Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma

Johannet, Paul; Coudray, Nicolas; Donnelly, Douglas M.; Jour, George; Illa-Bochaca, Irineu; Xia, Yuhe; Johnson, Douglas B.; Wheless, Lee; Patrinely, James R.; Nomikou, Sofia; Rimm, David L.; Pavlick, Anna C.; Weber, Jeffrey S.; Zhong, Judy; Tsirigos, Aristotelis; Osman, Iman

doi:10.1158/1078-0432.ccr-20-2415

Cited by 116 publications

(87 citation statements)

References 37 publications

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“…Johannet and colleagues reported a more advanced AI approach using CNNs trained and tested on treatment-naïve histopathology slides together with patients' clinical characteristics to predict responses to checkpoint immunotherapy in patients with advanced melanoma (AUC, 0.80; ref. 96).…”

Section: Patient Prognosis and Response To Therapymentioning

confidence: 99%

Artificial Intelligence in Cancer Research and Precision Medicine

Bhinder

Gilvary²,

Madhukar³

et al. 2021

Cancer Discovery

310

153

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Convolutional neural networks (CNN) have been the most popular deep learning architectures used for image classification in cancer ( Fig. 1 ). CNNs apply a series of nonlinear transformations to structured data (such as raw pixels of an image) to learn relevant features automatically, unlike conventional machine learning models that frequently require manual feature curation. On the fl ip side, it is diffi cult to tell what features are learned by the CNNs, making them what many have referred to as a "black box." One consequence is that images used for CNNs should be carefully prepro cessed to reduce the risk that the model learns from image artifacts. There are two major approaches for CNN models; one is transfer learning that uses images from a large collection of natural objects (such as in ImageNet) to train the initial layers of a model (where the model learns to identify general features such as shapes and edges) and then uses the diseasespecifi c data to fi ne-tune the training parameters in the last layers. The second variation of CNN is based on an autoencoder where the model learns background features from a subset of representative images and encodes a compressed representation of the basic features later used to initialize the CNN. In the CAMELYON16 Challenge-a crowdsourced competition to identify and classify lymph node metastasis in patients with breast cancer from whole slide images (WSI) of hematoxylin and eosin (H&E)-stained tumors-25 of the 32 submitted algorithms were CNNs, and the top 5 classifi cation models were exclusively based on transfer learning, which were GoogLeNet, ResNet, VGG-16 ( 2 ). Khosravi and colleagues trained and tested several state-of-the-art deep learning models to classify WSI from H&E-stained tumor tissues of The Cancer Genome Atlas (TCGA) cohort and reported on the

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Section: Patient Prognosis and Response To Therapymentioning

confidence: 99%

Artificial Intelligence in Cancer Research and Precision Medicine

Bhinder

Gilvary²,

Madhukar³

et al. 2021

Cancer Discovery

310

153

View full text Add to dashboard Cite

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“…These results were then combined through a multivariable logistic regression with clinical characteristics such as age, gender, histologic subtypes, etc. The classi er accurately strati ed patients into high versus low risk for disease progression with an AUC = 0.80 (32). Lastly, in another work regarding IO prediction in bladder cancer CT-scans were used to develop a ML model according to RECIST methodology and the ROI were processed to extract radiomic features.…”

Section: Discussionmentioning

confidence: 99%

Machine Learning to Improve Treatment Selection for NSCLC Patients Treated with Immunotherapy Using Real World and Translational Data

