AI and Cancer Research ── Prostate ── 2026-10-03
Prostate cancer: treatment, trials and AI
- Abstract
Skeletal muscle is a major determinant of functional status. The impact of advanced prostate cancer (aPC) systemic therapies on body composition is unknown. We identified aPC patients with serial PET-CTs receiving androgen deprivation therapy (ADT), androgen receptor pathway inhibitors (ARPI), chemotherapy, and/or Lutetium-177. An artificial intelligence algorithm quantified body composition changes from PET-CT images. Linear mixed effects models examined body composition changes over time by treatment type. The algorithm examined 2,342 PET-CTs from 468 patients. On multivariable analysis, ADT was associated with a 9% reduction in muscle density (-1.91 Hounsfield units), ARPIs an additional 6% (-1.4 Hounsfield units), and Lutetium-177 a subsequent 6% (-1.3 Hounsfield units), demonstrating progressive reduction in muscle density across later stages of systemic therapy. This large longitudinal analysis of body composition in aPC patients supports the use of an automated PET-CT algorithm to identify aPC patients on systemic therapy at risk for muscle wasting.
Journal IF-equivalent: 4.5 (OpenAlex 2-year mean citedness, value as of 2026-10-02, retrieved 2026-10-03; not the official Clarivate IF)Reference: Shi C, Wei N, Martinez GM, O’Byrne J, Weston A, Grossardt B, et al. An artificial intelligence algorithm characterizes the deterioration of body composition from systemic therapy for patients with advanced prostate cancer. JNCI: Journal of the National Cancer Institute. 2026 Oct 1 [Epub ahead of print]. doi:10.1093/jnci/djag349.Checked: Abstract only - Abstract
A multimodal artificial intelligence biomarker using prostate tissue images and clinical data predicted long-term outcomes in postprostatectomy patients receiving salvage radiotherapy. The model stratified risk and may help personalize hormone therapy decisions after biochemical recurrence.
Journal IF-equivalent: 1.5 (OpenAlex 2-year mean citedness, value as of 2026-10-02, retrieved 2026-10-03; not the official Clarivate IF)Reference: Morgan TM, Ren Y, Tang S, Zwerink W, Chen E, Mitani A, et al. Development and Validation of a Multimodal Artificial Intelligence–derived Digital Pathology–based Biomarker Predicting Metastasis Among Patients with Biochemical Recurrence After Radical Prostatectomy in NRG/RTOG Trials. Eur Urol. 2025 Dec [Epub ahead of print]. doi:10.1016/j.eururo.2025.12.007. PMID: 41436315; PMCID: PMC12774449.Checked: Abstract only