Predicting EGFR and PD-L1 Status in NSCLC Patients Using Multitask AI System Based on CT Images. Frontiers in immunology BACKGROUND:Epidermal growth factor receptor (EGFR) genotyping and programmed death ligand-1 (PD-L1) expressions are of paramount importance for treatment guidelines such as the use of tyrosine kinase inhibitors (TKIs) and immune checkpoint inhibitors (ICIs) in lung cancer. Conventional identification of EGFR or PD-L1 status requires surgical or biopsied tumor specimens, which are obtained through invasive procedures associated with risk of morbidities and may be unavailable to access tissue samples. Here, we developed an artificial intelligence (AI) system that can predict EGFR and PD-L1 status in using non-invasive computed tomography (CT) images. METHODS:A multitask AI system including deep learning (DL) module, radiomics (RA) module, and joint (JO) module combining the DL, RA, and clinical features was developed, trained, and optimized with CT images to predict the EGFR and PD-L1 status. We used feature selectors and feature fusion methods to find the best model among combinations of module types. The models were evaluated using the areas under the receiver operating characteristic curves (AUCs). RESULTS:Our multitask AI system yielded promising performance for gene expression status, subtype classification, and joint prediction. The AUCs of DL module achieved 0.842 (95% CI, 0.825-0.855) in the EGFR mutated status and 0.805 (95% CI, 0.779-0.829) in the mutated-EGFR subtypes discrimination (19Del, L858R, other mutations). DL module also demonstrated the AUCs of 0.799 (95% CI, 0.762-0.854) in the PD-L1 expression status and 0.837 (95% CI, 0.775-0.911) in the positive-PD-L1 subtypes (PD-L1 tumor proportion score, 1%-49% and ≥50%). Furthermore, the JO module of our AI system performed well in the EGFR and PD-L1 joint cohort, with an AUC of 0.928 (95% CI, 0.909-0.946) for distinguishing EGFR mutated status and 0.905 (95% CI, 0.886-0.930) for discriminating PD-L1 expression status. CONCLUSION:Our AI system has demonstrated the encouraging results for identifying gene status and further assessing the genotypes. Both clinical indicators and radiomics features showed a complementary role in prediction and provided accurate estimates to predict EGFR and PD-L1 status. Furthermore, this non-invasive, high-throughput, and interpretable AI system can be used as an assistive tool in conjunction with or in lieu of ancillary tests and extensive diagnostic workups to facilitate early intervention. 10.3389/fimmu.2022.813072

Emerging role of deep learning-based artificial intelligence in tumor pathology. Cancer communications (London, England) The development of digital pathology and progression of state-of-the-art algorithms for computer vision have led to increasing interest in the use of artificial intelligence (AI), especially deep learning (DL)-based AI, in tumor pathology. The DL-based algorithms have been developed to conduct all kinds of work involved in tumor pathology, including tumor diagnosis, subtyping, grading, staging, and prognostic prediction, as well as the identification of pathological features, biomarkers and genetic changes. The applications of AI in pathology not only contribute to improve diagnostic accuracy and objectivity but also reduce the workload of pathologists and subsequently enable them to spend additional time on high-level decision-making tasks. In addition, AI is useful for pathologists to meet the requirements of precision oncology. However, there are still some challenges relating to the implementation of AI, including the issues of algorithm validation and interpretability, computing systems, the unbelieving attitude of pathologists, clinicians and patients, as well as regulators and reimbursements. Herein, we present an overview on how AI-based approaches could be integrated into the workflow of pathologists and discuss the challenges and perspectives of the implementation of AI in tumor pathology. 10.1002/cac2.12012

Predicting gene mutation status via artificial intelligence technologies based on multimodal integration (MMI) to advance precision oncology. Seminars in cancer biology Personalized treatment strategies for cancer frequently rely on the detection of genetic alterations which are determined by molecular biology assays. Historically, these processes typically required single-gene sequencing, next-generation sequencing, or visual inspection of histopathology slides by experienced pathologists in a clinical context. In the past decade, advances in artificial intelligence (AI) technologies have demonstrated remarkable potential in assisting physicians with accurate diagnosis of oncology image-recognition tasks. Meanwhile, AI techniques make it possible to integrate multimodal data such as radiology, histology, and genomics, providing critical guidance for the stratification of patients in the context of precision therapy. Given that the mutation detection is unaffordable and time-consuming for a considerable number of patients, predicting gene mutations based on routine clinical radiological scans or whole-slide images of tissue with AI-based methods has become a hot issue in actual clinical practice. In this review, we synthesized the general framework of multimodal integration (MMI) for molecular intelligent diagnostics beyond standard techniques. Then we summarized the emerging applications of AI in the prediction of mutational and molecular profiles of common cancers (lung, brain, breast, and other tumor types) pertaining to radiology and histology imaging. Furthermore, we concluded that there truly exist multiple challenges of AI techniques in the way of its real-world application in the medical field, including data curation, feature fusion, model interpretability, and practice regulations. Despite these challenges, we still prospect the clinical implementation of AI as a highly potential decision-support tool to aid oncologists in future cancer treatment management. 10.1016/j.semcancer.2023.02.006