Prelaj

Boeri

Robuschi

et al. 2021

Preprint

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BackgroundIn advanced Non-Small Cell Lung Cancer (NSCLC), Programmed Death Ligand 1 (PD-L1) remains the only used biomarker to candidate patients to immunotherapy (IO) with many limits. Given the complex dynamics of the immune system it is improbable that a single biomarker could be able to profile prediction with high accuracy. A promising solution cope with this complexity is provided by Artificial Intelligence (AI) and Machine Learning (ML), which are techniques able to analyse and interpret big multifactorial data. The present study aims at using AI tools to improve response and efficacy prediction in NSCLC patients treated with IO. MethodsReal world data (clinical data, PD-L1, histology, molecular, lab tests) and the blood microRNA signature classifier (MSC), which include 24 different microRNAs, were used. Patients were divided into responders (R), who obtained a complete or partial response or stable disease as best response, and non-responders (NR), who experienced progressive or hyperprogressive disease and those who died before the first radiologic evaluation. Moreover, we used the same data to determine if the overall survival of the patients was likely to be shorter or longer than 24 months from baseline IO. For A literature review and forward feature selection technique was used to extract a specific subset of the patients data. To develop the final predictive model, different ML methods have been tested, i.e., Feedforward Neural Network (FFNN), Logistic Regression (LR), K-nearest neighbors (K-NN), Support Vector Machines (SVM), and Random Forest (RF).Results 200 patients were included. 164 out of 200 (i.e., only those patients with PD-L1 data available) were considered in the model, 73 (44.5%) were R and 91 (55.5%) NR. Overall, the best model was the LR and included 5 features: 2 clinical features including the ECOG performance status and IO-line of therapy; 1 tissue feature such as PD-L1 tumour expression; and 2 blood features including the MSC test and the neutrophil-to-lymphocyte ratio (NLR). The model predicting R/NR of the patient achieves accuracy ACC= 0.756, F1 score F1=0.722, and Area Under the ROC Curve AUC=0.82. The use of the PD-L1 alone has an ACC=0.655. The accuracy of the ML models excluding some of the features from the model were as follow: without PD-L1 value (ACC=0.726), MSC (ACC=0.750), and both PD-L1 and MSC (ACC=0.707), i.e., considering only clinical features. At data cut-off (Nov 2020), median Overall Survival (mOS) for R was 38.5 months (m) (95%IC 23.9 - 53.1) vs 3.8 m (95%IC 2.8 - 4.7) for NR, with p<0.001. LR was the most performing model in predicting patients with long survival (24-months OS), achieving ACC=0.839, F1=0.908, and AUC=0.87. ConclusionsThe results suggest that the integration of multifactorial data provided by ML techniques is a useful tool to improve personalized selection of NSCLC patients candidates to IO. In particular, compare to PD-L1 alone the expected improvement was around 10%. In particular, the model shows that the higher the ECOG, NLR value, IO-line, and MSC test level the lower the response, and the higher PD-L1 the higher the response. Considering the difference in survival among R and NR groups, these results suggest that the model can also be used to indirectly predict survival. Moreover, a second model was able to predict long survival patients with good accuracy.

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“…Johannet el al . developed a pipeline that integrates DL on histology specimens with clinical data to predict immunotherapy response in advanced melanoma [72]. The results showed that the classifier accurately stratified patients into responders and non‐responders with an AUC of 0.80.…”

Section: Patient Prognosis Response To Therapy and Precision Medicinementioning

confidence: 99%

Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine

Chen

Lin

et al. 2021

Cancer Communications

103

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Over the past decade, artificial intelligence (AI) has contributed substantially to the resolution of various medical problems, including cancer. Deep learning (DL), a subfield of AI, is characterized by its ability to perform automated feature extraction and has great power in the assimilation and evaluation of large amounts of complicated data. On the basis of a large quantity of medical data and novel computational technologies, AI, especially DL, has been applied in various aspects of oncology research and has the potential to enhance cancer diagnosis and treatment. These applications range from early cancer detection, diagnosis, classification and grading, molecular characterization of tumors, prediction of patient outcomes and treatment responses, personalized treatment, automatic radiotherapy workflows, novel anti‐cancer drug discovery, and clinical trials. In this review, we introduced the general principle of AI, summarized major areas of its application for cancer diagnosis and treatment, and discussed its future directions and remaining challenges. As the adoption of AI in clinical use is increasing, we anticipate the arrival of AI‐powered cancer care.

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Using Machine Learning Algorithms to Predict Immunotherapy Response in Patients with Advanced Melanoma

Cited by 116 publications

References 37 publications

Artificial Intelligence in Cancer Research and Precision Medicine

Artificial Intelligence in Cancer Research and Precision Medicine

Machine Learning to Improve Treatment Selection for NSCLC Patients Treated with Immunotherapy Using Real World and Translational Data

Artificial intelligence for assisting cancer diagnosis and treatment in the era of precision medicine

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