Generalization optimizing machine learning to improve CT scan radiomics and assess immune checkpoint inhibitors' response in non-small cell lung cancer: a multicenter cohort study.

Background: Recent developments in artificial intelligence suggest that radiomics may represent a promising non-invasive biomarker to predict response to immune checkpoint inhibitors (ICIs). Nevertheless, validation of radiomics algorithms in independent cohorts remains a challenge due to variations in image acquisition and reconstruction. Using radiomics, we investigated the importance of scan normalization as part of a broader machine learning framework to enable model external generalizability to predict ICI response in non-small cell lung cancer (NSCLC) patients across different centers. Methods: Radiomics features were extracted and compared from 642 advanced NSCLC patients on pre-ICI scans using established open-source PyRadiomics and a proprietary DeepRadiomics deep learning technology. The population was separated into two groups: a discovery cohort of 512 NSCLC patients from three academic centers and a validation cohort that included 130 NSCLC patients from a fourth center. We harmonized images to account for variations in reconstruction kernel, slice thicknesses, and device manufacturers. Multivariable models, evaluated using cross-validation, were used to estimate the predictive value of clinical variables, PD-L1 expression, and PyRadiomics or DeepRadiomics for progression-free survival at 6 months (PFS-6). Results: The best prognostic factor for PFS-6, excluding radiomics features, was obtained with the combination of Clinical + PD-L1 expression (AUC = 0.66 in the discovery and 0.62 in the validation cohort). Without image harmonization, combining Clinical + PyRadiomics or DeepRadiomics delivered an AUC = 0.69 and 0.69, respectively, in the discovery cohort, but dropped to 0.57 and 0.52, in the validation cohort. This lack of generalizability was consistent with observations in principal component analysis clustered by CT scan parameters. Subsequently, image harmonization eliminated these clusters. The combination of Clinical + DeepRadiomics reached an AUC = 0.67 and 0.63 in the discovery and validation cohort, respectively. Conversely, the combination of Clinical + PyRadiomics failed generalizability validations, with AUC = 0.66 and 0.59. Conclusion: We demonstrated that a risk prediction model combining Clinical + DeepRadiomics was generalizable following CT scan harmonization and machine learning generalization methods. These results had similar performances to routine oncology practice using Clinical + PD-L1. This study supports the strong potential of radiomics as a future non-invasive strategy to predict ICI response in advanced NSCLC.

Real-time differentiation of adenomatous and hyperplastic diminutive colorectal polyps during analysis of unaltered videos of standard colonoscopy using a deep learning model.

BACKGROUND: In general, academic but not community endoscopists have demonstrated adequate endoscopic differentiation accuracy to make the 'resect and discard' paradigm for diminutive colorectal polyps workable. Computer analysis of video could potentially eliminate the obstacle of interobserver variability in endoscopic polyp interpretation and enable widespread acceptance of 'resect and discard'. STUDY DESIGN AND METHODS: We developed an artificial intelligence (AI) model for real-time assessment of endoscopic video images of colorectal polyps. A deep convolutional neural network model was used. Only narrow band imaging video frames were used, split equally between relevant multiclasses. Unaltered videos from routine exams not specifically designed or adapted for AI classification were used to train and validate the model. The model was tested on a separate series of 125 videos of consecutively encountered diminutive polyps that were proven to be adenomas or hyperplastic polyps. RESULTS: The AI model works with a confidence mechanism and did not generate sufficient confidence to predict the histology of 19 polyps in the test set, representing 15% of the polyps. For the remaining 106 diminutive polyps, the accuracy of the model was 94% (95% CI 86% to 97%), the sensitivity for identification of adenomas was 98% (95% CI 92% to 100%), specificity was 83% (95% CI 67% to 93%), negative predictive value 97% and positive predictive value 90%. CONCLUSIONS: An AI model trained on endoscopic video can differentiate diminutive adenomas from hyperplastic polyps with high accuracy. Additional study of this programme in a live patient clinical trial setting to address resect and discard is planned.

Role of artificial intelligence in the care of patients with nonsmall cell lung cancer.

BACKGROUND: Lung cancer is the leading cause of cancer death worldwide. In up to 57% of patients, it is diagnosed at an advanced stage and the 5-year survival rate ranges between 10%-16%. There has been a significant amount of research using machine learning to generate tools using patient data to improve outcomes. METHODS: This narrative review is based on research material obtained from PubMed up to Nov 2017. The search terms include "artificial intelligence," "machine learning," "lung cancer," "Nonsmall Cell Lung Cancer (NSCLC)," "diagnosis" and "treatment." RESULTS: Recent studies support the use of computer-aided systems and the use of radiomic features to help diagnose lung cancer earlier. Other studies have looked at machine learning (ML) methods that offer prognostic tools to doctors and help them in choosing personalized treatment options for their patients based on molecular, genetics and histological features. Combining artificial intelligence approaches into health care may serve as a beneficial tool for patients with NSCLC, and this review outlines these benefits and current shortcomings throughout the continuum of care. CONCLUSION: We present a review of the various applications of ML methods in NSCLC as it relates to improving diagnosis, treatment and outcomes.

Bone microstructure and elastic tissue properties are reflected in QUS axial transmission measurements.

Accurate clinical interpretation of the sound velocity derived from axial transmission devices requires a detailed understanding of the propagation phenomena involved and of the bone factors that have an impact on measurements. In the low megahertz range, ultrasonic propagation in cortical bone depends on anisotropic elastic tissue properties, porosity and the cortical geometry (e.g., thickness). We investigated 10 human radius samples from a previous biaxial transmission study using a 50-MHz scanning acoustic microscope (SAM) and synchrotron radiation microcomputed tomography. The relationships between low-frequency axial transmission sound speed at 1 and 2 MHz, structural properties (cortical width Ct.Wi, porosity, Haversian canal density and material properties (acoustic impedance, mineral density) on site-matched cross-sections were investigated. Significant linear multivariate regression models (1 MHz: R(2) = 0.84, p < 10(-4), root-mean-square error (RMSE) = 38 m/s, 2 MHz: R(2) = 0.65, p < 10(-4), RMSE = 48 m/s) were found for the combination of Ct.Wi with porosity and impedance. A new model was derived that accounts for the nonlinear dispersion relation with Ct.Wi and predicts axial transmission velocities measured at different ultrasonic frequencies (R(2) = 0.69, p < 10(-4), RMSE = 52 m/s).