Digital Pathology Images Research Articles

QuPath Analysis for CD30+ Cutaneous T-Cell Lymphoma.

Mycosis fungoides is the most common subtype of cutaneous T-cell lymphoma, in which the expression of cluster of differentiation 30 (CD30)+ subtype can now be treated with the CD30 antibody conjugate brentuximab vedotin. Diagnostic methods are based on immunohistochemical (IHC) staining followed by manual assessment by pathologists, which is always a subjective calculation. QuPath, an open-source software for digital pathology image analysis, satisfies the requirements of objective approaches. Ten samples from mycosis fungoides patients with CD30 expression at different stages were stained for CD3 and CD30 by IHC staining, scanned, and quantitative analysis was performed using QuPath (version 2.1). Each slide was independently assessed by 3 board-certified dermatopathologists. Individual estimates for CD30+/CD3+ cells varied among the individual histopathologists (mean coefficient of variation, 0.46; range, 0-0.78). QuPath analysis showed excellent separation between the positively stained cells for CD3 and CD30 IHC and other cells and tissue structures, although the results correlated strongly with the respective mean estimates of the 3 histopathologists (Pearson-R 0.93). The results show a high interobserver variability evaluation of IHC markers, although quantitative image analysis offer a significant advantage for comparison. This is not only relevant for clinical routine but also especially critical in therapeutic studies addressing targeted molecules.

Predicting origin for bone metastatic cancer using deep learning-based pathology.

Predicting origin for bone metastatic cancer using deep learning-based pathology.

Awareness of Digital Imaging/Telepathology Among Dental Post Graduate Students: A Survey

Background: The collection, storage and sharing of pathological data among oral pathologists to this day is done mostly by physical transfer of slides and reports. Telepathology is a form of communication between medical professionals that includes the transmission of pathology images or testing data. Aim: The aim of the study is to assess the knowledge, attitude and perception of digital pathology/telepathology among dental post graduate students. Materials and Methods: A cross-sectional study consisting of a self constructed survey was distributed through an online portal to dental post graduate students. The survey focused on the type of respondent, knowledge of microscopic digital photography, current usage, strengths and weaknesses and perceived future direction of digital imaging in the pathology laboratory. Results: A total of 102 responses were collected. The overall response rate was 100%. 56.9% of dental post graduate students are aware of the term “Digital Pathology” or “Telepathology”. 11.8% of dental post graduate students use digital pathology in their routine practice. 20.6 % of dental post graduate students send specimens once a year. 75.5% of dental post graduate students believe that “Digital Pathology/Telepathology” can be effective for research purposes. 62.7% of dental post graduate students believe that digital pathology imaging provides faster diagnosis. 75.5% of dental post graduate students believe that digital pathology imaging has high equipment cost. Comparison between the age of the respondents and their awareness of the term Digital Imaging/Telepathology and its scope in India stated that dental post graduate students of age group between 21-30 years were aware of the term and believed it has scope in India, but the results were insignificant (p>0.05). Conclusion: Improved knowledge and acceptance of digital pathology and telepathology throughout the dentistry community could facilitate the communication and cooperation necessary to realize the type of initiatives capable of advancing the importance of pathologists in the changing healthcare environment.

Federating Medical Deep Learning Models from Private Jupyter Notebooks to Distributed Institutions

Deep learning-based algorithms have led to tremendous progress over the last years, but they face a bottleneck as their optimal development highly relies on access to large datasets. To mitigate this limitation, cross-silo federated learning has emerged as a way to train collaborative models among multiple institutions without having to share the raw data used for model training. However, although artificial intelligence experts have the expertise to develop state-of-the-art models and actively share their code through notebook environments, implementing a federated learning system in real-world applications entails significant engineering and deployment efforts. To reduce the complexity of federation setups and bridge the gap between federated learning and notebook users, this paper introduces a solution that leverages the Jupyter environment as part of the federated learning pipeline and simplifies its automation, the Notebook Federator. The feasibility of this approach is then demonstrated with a collaborative model solving a digital pathology image analysis task in which the federated model reaches an accuracy of 0.8633 on the test set, as compared to the centralized configurations for each institution obtaining 0.7881, 0.6514, and 0.8096, respectively. As a fast and reproducible tool, the proposed solution enables the deployment of a cross-country federated environment in only a few minutes.

Artificial intelligence assists precision medicine in cancer treatment.

