Development of pathological reconstructed high-resolution images using artificial intelligence based on whole slide image

Digital pathology, deep learning, and cancer: a narrative review

Background and Objective

Cancer is a leading cause of morbidity and mortality worldwide. The emergence of digital pathology and deep learning technologies signifies a transformative era in healthcare. These technologies can enhance cancer detection, streamline operations, and bolster patient care. A substantial gap exists between the development phase of deep learning models in controlled laboratory environments and their translations into clinical practice. This narrative review evaluates the current landscape of deep learning and digital pathology, analyzing the factors influencing model development and implementation into clinical practice.

Methods

We searched multiple databases, including Web of Science, Arxiv, MedRxiv, BioRxiv, Embase, PubMed, DBLP, Google Scholar, IEEE Xplore, Semantic Scholar, and Cochrane, targeting articles on whole slide imaging and deep learning published from 2014 and 2023. Out of 776 articles identified based on inclusion criteria, we selected 36 papers for the analysis.

Key Content and Findings

Most articles in this review focus on the in-laboratory phase of deep learning model development, a critical stage in the deep learning lifecycle. Challenges arise during model development and their integration into clinical practice. Notably, lab performance metrics may not always match real-world clinical outcomes. As technology advances and regulations evolve, we expect more clinical trials to bridge this performance gap and validate deep learning models' effectiveness in clinical care. High clinical accuracy is vital for informed decision-making throughout a patient's cancer care.

Conclusions

Deep learning technology can enhance cancer detection, clinical workflows, and patient care. Challenges may arise during model development. The deep learning lifecycle involves data preprocessing, model development, and clinical implementation. Achieving health equity requires including diverse patient groups and eliminating bias during implementation. While model development is integral, most articles focus on the pre-deployment phase. Future longitudinal studies are crucial for validating models in real-world settings post-deployment. A collaborative approach among computational pathologists, technologists, industry, and healthcare providers is essential for driving adoption in clinical settings.

Evaluation of liver regeneration after hemi-hepatectomy by combining computed tomography and post-operative liver function

Background

Accurate evaluation of postoperative liver regeneration is essential to prevent postoperative liver failure.

Aims

To analyze the predictors that affect liver regeneration after hemi-hepatectomy.

Method

Patients who underwent hemi-hepatectomy in Hangzhou First People's Hospital and Hangzhou Shulan Hospital from January 2016 to December 2021 were enrolled in this study. The regeneration index (RI) was calculated by the following equation: RI = [(postoperative total liver volume {TLVpost} - future liver remnant volume {FLRV}/FLRV] × 100 %. Hepatic dysfunction was defined according to the "TBilpeak>7" standard, which was interpreted as (peak) total bilirubin (TBil) >7.0 mg/dL. Good liver regeneration was defined solely when the RI surpassed the median with hepatic dysfunction. Logistic regression analyses were performed to estimate prognostic factors affecting liver regeneration.

Result

A total of 153 patients were enrolled, with 33 in the benign group and 120 patients in the malignant group. In the entire study population, FLRV% [OR 4.087 (1.405-11.889), P = 0.010], international normalized ratio (INR) [OR 2.763 (95%CI, 1.008-7.577), P = 0.048] and TBil [OR 2.592 (95%CI, 1.177-5.710), P = 0.018] were independent prognostic factors associated with liver regeneration. In the benign group, only the computed tomography (CT) parameter FLRV% [OR, 11.700 (95%CI, 1.265-108.200), P = 0.030] predicted regeneration. In the malignant group, parenchymal hepatic resection rate (PHRR%) [OR 0.141 (95%CI, 0.040-0.499), P = 0.002] and TBil [OR 3.384 (95%CI, 1.377-8.319), P = 0.008] were independent prognostic factors.

Conclusion

FLRV%, PHRR%, TBil and INR were predictive factors associated with liver regeneration.

Artificial intelligence in cancer immunotherapy: Applications in neoantigen recognition, antibody design and immunotherapy response prediction

Cancer immunotherapy is a method of controlling and eliminating tumors by reactivating the body's cancer-immunity cycle and restoring its antitumor immune response. The increased availability of data, combined with advancements in high-performance computing and innovative artificial intelligence (AI) technology, has resulted in a rise in the use of AI in oncology research. State-of-the-art AI models for functional classification and prediction in immunotherapy research are increasingly used to support laboratory-based experiments. This review offers a glimpse of the current AI applications in immunotherapy, including neoantigen recognition, antibody design, and prediction of immunotherapy response. Advancing in this direction will result in more robust predictive models for developing better targets, drugs, and treatments, and these advancements will eventually make their way into the clinical setting, pushing AI forward in the field of precision oncology.

Development of pathological reconstructed high-resolution images using artificial intelligence based on whole slide image

Pathology plays a very important role in cancer diagnosis. The rapid development of digital pathology (DP) based on whole slide image (WSI) has led to many improvements in computer‐assisted diagnosis by artificial intelligence. The common digitization strategy is to scan the pathology slice with 20× or 40× objective, and the 40× objective requires excessive storage space and transmission time, which are significant negative factors in the popularization of DP. In this article, we present a novel reconstructed high‐resolution (HR) process based on deep learning to switch 20 × WSI to 40 × without the loss of whole and local features. Furthermore, we collected the WSI data of 100 uterine leiomyosarcomas and 100 adult granulosa cell tumors to test our reconstructed HR process. We tested the reconstructed HR WSI by the peak signal‐to‐noise ratio, structural similarity, and the blind/reject image spatial quality evaluator, which were 42.03, 0.99, and 49.22, respectively. Subsequently, we confirmed the consistency between the actual and our reconstructed HR images. The testing results indicate that the reconstructed HR imaging is a reliable method for the digital slides of a variety of tumors and can be available on a large scale in clinical pathology as an innovative technique.