Farhood Farahnak

Worldwide Innovative Network Consortium: Building a Common Global Cancer Database

Farhood Farahnak

Wafik S. El‐Deiry

Yves A. Lussier

Razelle Kurzrock

Shai Magidi

Catherine Bresson

Shirin Abbasinejad Enger

Jia Liu

Jair Bar

Jeremy Lyle Warner

Tobias Meißner

Eitan Rubin

Jens Rueter

Himabindu Gaddipati

Mandar Kulkarni

Zhen Chen

Sewanti Atul Limaye

Rachel J. Elsey

Brenda Marilyn Rubenstein

Anthony Michael Joshua … (voir 30 de plus)

Humaid Obaid Al-Shamsi

Khaled M. Musallam

Fanny Wunder

Jacques Raynaud

Guy Berchem

Manon Gantenbein

Amal Al‐Omari

Said Dermime

Hikmat Abdel‐Razeq

Pierre Saintigny

Andrès M.R. Cervantes

Roger R. Reddel

Adel T. Aref

Juan Martín-Liberal

Conxi Lázaro

David Cordero

Marina Sekacheva

Raanan Berger

Conjeevaram S. Pramesh

Ioana Berindan‐Neagoe

Eugenia Girda

Alejandro Piris‐Giménez

Carol J. Farhangfar

Mohamed E. Salem

Rodrigo Dienstmann

Ramón Salazar

Naftali Z. Frankel

Zachary Batist

Yuri Quintana

Gerald Batist

This review shares the ongoing work of the global Worldwide Innovative Network (WIN) Consortium for Precision Medicine to synthesize emergin… (voir plus)g cancer treatment data and to define the requirements for a common global cancer database that can truly support precision oncology. We performed a narrative review of emerging cancer treatment data, molecular profiling technologies, and existing clinicogenomic databases, focusing on how tumors are characterized, how subgroups are defined, and how demographic, lifestyle, and environmental factors are captured. The growth in molecular profiling technologies and the development of new targeted therapies are transforming cancer care. Tumors, regardless of tissue origin, are increasingly defined as composites of multiple, often rare, subgroups, each with distinct biology and likely response to specific therapies, based on multidimensional profiling of the tumor and its microenvironment. The solution lies in building vast databases that capture racial and ethnic diversity, reflected in genomic data, as well as diet and lifestyle factors that may have epigenetic impact on gene expression and post-translational modifications. A truly inclusive and informative data set must reflect global diversity, and there are multiple examples of demography-dependent differences in genomic signals. With members caring for and studying patients with cancer across five continents, WIN is actively exploring pathways to create a global cancer database, rich in clinical and molecular detail, granular enough for precise analysis, and large enough to power artificial intelligence-driven insights, provided appropriate data quality, validation, and governance frameworks are in place. This review surveys the current landscape and outlines practical paths forward to achieve this goal.

2026-08-12

JCO Global Oncology (publié)

doi.org

Prognostic data extraction harnessing a privacy-preserving large language model: a clinician-AI collaborative retrospective evaluation in head and neck oncology

Yujing Zou

Laya Rafiee Sevyeri

Farhood Farahnak

George Shenouda

Marie Duclos

Tomás Yokoo Teodoro de Souza

Khalil Sultanem

Parsa Bagherzadeh

Farhad Maleki

Shirin A. Enger

Privacy regulations and limited expert-validation constrain the deployment of large language models (LLMs) for electronic health record stru… (voir plus)cturing. We evaluated locally deployed LLMs to extract 30 prognostic variables from 1,360 head and neck cancer reports (882 patients) using zero-shot prompting. A stratified 50-case subset was reviewed by three radiation oncologists (50 cases, 30 fields, 3 reviewers; 4,500 decisions) to form a majority-vote reference for Llama3.3-70B, which achieved 98.6% F1 with high clinician agreement and processed reports in 53 s/report. Among seven additional models (2.6B-70B) benchmarked against this reference, GPT-OSS-20.9B (F1 89.4%) and MedGemma-27B (F1 88.5%) performed best. Integrating LLM-extracted HPV status, smoking history, and Charlson Comorbidity Score into a multivariate Cox Proportional Hazards model (age, sex, T/N stage) improved disease-free survival (likelihood ratio test p = 0.014; ΔC-index + 0.071) and locoregional failure-free survival (p = 0.026; ΔC-index + 0.108) with 1,000-bootstrap internal validation. This clinician-AI collaborative evaluation shows that on-premises LLMs enable privacy-preserving and efficient tumour board support, longitudinal data curation, and outcome prediction.

2026-06-05

npj Precision Oncology (publié)

doi.org

Source-free Domain Adaptation Requires Penalized Diversity

Laya Rafiee Sevyeri

Ivaxi Sheth

Farhood Farahnak

Alexandre See

Samira Ebrahimi Kahou

Thomas Fevens

Mohammad Havaei

While neural networks are capable of achieving human-like performance in many tasks such as image classification, the impressive performance… (voir plus) of each model is limited to its own dataset. Source-free domain adaptation (SFDA) was introduced to address knowledge transfer between different domains in the absence of source data, thus, increasing data privacy. Diversity in representation space can be vital to a model`s adaptability in varied and difficult domains. In unsupervised SFDA, the diversity is limited to learning a single hypothesis on the source or learning multiple hypotheses with a shared feature extractor. Motivated by the improved predictive performance of ensembles, we propose a novel unsupervised SFDA algorithm that promotes representational diversity through the use of separate feature extractors with Distinct Backbone Architectures (DBA). Although diversity in feature space is increased, the unconstrained mutual information (MI) maximization may potentially introduce amplification of weak hypotheses. Thus we introduce the Weak Hypothesis Penalization (WHP) regularizer as a mitigation strategy. Our work proposes Penalized Diversity (PD) where the synergy of DBA and WHP is applied to unsupervised source-free domain adaptation for covariate shift. In addition, PD is augmented with a weighted MI maximization objective for label distribution shift. Empirical results on natural, synthetic, and medical domains demonstrate the effectiveness of PD under different distributional shifts.

2025-08-25

Machine Learning (publié)

doi.org

arxiv.org

Transparent Anomaly Detection via Concept-based Explanations

Laya Rafiee Sevyeri

Ivaxi Sheth

Farhood Farahnak

S. Enger

2023-10-15

ArXiv (prépublication)

doi.org