Cancer is a major medical problem worldwide. Due to its high heterogeneity, the use of the same drugs or surgical methods in patients with the same tumor may have different curative effects, leading to the need for more accurate treatment methods for tumors and personalized treatments for patients. The precise treatment of tumors is essential, which renders obtaining an in-depth understanding of the changes that tumors undergo urgent, including changes in their genes, proteins and cancer cell phenotypes, in order to develop targeted treatment strategies for patients. Artificial intelligence (AI) based on big data can extract the hidden patterns, important information, and corresponding knowledge behind the enormous amount of data. For example, the ML and deep learning of subsets of AI can be used to mine the deep-level information in genomics, transcriptomics, proteomics, radiomics, digital pathological images, and other data, which can make clinicians synthetically and comprehensively understand tumors. In addition, AI can find new biomarkers from data to assist tumor screening, detection, diagnosis, treatment and prognosis prediction, so as to providing the best treatment for individual patients and improving their clinical outcomes.

An accurate prediction of the origin for bone metastatic cancer using deep learning on digital pathological images.

Determining the origin of bone metastatic cancer (OBMC) is of great significance to clinical therapeutics. It is challenging for pathologists to determine the OBMC with limited clinical information and bone biopsy. We designed a regional multiple-instance learning algorithm to predict the OBMC based on hematoxylin-eosin (H&E) staining slides alone. We collected 1041 cases from eight different hospitals and labeled 26,431 regions of interest to train the model. The performance of the model was assessed by ten-fold cross validation and external validation. Under the guidance of top3 predictions, we conducted an IHC test on 175 cases of unknown origins to compare the consistency of the results predicted by the model and indicated by the IHC markers. We also applied the model to identify whether there was tumor or not in a region, as well as distinguishing squamous cell carcinoma, adenocarcinoma, and neuroendocrine tumor. In the within-cohort, our model achieved a top1-accuracy of 91.35% and a top3-accuracy of 97.75%. In the external cohort, our model displayed a good generalizability with a top3-accuracy of 97.44%. The top1 consistency between the results of the model and the immunohistochemistry markers was 83.90% and the top3 consistency was 94.33%. The model obtained an accuracy of 98.98% to identify whether there was tumor or not and an accuracy of 93.85% to differentiate three types of cancers. Our model demonstrated good performance to predict the OBMC from routine histology and had great potential for assisting pathologists with determining the OBMC accurately. National Science Foundation of China (61875102 and 61975089), Natural Science Foundation of Guangdong province (2021A15-15012379 and 2022A1515 012550), Science and Technology Research Program of Shenzhen City (JCYJ20200109110606054 and WDZC20200821141349001), and Tsinghua University Spring Breeze Fund (2020Z99CFZ023).

Shortcomings and areas for improvement in digital pathology image segmentation challenges.

Digital pathology image analysis challenges have been organised regularly since 2010, often with events hosted at major conferences and results published in high-impact journals. These challenges mobilise a lot of energy from organisers, participants, and expert annotators (especially for image segmentation challenges). This study reviews image segmentation challenges in digital pathology and the top-ranked methods, with a particular focus on how reference annotations are generated and how the methods' predictions are evaluated. We found important shortcomings in the handling of inter-expert disagreement and the relevance of the evaluation process chosen. We also noted key problems with the quality control of various challenge elements that can lead to uncertainties in the published results. Our findings show the importance of greatly increasing transparency in the reporting of challenge results, and the need to make publicly available the evaluation codes, test set annotations and participants' predictions. The aim is to properly ensure the reproducibility and interpretation of the results and to increase the potential for exploitation of the substantial work done in these challenges.

FRE-Net: Full-region enhanced network for nuclei segmentation in histopathology images

Accurate nuclei segmentation is a critical step for physicians to achieve essential information about a patient’s disease through digital pathology images, enabling an effective diagnosis and evaluation of subsequent treatments. Since pathology images contain many nuclei, manual segmentation is time-consuming and error-prone. Therefore, developing a precise and automatic method for nuclei segmentation is urgent. This paper proposes a novel multi-task segmentation network that incorporates background and contour segmentation into the nuclei segmentation method and produces more accurate segmentation results. The convolution and attention modules are merged with the model to increase its global focus and enhance good segmentation results indirectly. We propose a reverse feature enhance module for contour extraction that facilitates feature integration between auxiliary tasks. The multi-feature fusion module is embedded in the final decoding branch to use different levels of features from auxiliary segmentation branches with varying concerns. We evaluate the proposed method on four challenging nuclei segmentation datasets. The proposed method achieves excellent performance on all four datasets. We found that the Dice coefficient reached 0.8563±0.0323, 0.8183±0.0383, 0.9222±0.0216, and 0.9220±0.0602 on the TNBC, MoNuSeg, KMC, and Glas. Our method produces better boundary accuracy and less sticking than other end-to-end segmentation methods. The results show that our method can perform better than other proposed state-of-the-art methods.

