (Delong p-value = 0.659). CONCLUSIONS: We develop a model that fairly and accurately stratifies 30-day acute renal failure risk in patients undergoing lower extremity endovascular procedures for PAD. This model may assist in identifying patients who may benefit from strategies to prevent CIN.

Machine Learning-Based Radiomic Features on Pre-Ablation MRI as Predictors of Pathologic Response in Patients with Hepatocellular Carcinoma Who Underwent Hepatic Transplant.

BACKGROUND: The aim was to investigate the role of pre-ablation tumor radiomics in predicting pathologic treatment response in patients with early-stage hepatocellular carcinoma (HCC) who underwent liver transplant. METHODS: Using data collected from 2005-2015, we included adult patients who (1) had a contrast-enhanced MRI within 3 months prior to ablation therapy and (2) underwent liver transplantation. Demographics were obtained for each patient. The treated hepatic tumor volume was manually segmented on the arterial phase T1 MRI images. A vector with 112 radiomic features (shape, first-order, and texture) was extracted from each tumor. Feature selection was employed through minimum redundancy and maximum relevance using a training set. A random forest model was developed based on top radiomic and demographic features. Model performance was evaluated by ROC analysis. SHAP plots were constructed in order to visualize feature importance in model predictions. RESULTS: Ninety-seven patients (117 tumors, 31 (32%) microwave ablation, 66 (68%) radiofrequency ablation) were included. The mean model for end-stage liver disease (MELD) score was 10.5 ± 3. The mean follow-up time was 336.2 ± 179 days. Complete response on pathology review was achieved in 62% of patients at the time of transplant. Incomplete pathologic response was associated with four features: two first-order and two GLRM features using univariate logistic regression analysis (p < 0.05). The random forest model included two radiomic features (diagnostics maximum and first-order maximum) and four clinical features (pre-procedure creatinine, pre-procedure albumin, age, and gender) achieving an AUC of 0.83, a sensitivity of 82%, a specificity of 67%, a PPV of 69%, and an NPV of 80%. CONCLUSIONS: Pre-ablation MRI radiomics could act as a valuable imaging biomarker for the prediction of tumor pathologic response in patients with HCC.

Risk stratification with explainable machine learning for 30-day procedure-related mortality and 30-day unplanned readmission in patients with peripheral arterial disease.

Predicting 30-day procedure-related mortality risk and 30-day unplanned readmission in patients undergoing lower extremity endovascular interventions for peripheral artery disease (PAD) may assist in improving patient outcomes. Risk prediction of 30-day mortality can help clinicians identify treatment plans to reduce the risk of death, and prediction of 30-day unplanned readmission may improve outcomes by identifying patients who may benefit from readmission prevention strategies. The goal of this study is to develop machine learning models to stratify risk of 30-day procedure-related mortality and 30-day unplanned readmission in patients undergoing lower extremity infra-inguinal endovascular interventions. We used a cohort of 14,444 cases from the American College of Surgeons National Surgical Quality Improvement Program database. For each outcome, we developed and evaluated multiple machine learning models, including Support Vector Machines, Multilayer Perceptrons, and Gradient Boosting Machines, and selected a random forest as the best-performing model for both outcomes. Our 30-day procedure-related mortality model achieved an AUC of 0.75 (95% CI: 0.71-0.79) and our 30-day unplanned readmission model achieved an AUC of 0.68 (95% CI: 0.67-0.71). Stratification of the test set by race (white and non-white), sex (male and female), and age (≥65 years and <65 years) and subsequent evaluation of demographic parity by AUC shows that both models perform equally well across race, sex, and age groups. We interpret the model globally and locally using Gini impurity and SHapley Additive exPlanations (SHAP). Using the top five predictors for death and mortality, we demonstrate differences in survival for subgroups stratified by these predictors, which underscores the utility of our model.

Interpretable Machine Learning for the Prediction of Amputation Risk Following Lower Extremity Infrainguinal Endovascular Interventions for Peripheral Arterial Disease.

PURPOSE: Severe peripheral artery disease (PAD) may result in lower extremity amputation or require multiple procedures to achieve limb salvage. Current prediction models for major amputation risk have had limited performance at the individual level. We developed an interpretable machine learning model that will allow clinicians to identify patients at risk of amputation and optimize treatment decisions for PAD patients. METHODS: We utilized the American College of Surgeons National Surgical Quality Improvement Program database to collect preoperative clinical and laboratory information on 14,444 patients who underwent lower extremity endovascular procedures for PAD from 2011 to 2018. Using data from 2011 to 2017 for training and data from 2018 for testing, we developed a machine learning model to predict 30 day amputation in this patient population. We present performance metrics overall and stratified by race, sex, and age. We also demonstrate model interpretability using Gini importance and SHapley Additive exPlanations. RESULTS: A random forest machine learning model achieved an area under the receiver-operator curve (AU-ROC) of 0.81. The most important features of the model were elective surgery designation, claudication, open wound/wound infection, white blood cell count, and albumin. The model performed equally well on white and non-white patients (Delong p-value = 0.189), males and females (Delong p-value = 0.572), and patients under age 65 and patients age 65 and older (Delong p-value = 0.704). CONCLUSION: We present a machine learning model that predicts 30 day major amputation events in PAD patients undergoing lower extremity endovascular procedures. This model can optimize clinical decision-making for patients with PAD.

A man with an umbilicated papule of the hand: what is your diagnosis?

Introduction. Ecthyma contagiosum is a zoonotic disease caused by the parapoxvirus that causes "sore mouth" in sheep and goats and orf in human. Case Presentation. A 61-year-old sheep farmer presented with a painful non-pruritic lesion on the left hand that had been present for approximately 5 weeks. Physical examination demonstrated a 1 cm pearly, umbilicated papule with raised borders. A biopsy showed an asymmetrical nodule with parakeratotic crust and acanthosis with thin epidermal strands extending deeply in the underlying dermis. Marked edema, capillary proliferation and extensive lymphocytic infiltration was also present. One red intranuclear inclusion was identified in an epidermal keratinocyte. A diagnosis of human orf (ecthyma contagiosum) was made. Conclusion. Infected sheep and freshly vaccinated sheep or goats are the reservoir for human infection. After an incubation period of 3-7 days, parapoxvirus infections produce 1-3 painful lesions measuring 1-2 cm in diameter. The natural history of the disease is complete resolution and no treatment is indicated. Prevention of echthyma contagiosum in ruminants through vaccination is thought to be the best way to control infection.

An unusual clinical presentation of merkel cell carcinoma: a case report.

Introduction. Merkel cell carcinoma is a rare, aggressive neuroendocrine cell carcinoma arising in the sun-exposed skin of elderly patients. Most of these tumors are located in the dermis. An unusual clinical presentation of such a tumor in the subcutis, if not biopsied, may be easily mistaken as a benign lesion. Case Presentation. An 83-year-old white woman presented with a several-month history of a painless 7 mm subcutaneous mass that was initially thought to be a lipoma. A conservative follow-up was planned. At the insistence of the patient, an excisional biopsy of the mass was performed revealing a subcutaneous Merkel cell carcinoma. The tumor cells stained positively for CK 20, chromogranin, and synaptophysin. No other primary or metastatic tumors found after a thorough work-up. The patient was treated with local irradiation. She remains disease free at her six-month follow-up visit. Conclusion. When a new growth is encountered in the sun-exposed skin of elderly patients, a biopsy is warranted even if the lesion clinically appears benign.