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


Autoři: Georgios Kaissis aff001;  Sebastian Ziegelmayer aff001;  Fabian Lohöfer aff001;  Katja Steiger aff002;  Hana Algül aff003;  Alexander Muckenhuber aff002;  Hsi-Yu Yen aff002;  Ernst Rummeny aff001;  Helmut Friess aff004;  Roland Schmid aff003;  Wilko Weichert aff002;  Jens T. Siveke aff005;  Rickmer Braren aff001
Působiště autorů: Department of Diagnostic and Interventional Radiology, School of Medicine, Technical University of Munich, Munich, Germany aff001;  Department of Pathology, School of Medicine, Technical University of Munich, Munich, Germany aff002;  Department of Internal Medicine II, School of Medicine, Technical University of Munich, Munich, Germany aff003;  Department of Surgery, School of Medicine, Technical University of Munich, Munich, Germany aff004;  Division of Solid Tumor Translational Oncology, West German Cancer Center, University Hospital Essen, Essen, Germany aff005;  German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Heidelberg, Germany aff006
Vyšlo v časopise: PLoS ONE 14(10)
Kategorie: Research Article
doi: 10.1371/journal.pone.0218642

Souhrn

Purpose

Development 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.

Methods

The 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.

Results

The 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.

Conclusions

The 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.

Klíčová slova:

Adenocarcinomas – Algorithms – Cancer chemotherapy – Cancer treatment – Entropy – Chemotherapy – Magnetic resonance imaging – Machine learning algorithms


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PLOS One


2019 Číslo 10

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