A micro-hyperspectral image classification method of gallbladder cancer based on multi-scale fusion attention mechanism

A micro-hyperspectral image classification method of gallbladder cancer based on multi-scale fusion attention mechanism

A semi-supervised multi-task learning framework for cancer classification with weak annotation in whole-slide images.

Cancer region detection (CRD) and subtyping are two fundamental tasks in digital pathology image analysis. The development of data-driven models for CRD and subtyping on whole-slide images(WSIs) would mitigate the burden of pathologists and improve their accuracy in diagnosis. However, the existing models are facing two major limitations. Firstly, they typically require large-scale datasets with precise annotations, which contradicts with the original intention of reducing labor effort. Secondly, for the subtyping task, the non-cancerous regions are treated as the same as cancerous regions within a WSI, which confuses a subtyping model in its training process. To tackle the latter limitation, the previous research proposed to perform CRD first for ruling out the non-cancerous region, then train a subtyping model based on the remaining cancerous patches. However, separately training ignores the interaction of these two tasks, also leads to propagating the error of the CRD task to the subtyping task. To address these issues and concurrently improve the performance on both CRD and subtyping tasks, we propose a semi-supervised multi-task learning(MTL) framework for cancer classification. Our framework consists of a backbone feature extractor, two task-specific classifiers, and a weight control mechanism. The backbone feature extractor is shared by two task-specific classifiers, such that the interaction of CRD and subtyping tasks can be captured. The weight control mechanism preserves the sequential relationship of these two tasks and guarantees the error back-propagation from the subtyping task to the CRD task under the MTL framework. We train the overall framework in a semi-supervised setting, where datasets only involve small quantities of annotations produced by our minimal point-based(min-point) annotation strategy. Extensive experiments on four large datasets with different cancer types demonstrate the effectiveness of the proposed framework in both accuracy and generalization.

Tumor-identification method for predicting recurrence of early-stage lung adenocarcinoma using digital pathology images by machine learning

Lung cancer is one of the cancers with the highest morbidity and mortality in the world. Recurrence often occurs even after complete resection of early-stage lung cancer, and prediction of recurrence after resection is clinically important. However, the pathological characteristics of the recurrence of pathological stage IB lung adenocarcinoma (LAIB) have not yet been elucidated. Therefore, the problem is what type of histological image of lung adenocarcinoma recurs, and it is important to examine the histological image of recurrence. We attempted to predict recurrence of early lung adenocarcinoma after resection on the basis of digital pathological images of hematoxylin and eosin-stained specimens and machine learning applying a convolutional neural network. We constructed a model that extracts the features of two-color spaces and a switching model that automatically switches between our extraction model and one that extracts the features of one-color space for each image. We then developed a tumor-identification method for predicting the presence or absence of LAIB recurrence using these models. We conducted an experiment involving 55 patients with LAIB who underwent surgical resection to evaluate the proposed method. The proposed method determined LAIB recurrence with an accuracy of 84.8%. The use of digital pathology and machine learning can be used for highly accurate prediction of LAIB recurrence after surgical resection. The proposed method has the potential for objective postoperative follow-up observation.

Diagnostic and Prognostic Deep Learning Applications for Histological Assessment of Cutaneous Melanoma.

Melanoma is among the most devastating human malignancies. Accurate diagnosis and prognosis are essential to offer optimal treatment. Histopathology is the gold standard for establishing melanoma diagnosis and prognostic features. However, discrepancies often exist between pathologists, and analysis is costly and time-consuming. Deep-learning algorithms are deployed to improve melanoma diagnosis and prognostication from histological images of melanoma. In recent years, the development of these machine-learning tools has accelerated, and machine learning is poised to become a clinical tool to aid melanoma histology. Nevertheless, a review of the advances in machine learning in melanoma histology was lacking. We performed a comprehensive literature search to provide a complete overview of the recent advances in machine learning in the assessment of melanoma based on hematoxylin eosin digital pathology images. In our work, we review 37 recent publications, compare the methods and performance of the reviewed studies, and highlight the variety of promising machine-learning applications in melanoma histology.

