Mechanistic Learning for Predicting Survival Outcomes in Head and Neck Squamous Cell Carcinoma.

We employed a mechanistic learning approach, integrating on-treatment tumor kinetics (TK) modeling with various machine learning (ML) models to address the challenge of predicting post-progression survival (PPS)-the duration from the time of documented disease progression to death-and overall survival (OS) in Head and Neck Squamous Cell Carcinoma (HNSCC). We compared the predictive power of model-derived TK parameters versus RECIST and assessed the efficacy of nine TK-OS ML models against conventional survival models. Data from 526 advanced HNSCC patients treated with chemotherapy and cetuximab in the TPExtreme trial were analyzed using a double-exponential model.

Lack of Prophylactic Cranial Irradiation for Extensive Small-Cell Lung Cancer in Real Life, with the Emergence of Immunotherapy.

Background: Prophylactic cranial irradiation (PCI) is recommended to decrease the incidence of brain metastases (BM) in extensive-stage small-cell lung cancer (ESSCLC) without BM after response to chemotherapy. However, PCI is associated with significant neurocognitive effects, and new studies are debating its benefits. Moreover, the introduction of immunotherapy in the management of the disease has raised new questions, and there is a lack of data on PCI and immunotherapy.

Temporal Trends in Patient Characteristics and Clinical Outcomes of TAVR: Over a Decade of Practice.

Transcatheter aortic valve replacement (TAVR) is indicated for severe aortic stenosis patients with a prohibitive surgical risk. However, its use has been expanding in recent years to include intermediate- and low-risk patients. Thus, registry data describing changes in patient characteristics and outcomes are needed.

Predicting Survival in Patients with Advanced NSCLC Treated with Atezolizumab Using Pre- and on-Treatment Prognostic Biomarkers.

Existing survival prediction models rely only on baseline or tumor kinetics data and lack machine learning integration. We introduce a novel kinetics-machine learning (kML) model that integrates baseline markers, tumor kinetics, and four on-treatment simple blood markers (albumin, C-reactive protein, lactate dehydrogenase, and neutrophils). Developed for immune-checkpoint inhibition (ICI) in non-small cell lung cancer on three phase II trials (533 patients), kML was validated on the two arms of a phase III trial (ICI and chemotherapy, 377 and 354 patients).