Artificial intelligence (AI) is rapidly reshaping cancer research and personalized clinical care Availability of high-dimensionality datasets coupled with advances in high-performance computing, as well as innovative deep learning architectures, has led to an explosion of AI use in various aspects of oncology research These applications range from detection and classification of cancer, to molecular characterization of tumors and their microenvironment, to drug discovery and repurposing, to predicting treatment outcomes for patients As these advances start penetrating the clinic, we foresee a shifting paradigm in cancer care becoming strongly driven by AI SIGNIFICANCE: AI has the potential to dramatically affect nearly all aspects of oncology-from enhancing diagnosis to personalizing treatment and discovering novel anticancer drugs Here, we review the recent enormous progress in the application of AI to oncology, highlight limitations and pitfalls, and chart a path for adoption of AI in the cancer clinic

Artificial Intelligence in Cancer Research and Precision Medicine

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.

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cac2.12215

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

Colon cancer is a common type of carcinoma that occurs in the large intestine. This type of cancer affects millions of people around the world each year. Early and accurate diagnosis is very important in the treatment of colon cancer as in other types of cancer. Thanks to early and accurate diagnosis, many people can get rid of this disease with less damage. Medical imaging techniques are widely used in the early diagnosis, follow‐up, and after the treatment process of colon cancer. Therefore, manually controlling a large number of medical images and their interpretation is a difficult process and consumes more time. In addition, the interpretation of data with traditional methods in this process can cause misdiagnosis due to human errors. For this reason, computer‐aided systems can be used in the diagnosis of colon cancer in order to both help experts and carry out the process more quickly and successfully. In this study, a novel method named by us, CNN‐based, MA_ColonNET is developed for detecting colon cancer image data. A 45‐layer model in MA_ColonNET has been used to classify. A success (accuracy) rate of 99.75% has been achieved by means of the new model. It is shown that the proposed model can detect colon cancer earlier. In this way, the treatment process can be carried out more successfully.

Classification with respect to colon adenocarcinoma and colon benign tissue of colon histopathological images with a new CNN model: MA_ColonNET

Simultaneous profiling of the spatial distributions of multiple biological molecules at single-cell resolution has recently been enabled by the development of highly multiplexed imaging technologies. Extracting and analyzing biologically relevant information contained in complex imaging data requires the use of a diverse set of computational tools and algorithms. Here, we report the development of a user-friendly, customizable, and interoperable workflow for processing and analyzing data generated by highly multiplexed imaging technologies. The steinbock framework supports image pre-processing, segmentation, feature extraction, and standardized data export. Each step is performed in a reproducible fashion. The imcRtools R/Bioconductor package forms the bridge between image processing and single-cell analysis by directly importing data generated by steinbock. The package further supports spatial data analysis and integrates with tools developed within the Bioconductor project. Together, the tools described in this workflow facilitate analyses of multiplexed imaging raw data at the single-cell and spatial level.

https://www.biorxiv.org/content/biorxiv/early/2021/11/13/2021.11.12.468357.full.pdf

An end-to-end workflow for multiplexed image processing and analysis

/pdf/gashissdb-a-new-gastric-histopathology-image-dataset-for-j6pi6wsq.pdf

GasHisSDB: A new gastric histopathology image dataset for computer aided diagnosis of gastric cancer

A novel approach combined transfer learning and deep learning to predict TMB from histology image

Highly multiplexed imaging technology is a powerful tool to facilitate understanding cells composition and interaction in tumor microenvironment at subcellular resolution, which is crucial for both basic research and clinical applications. Imaging mass cytometry (IMC), a multiplex imaging method recently introduced, can measure up to 40 markers simultaneously in one tissue section by using a high-resolution laser with a mass cytometer. However, due to its high resolution and large number of channels, how to process and interpret the image data from IMC remains a key challenge for its further applications. Accurate and reliable single cell segmentation is the first and a critical step to process IMC image data. Unfortunately, existing segmentation pipelines either produce inaccurate cell segmentation results, or require manual annotation which is very time-consuming. Here, we developed Dice-XMBD, a Deep learnIng-based Cell sEgmentation algorithm for tissue multiplexed imaging data. %, which combine nuclear proteins and membrane/cytoplasm proteins as two channels of input. In comparison with other state-of-the-art cell segmentation methods currently used in IMC, Dice-XMBD generates more accurate single cell masks efficiently on IMC images produced with different nuclear, membrane and cytoplasm markers. All codes and datasets are available at https://github.com/xmuyulab/Dice-XMBD.

/pdf/dice-xmbd-deep-learning-based-cell-segmentation-for-imaging-5fdslvgev7.pdf

Dice-XMBD: Deep Learning-Based Cell Segmentation for Imaging Mass Cytometry.

Highly multiplexed imaging technology is a powerful tool to facilitate understanding cells composition and interaction in tumor microenvironment at subcellular resolution, which is crucial for both basic research and clinical applications. Imaging mass cytometry (IMC), a multiplex imaging method recently introduced, can measure up to 40 markers simultaneously in one tissue section by using a high-resolution laser with a mass cytometer. However, due to its high resolution and large number of channels, how to process and interpret the image data from IMC remains a key challenge for its further applications. Accurate and reliable single cell segmentation is the first and a critical step to process IMC image data. Unfortunately, existing segmentation pipelines either produce inaccurate cell segmentation results, or require manual annotation which is very time-consuming. Here, we developed Dice-XMBD, a Deep learnIng-based Cell sEgmentation algorithm for tissue multiplexed imaging data. In comparison with other state-of-the-art cell segmentation methods currently used in IMC, Dice-XMBD generates more accurate single cell masks efficiently on IMC images produced with different nuclear, membrane and cytoplasm markers. All codes and datasets are available at https://github.com/xmuyulab/Dice-XMBD.

/pdf/dice-xmbd-deep-learning-based-cell-segmentation-for-imaging-59ozh7jftq.pdf

Yudi Jiao

Papers

A novel approach combined transfer learning and deep learning to predict TMB from histology image

Dice-XMBD: Deep Learning-Based Cell Segmentation for Imaging Mass Cytometry.

Dice-XMBD: Deep learning-based cell segmentation for imaging mass cytometry