Artificial Intelligence-Based Prediction of Recurrence after Curative Resection for Colorectal Cancer from Digital Pathological Images.

To develop an artificial intelligence-based model to predict recurrence after curative resection for stage I-III colorectal cancer from digitized pathological slides. In this retrospective study, 471 consecutive patients who underwent curative resection for stage I-III colorectal cancer at our institution from 2004 to 2015 were enrolled, and 512 randomly selected tiles from digitally scanned images of hematoxylin and eosin-stained tumor tissue sections were used to train a convolutional neural network. Five-fold cross-validation was used to validate the model. The association between recurrence and the model's output scores were analyzed in the test cohorts. The area under the receiver operating characteristic curve of the cross-validation was 0.7245 [95% confidence interval (CI) 0.6707-0.7783; P < 0.0001]. The score successfully classified patients into those with better and worse recurrence free survival (P < 0.0001). Multivariate analysis revealed that a high score was significantly associated with worse recurrence free survival [odds ratio (OR) 1.857; 95% CI 1.248-2.805; P = 0.0021], which was independent from other predictive factors: male sex (P = 0.0238), rectal cancer (P = 0.0396), preoperative abnormal carcinoembryonic antigen (CEA) level (P = 0.0216), pathological T3/T4 stage (P = 0.0162), and pathological positive lymph node metastasis (P < 0.0001). The artificial intelligence-based prediction model discriminated patients with a high risk of recurrence. This approach could help decision-makers consider the benefits of adjuvant chemotherapy.

Expectation-maximization algorithm leads to domain adaptation for a perineural invasion and nerve extraction task in whole slide digital pathology images.

In addition to lymphatic and vascular channels, tumor cells can also spread via nerves, i.e., perineural invasion (PNI). PNI serves as an independent prognostic indicator in many malignancies. As a result, identifying and determining the extent of PNI is an important yet extremely tedious task in surgical pathology. In this work, we present a computational approach to extract nerves and PNI from whole slide histopathology images. We make manual annotations on selected prostate cancer slides once but then apply the trained model for nerve segmentation to both prostate cancer slides and head and neck cancer slides. For the purpose of multi-domain learning/prediction and investigation on the generalization capability of deep neural network, an expectation-maximization (EM)-based domain adaptation approach is proposed to improve the segmentation performance, in particular for the head and neck cancer slides. Experiments are conducted to demonstrate the segmentation performances. The average Dice coefficient for prostate cancer slides is 0.82 and 0.79 for head and neck cancer slides. Comparisons are then made for segmentations with and without the proposed EM-based domain adaptation on prostate cancer and head and neck cancer whole slide histopathology images from The Cancer Genome Atlas (TCGA) database and significant improvements are observed.

Machine learning in renal pathology.

When assessing kidney biopsies, pathologists use light microscopy, immunofluorescence, and electron microscopy to describe and diagnose glomerular lesions and diseases. These methods can be laborious, costly, fraught with inter-observer variability, and can have delays in turn-around time. Thus, computational approaches can be designed as screening and/or diagnostic tools, potentially relieving pathologist time, healthcare resources, while also having the ability to identify novel biomarkers, including subvisual features. Here, we implement our recently published biomarker feature extraction (BFE) model along with 3 pre-trained deep learning models (VGG16, VGG19, and InceptionV3) to diagnose 3 glomerular diseases using PAS-stained digital pathology images alone. The BFE model extracts a panel of 233 explainable features related to underlying pathology, which are subsequently narrowed down to 10 morphological and microstructural texture features for classification with a linear discriminant analysis machine learning classifier. 45 patient renal biopsies (371 glomeruli) from minimal change disease (MCD), membranous nephropathy (MN), and thin-basement membrane nephropathy (TBMN) were split into training/validation and held out sets. For the 3 deep learningmodels, data augmentation and Grad-CAM were used for better performance and interpretability. The BFE model showed glomerular validation accuracy of 67.6% and testing accuracy of 76.8%. All deep learning approaches had higher validation accuracies (most for VGG16 at 78.5%) but lower testing accuracies. The highest testing accuracy at the glomerular level was VGG16 at 71.9%, while at the patient-level was InceptionV3 at 73.3%. The results highlight the potential of both traditional machine learning and deep learning-based approaches for kidney biopsy evaluation.

