Synchronous BALT Lymphoma and Squamous Cell Carcinoma of the Lung: Coincidence or Linkage?

A 72-year-old man presented with weight loss, fever, and malaise. Chest radiograph and CT revealed two large ill-defined masses in middle and left lower lobes. CT-guided biopsy of left lower lobe mass disclosed bronchus-associated lymphoid tissue (BALT) lymphoma. Middle lobe mass was considered second deposit in contralateral lung. The patient received chemotherapy for BALT. Followup CT disclosed regression of left lower lobe mass and stability of middle-lobe mass and of right paratracheal lymph nodes. CT-guided biopsy of middle-lobe mass revealed squamous cell lung carcinoma. Surgical biopsy of right paratracheal lymph nodes revealed malignancy. Disease was staged T3, N2, and M0. Combined chemotherapy for lung cancer and BALT lymphoma was initiated

An Explainable Machine Learning Pipeline for Stroke Prediction on Imbalanced Data

Stroke is an acute neurological dysfunction attributed to a focal injury of the central nervous system due to reduced blood flow to the brain. Nowadays, stroke is a global threat associated with premature death and huge economic consequences. Hence, there is an urgency to model the effect of several risk factors on stroke occurrence, and artificial intelligence (AI) seems to be the appropriate tool. In the present study, we aimed to (i) develop reliable machine learning (ML) prediction models for stroke disease; (ii) cope with a typical severe class imbalance problem, which is posed due to the stroke patients’ class being significantly smaller than the healthy class; and (iii) interpret the model output for understanding the decision-making mechanism. The effectiveness of the proposed ML approach was investigated in a comparative analysis with six well-known classifiers with respect to metrics that are related to both generalization capability and prediction accuracy. The best overall false-negative rate was achieved by the Multi-Layer Perceptron (MLP) classifier (18.60%). Shapley Additive Explanations (SHAP) were employed to investigate the impact of the risk factors on the prediction output. The proposed AI method could lead to the creation of advanced and effective risk stratification strategies for each stroke patient, which would allow for timely diagnosis and the right treatments