A machine learning algorithm predicts molecular subtypes in pancreatic ductal adenocarcinoma with differential response to gemcitabine-based versus FOLFIRINOX chemotherapy

Abstract

PurposeDevelopment of a supervised machine-learning model capable of predicting clinically relevant molecular subtypes of pancreatic ductal adenocarcinoma (PDAC) from diffusion-weighted-imaging-derived radiomic features.MethodsThe retrospective observational study assessed 55 surgical PDAC patients. Molecular subtypes were defined by immunohistochemical staining of KRT81. Tumors were manually segmented and 1606 radiomic features were extracted with PyRadiomics. A gradient-boosted-tree algorithm was trained on 70% of the patients (N = 28) and tested on 30% (N = 17) to predict KRT81+ vs. KRT81- tumor subtypes. A gradient-boosted survival regression model was fit to the disease-free and overall survival data. Chemotherapy response and survival were assessed stratified by subtype and radiomic signature. Radiomic feature importance was ranked.ResultsThe mean±STDEV sensitivity, specificity and ROC-AUC were 0.90±0.07, 0.92±0.11, and 0.93±0.07, respectively. The mean±STDEV concordance indices between the disease-free and overall survival predicted by the model based on the radiomic parameters and actual patient survival were 0.76±0.05 and 0.71±0.06, respectively. Patients with a KRT81+ subtype experienced significantly diminished median overall survival compared to KRT81- patients (7.0 vs. 22.6 months, HR 4.03, log-rank-test P = <0.001) and a significantly improved response to gemcitabine-based chemotherapy over FOLFIRINOX (10.14 vs. 3.8 months median overall survival, HR 2.33, P = 0.037) compared to KRT81- patients, who responded significantly better to FOLFIRINOX over gemcitabine-based treatment (30.8 vs. 13.4 months median overall survival, HR 2.41, P = 0.027). Entropy was ranked as the most important radiomic feature.ConclusionsThe machine-learning based analysis of radiomic features enables the prediction of subtypes of PDAC, which are highly relevant for disease-free and overall patient survival and response to chemotherapy.

Image-Based Molecular Phenotyping of Pancreatic Ductal Adenocarcinoma

“…We previously presented a machine learning-algorithm capable of subtype characterization and-by extension-patient survival, based on pre-operative diffusion weighted MRI [8,9]. Extending this work, our current findings successfully transfer this methodology to routine CT acquisitions.…”

“…We recently reported on machine-learning approaches for the prediction of molecular subtypes and survival risk in PDAC patients from pre-operative magnetic resonance imaging (MRI) [8,9]. We noted that limited availability of MR imaging data, overall reduced image quality and the less-quantitative and unstandardized nature of MRI pose barriers to algorithm development and generalization.…”

Clinical Validation of a Machine-learning–derived Signature Predictive of Outcomes from First-line Oxaliplatin-based Chemotherapy in Advanced Colorectal Cancer

“…Machine-learning algorithms can be instrumental in uncovering such patterns as it has been demonstrated in the context of radiologic imaging 20 . However, machine-learning algorithms as decision support tools for standard treatment decisions in oncology have mostly been limited to cognitive support systems such as IBM Watson 21 or lack sufficient clinical validation 22,23 .…”

Swarm Learning as a privacy-preserving machine learning approach for disease classification

“…This will not be restricted to omics data as exemplified here, but will extend to other large medical data such as medical imaging data 55,56 . Particularly in oncology, great successes applying machine learning have already been reported for tumor detection 47,55,57,58 , subtyping 59,60 , grading 61 , genomic characterization 62 , or outcome prediction 63 , yet progress is hindered by too small datasets at any given institution 26 with current privacy regulations 8 (hhs.gov, https://www.hhs.gov/hipaa/index.html, 2020; Intersoft Consulting, General Data Protection Regulation, https://gdpr-info.ee) making it less appealing to develop centralized AI systems. We introduce Swarm Learning as a decentralized learning system with access to data stored locally that can replace the current paradigm of data sharing and centralized storage while preserving data privacy in cross-institutional research in a wide spectrum of biomedical disciplines.…”