Telepathology: A Transforming Practice for the Efficient, Safe, and Best Patient Care at the Regional Veteran Affairs Medical Center

Abstract Introduction/Objective Telepathology is the practice of remote pathology using telecommunication links to enable transmission of digital pathology images for primary diagnosis, quality assurance, education, research, or second opinion diagnoses. Once properly validated and implemented, telepathology has great potential to transform the practice of pathology by increasing productivity, savings in costs, improving the quality of care, and participating in innovations to improve future care. These improvements would help ensure equity throughout all remote sites that may be served by the central regional Veteran Affairs Medical Center (VAMC), even with the long-term declining number of pathologists in the country. The validation and implementation of telepathology has been further refined by recent College of American Pathologist (CAP) and American Telemedicine Association (ATA) clinical guidelines and recommendations for the validation and implementation of telepathology, but otherwise the literature is sparse for recent telepathology validation and implementation after the promulgation of these guidelines and recommendations in the Veteran Affairs setting. Methods/Case Report An initial narrative literature review was conducted to ascertain the current clinical guidelines and practices of telepathology to ensure that practices during validation and implementation meet and exceed the standard of care. Furthermore, meetings including the appropriate stakeholders in information technology, privacy, facilities, and pathology were held. The telepathology platform from Leica Biosystems Aperio (Deer Park IL) was brought in house at the regional VAMC’s pathology department for validation and future implementation. Results (if a Case Study enter NA) Planning included following all appropriate guidelines from ATA and CAP regarding telepathology to ensure that the program met the standard of care. The arrival of the instrument was installed successfully within the pathology. Conclusion The installation of telepathology for use at a regional VAMC represents an important step to transform pathology to increase access, including even remote access, to pathology resources and subspecialty expertise throughout the Veteran Affairs Medical System.

Abstract 15640: Histomic-Based Clot Structure Quantification for Prediction of Ischemic Stroke Etiology

Introduction: Determining stroke etiology is paramount to clinical management and prevention of recurrent strokes. Although stroke patients undergo extensive post-treatment work-ups, 30-40% of cases remain cryptogenic. Hypothesis: Engineered histomic features from digital pathology images of stroke blood clots collected during mechanical thrombectomy (MT) can be used to delineate stroke etiology. Methods: For clots retrieved from patients undergoing MT, etiology was determined by the trial of Trial of Org 10172 in Acute Stroke Treatment (TOAST) score. After sectioning and H&amp;E staining, clot components (red blood cells-RBCs, fibrin-platelet regions-FP, and white blood cells-WBCs) were segmented from whole slide images. Histomic features were engineered to capture the structural distribution of RBC/FP regions throughout the clot. To measure clot WBC diversity, WBC instances were clustered into WBC “classes” based on texture, and summarized as a class frequency distribution (CFD). The three most significant RBC/FP and WBC features between large artery atherosclerosis (LAA) and cardioembolic (CE) cases were used to train a complement Naïve Bayes (CNB) model, which was then implemented to predict the etiology of cryptogenic cases. Results: In our data (n=53), 31 clots were CE, 8 were LAA, 4 were from strokes of other determined etiology, and 10 were cryptogenic. 17 significant RBC/FP features and 3 significant WBC CFDs were different between CE and LAA. The trained CNB model accurately classified CE vs. LAA with a validation AUC of 0.87±0.03, exhibiting superior performance to a model trained using common clot percent cellular composition metrics (AUC=0.69±0.16). Furthermore, we showed the potential to classify cryptogenic cases as CE or LAA. Conclusions: This first-of-its-kind, biologically-informed clot histomic pipeline captured significant information that may augment clinical and laboratory features used in stroke etiology classification.

