Deep Learning Predicts Underlying Features on Pathology Images with Therapeutic Relevance for Breast and Gastric Cancer.
Renan ValierisLucas AmaroCynthia Aparecida Bueno de Toledo OsórioAdriana Passos BuenoRafael Andres Rosales MitrowskyDirce Maria CarraroDiana Noronha NunesEmmanuel Dias-NetoIsrael Tojal da SilvaPublished in: Cancers (2020)
DNA repair deficiency (DRD) is an important driver of carcinogenesis and an efficient target for anti-tumor therapies to improve patient survival. Thus, detection of DRD in tumors is paramount. Currently, determination of DRD in tumors is dependent on wet-lab assays. Here we describe an efficient machine learning algorithm which can predict DRD from histopathological images. The utility of this algorithm is demonstrated with data obtained from 1445 cancer patients. Our method performs rather well when trained on breast cancer specimens with homologous recombination deficiency (HRD), AUC (area under curve) = 0.80. Results for an independent breast cancer cohort achieved an AUC = 0.70. The utility of our method was further shown by considering the detection of mismatch repair deficiency (MMRD) in gastric cancer, yielding an AUC = 0.81. Our results demonstrate the capacity of our learning-base system as a low-cost tool for DRD detection.
Keyphrases
- deep learning
- dna repair
- machine learning
- dna damage
- artificial intelligence
- convolutional neural network
- low cost
- loop mediated isothermal amplification
- real time pcr
- big data
- label free
- dna damage response
- replacement therapy
- young adults
- oxidative stress
- high throughput
- liquid chromatography
- optical coherence tomography
- high intensity
- quantum dots
- resistance training