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2025Journal ArticleElsevier1 citation

Stratifying Prostate Cancer: A Comprehensive Framework for Staging and Treatment Planning Classification

Avishek Rauniyar, Bowrampet Manish Reddy, Remya S Dr.

Procedia Computer Science, Vol. 259, pp. 356–365

Overview

Abstract

Prostate cancer remains one of the most prevalent cancers globally, with treatment outcomes heavily dependent on accurate staging and personalized treatment planning. This paper presents a comprehensive deep learning framework for stratifying prostate cancer patients across multiple clinical dimensions — Gleason grading, TNM staging, and treatment pathway classification. The framework integrates histopathological imaging features with clinical biomarkers to produce multi-level risk assessments. Published in Procedia Computer Science Volume 259, pages 356–365.

Keywords
Medical AIHealthcare AIDeep LearningCancer DiagnosisProstate CancerTreatment PlanningClinical Decision Support
Research Areas
Medical AIHealthcare AIDeep LearningCancer Diagnosis

Technical Details

Technologies Used
PythonPyTorchMedical ImagingDeep LearningClinical Data Processing
Datasets
  • Clinical prostate cancer dataset
Models
  • CNN
  • Multi-modal fusion network

My Contribution

Role

Lead author — framework design, model development, dataset curation, evaluation, manuscript preparation.

Details

Lead author. Developed the comprehensive classification framework for prostate cancer staging and treatment planning. Designed the deep learning architecture, curated and preprocessed clinical datasets, and conducted all evaluation experiments. Led manuscript writing.

Challenges

Limited availability of annotated clinical data for rare cancer subtypes
Balancing model interpretability with predictive performance for clinical acceptance
Integrating heterogeneous data sources (imaging + biomarkers + clinical notes)

Key Contributions

Multi-dimensional prostate cancer stratification framework
Integration of histopathological and clinical biomarker data for comprehensive staging
Clinically interpretable risk assessment outputs aligned with TNM staging standards

Impact

Provides a reproducible, AI-assisted framework for prostate cancer staging that supports clinical decision-making and treatment planning standardization.

Lessons Learned

Clinical AI requires explainability at every decision point — clinicians need to understand why a classification was made
Multi-modal fusion (imaging + tabular clinical data) consistently outperforms single-modality approaches
Dataset curation is 70% of the work in medical AI; model architecture is 30%

Citation

APA

Rauniyar, A., Reddy, B. M., & S, R. (2025). Stratifying prostate cancer: A comprehensive framework for staging and treatment planning classification. Procedia Computer Science, 259, 356–365. Elsevier. https://doi.org/10.1016/j.procs.2025.03.337

IEEE

A. Rauniyar, B. M. Reddy, and R. S, "Stratifying Prostate Cancer: A Comprehensive Framework for Staging and Treatment Planning Classification," Procedia Computer Science, vol. 259, pp. 356–365, Elsevier, 2025.

MLA

Rauniyar, Avishek, et al. "Stratifying Prostate Cancer: A Comprehensive Framework for Staging and Treatment Planning Classification." Procedia Computer Science, vol. 259, Elsevier, 2025, pp. 356-365.

Chicago

Rauniyar, Avishek, Bowrampet Manish Reddy, and Remya S. "Stratifying Prostate Cancer: A Comprehensive Framework for Staging and Treatment Planning Classification." Procedia Computer Science 259 (2025): 356–365. Elsevier. https://doi.org/10.1016/j.procs.2025.03.337.

BibTeX
@article{rauniyar2025prostate,
  title     = {Stratifying Prostate Cancer: A Comprehensive Framework for Staging and Treatment Planning Classification},
  author    = {Rauniyar, Avishek and Reddy, Bowrampet Manish and S, Remya},
  journal   = {Procedia Computer Science},
  volume    = {259},
  pages     = {356--365},
  publisher = {Elsevier},
  year      = {2025},
  doi       = {10.1016/j.procs.2025.03.337}
}