Survival Prediction of Glioma Patients from Integrated Radiology and Pathology Images Using Machine Learning Ensemble Regression Methods

Gliomas are tumors of the central nervous system, which usually start within the glial cells of the brain or the spinal cord. These are extremely migratory and diffusive tumors, which quickly expand to the surrounding regions in the brain. There are different grades of gliomas, hinting about their growth patterns and aggressiveness and potential response to the treatment. As part of routine clinical procedure for gliomas, both radiology images (rad), such as multiparametric MR images, and digital pathology images (path) from tissue samples are acquired. Each of these data streams are used separately for prediction of the survival outcome of gliomas, however, these images provide complimentary information, which can be used in an integrated way for better prediction. There is a need to develop an image-based method that can utilise the information extracted from these imaging sequences in a synergistic way to predict patients’ outcome and to potentially assist in building comprehensive and patient-centric treatment plans. The objective of this study is to improve survival prediction outcomes of gliomas by integrating radiology and pathology imaging. Multiparametric magnetic resonance imaging (MRI), rad images, and path images of glioma patients were acquired from The Cancer Imaging Archive. Quantitative imaging features were extracted from tumor regions in rad and path images. The features were given as input to an ensemble regression machine learning pipeline, including support vector regression, AdaBoost, gradient boost, and random forest. The performance of the model was evaluated in several configurations, including leave-one-out, five-fold cross-validation, and split-train-test. Moreover, the quantitative performance evaluations were conducted separately in the complete cohort (n = 171), high-grade gliomas (HGGs), n = 75, and low-grade gliomas (LGGs), n = 96. The combined rad and path features outperformed individual feature types in all the configurations and datasets. In leave-one-out configuration, the model comprising both rad and path features was successfully validated on the complete dataset comprising HGFs and LGGs (R=0.84 p=2.2×10−16). The Kaplan–Meier curves generated on the predictions of the proposed model yielded a hazard ratio of 3.314 [95%CI:1.718−6.394], log−rank(P)=2×10−4 on combined rad and path features. Conclusion: The proposed approach emphasizes radiology experts and pathology experts’ clinical workflows by creating prognosticators upon ‘rad’ radiology images and digital pathology ‘path’ images independently, as well as combining the power of both, also through delivering integrated analysis, that can contribute to a collaborative attempt between different departments for administration of patients with gliomas.

The relationship between radiomics and pathomics in Glioblastoma patients: Preliminary results from a cross-scale association study.

Glioblastoma multiforme (GBM) typically exhibits substantial intratumoral heterogeneity at both microscopic and radiological resolution scales. Diffusion Weighted Imaging (DWI) and dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) are two functional MRI techniques that are commonly employed in clinic for the assessment of GBM tumor characteristics. This work presents initial results aiming at determining if radiomics features extracted from preoperative ADC maps and post-contrast T1 (T1C) images are associated with pathomic features arising from H&E digitized pathology images. 48 patients from the public available CPTAC-GBM database, for which both radiology and pathology images were available, were involved in the study. 91 radiomics features were extracted from ADC maps and post-contrast T1 images using PyRadiomics. 65 pathomic features were extracted from cell detection measurements from H&E images. Moreover, 91 features were extracted from cell density maps of H&E images at four different resolutions. Radiopathomic associations were evaluated by means of Spearman’s correlation (ρ) and factor analysis. p values were adjusted for multiple correlations by using a false discovery rate adjustment. Significant cross-scale associations were identified between pathomics and ADC, both considering features (n = 186, 0.45 < ρ < 0.74 in absolute value) and factors (n = 5, 0.48 < ρ < 0.54 in absolute value). Significant but fewer ρ values were found concerning the association between pathomics and radiomics features (n = 53, 0.5 < ρ < 0.65 in absolute value) and factors (n = 2, ρ = 0.63 and ρ = 0.53 in absolute value). The results of this study suggest that cross-scale associations may exist between digital pathology and ADC and T1C imaging. This can be useful not only to improve the knowledge concerning GBM intratumoral heterogeneity, but also to strengthen the role of radiomics approach and its validation in clinical practice as “virtual biopsy”, introducing new insights for omics integration toward a personalized medicine approach.

A multi-task deep learning framework for perineural invasion recognition in gastric cancer whole slide images

Perineural invasion (PNI) is the process of neoplastic invasion of the nerves and is a prognostic factor in gastric cancer. However, such examination is a labor-intensive task in high-resolution digital pathological images. To alleviate this problem, we propose a multi-task deep learning-based framework to highlight diagnostically significant PNI regions in whole slide images (WSIs) of human gastric cancer tissue sections. The proposed framework includes a gastric cancer segmentation model, neural detection model, and PNI decision-making module, which realizes the segmentation of the gastric cancer region while completing the task of identifying PNI. Adequate comparative experiments were performed on our own gastric cancer PNI dataset called GC-PNI. Experiments have shown that our proposed model can effectively diagnose PNI with a high sensitivity of 0.972 and a specificity of 0.933, illustrating the potential of this practical application.