Based on artificial intelligence (AI), computer-assisted medical diagnosis can scientifically and efficiently deal with a large quantity of medical imaging data. AI technologies including deep learning have shown remarkable progress across medical image recognition and genome analysis. Imaging-genomics attempts to explore the associations between potential gene expression patterns and specific imaging phenotypes. These associations provide potential cellular pathophysiology information, allowing sampling of the lesion habitat with high spatial resolution. Glioblastoma (GB) poses spatial and temporal heterogeneous characteristics, challenging to current precise diagnosis and treatments for the disease. Imaging-genomics provides a powerful tool for non-invasive global assessment of GB and its response to treatment. Imaging-genomics also has the potential to advance our understanding of underlying cancer biology, gene alterations, and corresponding biological processes. This article reviews the recent progress in the utilization of the imaging-genomics analysis in GB patients, focusing on its implications and prospects in individualized diagnosis and management.

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Imaging-Genomics in Glioblastoma: Combining Molecular and Imaging Signatures.

http://www.cell.com/article/S2589004222021459/pdf

Vision transformer-based weakly supervised histopathological image analysis of primary brain tumors

AI Neuropathologist: an innovative technology enabling a faultless pathological diagnosis?

In this paper, we propose to quantitatively compare loss functions based on parameterized Tsallis–Havrda–Charvat entropy and classical Shannon entropy for the training of a deep network in the case of small datasets which are usually encountered in medical applications. Shannon cross-entropy is widely used as a loss function for most neural networks applied to the segmentation, classification and detection of images. Shannon entropy is a particular case of Tsallis–Havrda–Charvat entropy. In this work, we compare these two entropies through a medical application for predicting recurrence in patients with head–neck and lung cancers after treatment. Based on both CT images and patient information, a multitask deep neural network is proposed to perform a recurrence prediction task using cross-entropy as a loss function and an image reconstruction task. Tsallis–Havrda–Charvat cross-entropy is a parameterized cross-entropy with the parameter α . Shannon entropy is a particular case of Tsallis–Havrda–Charvat entropy for α=1 . The influence of this parameter on the final prediction results is studied. In this paper, the experiments are conducted on two datasets including in total 580 patients, of whom 434 suffered from head–neck cancers and 146 from lung cancers. The results show that Tsallis–Havrda–Charvat entropy can achieve better performance in terms of prediction accuracy with some values of α .

/pdf/a-quantitative-comparison-between-shannon-and-tsallis-havrda-1h0r5qll.pdf

A Quantitative Comparison between Shannon and Tsallis–Havrda–Charvat Entropies Applied to Cancer Outcome Prediction

Simple Summary Early diagnosis and treatment are essential to reduce the mortality rate of bladder cancer. However, current techniques of diagnosis are susceptible to pathologist variability, and histopathological prognostic methods are insufficient to cover all features of muscle-invasive bladder cancer. In this work, we developed weakly supervised models based on deep learning for the diagnosis of bladder cancer and prediction of overall survival in muscle-invasive bladder cancer patients using whole slide digitized histological images in two cohorts. Encouragingly, results showed that our models can not only assist clinicians in the accurate diagnosis of bladder cancer, but also facilitate differential risk stratification in patients with muscle-invasive bladder cancer and improve personalized treatment decisions accordingly. Furthermore, the regions most relevant for diagnosis or prognosis can be further analyzed to increase the amount of information extracted from pathological images. Finally, we identified six genes closely related to cancer progression based on the predicted risk scores, which potentially led to new biomarker discoveries. Abstract (1) Background: Early diagnosis and treatment are essential to reduce the mortality rate of bladder cancer (BLCA). We aimed to develop deep learning (DL)-based weakly supervised models for the diagnosis of BLCA and prediction of overall survival (OS) in muscle-invasive bladder cancer (MIBC) patients using whole slide digitized histological images (WSIs). (2) Methods: Diagnostic and prognostic models were developed using 926 WSIs of 412 BLCA patients from The Cancer Genome Atlas cohort. We collected 250 WSIs of 150 BLCA patients from the Renmin Hospital of Wuhan University cohort for external validation of the models. Two DL models were developed: a BLCA diagnostic model (named BlcaMIL) and an MIBC prognostic model (named MibcMLP). (3) Results: The BlcaMIL model identified BLCA with accuracy 0.987 in the external validation set, comparable to that of expert uropathologists and outperforming a junior pathologist. The C-index values for the MibcMLP model on the internal and external validation sets were 0.631 and 0.622, respectively. The risk score predicted by MibcMLP was a strong predictor independent of existing clinical or histopathologic indicators, as demonstrated by univariate Cox (HR = 2.390, p < 0.0001) and multivariate Cox (HR = 2.414, p < 0.0001) analyses. The interpretability of DL models can help in the analysis of critical regions associated with tumors to enrich the information obtained from WSIs. Furthermore, the expression of six genes (ANAPC7, MAPKAPK5, COX19, LINC01106, AL161431.1 and MYO16-AS1) was significantly associated with MibcMLP-predicted risk scores, revealing possible potential biological correlations. (4) Conclusions: Our study developed DL models for accurately diagnosing BLCA and predicting OS in MIBC patients, which will help promote the precise pathological diagnosis of BLCA and risk stratification of MIBC to improve clinical treatment decisions.

https://www.mdpi.com/2072-6694/14/23/5807/pdf?version=1669367602

Accurate Diagnosis and Survival Prediction of Bladder Cancer Using Deep Learning on Histological Slides

Background Pathological diagnosis of glioma subtypes is essential for treatment planning and prognosis. Standard histological diagnosis of glioma is based on postoperative hematoxylin and eosin stained slides by neuropathologists. With advancing artificial intelligence (AI), the aim of this study was to determine whether deep learning can be applied to glioma classification. Methods A neuropathological diagnostic platform is designed comprising a slide scanner and deep convolutional neural networks (CNNs) to classify 5 major histological subtypes of glioma to assist pathologists. The CNNs were trained and verified on over 79 990 histological patch images from 267 patients. A logical algorithm is used when molecular profiles are available. Results A new model of the squeeze-and-excitation block DenseNet with weighted cross-entropy (named SD-Net_WCE) is developed for the glioma classification task, which learns the recognizable features of glioma histology CNN-based independent diagnostic testing on data from 56 patients with 17 262 histological patch images demonstrated patch level accuracy of 86.5% and patient level accuracy of 87.5%. Histopathological classifications could be further amplified to integrated neuropathological diagnosis by 2 molecular markers (isocitrate dehydrogenase and 1p/19q). Conclusion The model is capable of solving multiple classification tasks and can satisfactorily classify glioma subtypes. The system provides a novel aid for the integrated neuropathological diagnostic workflow of glioma.

Artificial intelligence neuropathologist for glioma classification using deep learning on hematoxylin and eosin stained slide images and molecular markers

The embodiment of the invention discloses a tumor determination system and method and a storage medium. The method comprises the following steps: an image acquisition module used for acquiring a pathological image of a detected tissue; an image extraction module which is used for extracting analysis images with one or more sizes from the pathological image of the detected tissue; an analysis module which is used for analyzing the analysis image based on one or more trained detection models to obtain a plurality of classification characteristics, wherein each trained detection model is used fordetermining at least one classification characteristic; And an output module which is used for determining and outputting a tumor recognition result according to the classification characteristics. According to the technical scheme, the problems that in the prior art, tumor typing depends on experience of doctors, and the tumor typing accuracy is low can be solved, and the technical effect of automatically conducting tumor typing and tumor classification is achieved.

Chun Qiuping

Papers

Artificial intelligence neuropathologist for glioma classification using deep learning on hematoxylin and eosin stained slide images and molecular markers

Tumor determination system and method and storage medium