Histopathological Imaging Data Research Articles

Exploring the interplay between colorectal cancer subtypes genomic variants and cellular morphology: A deep-learning approach.

Molecular subtypes of colorectal cancer (CRC) significantly influence treatment decisions. While convolutional neural networks (CNNs) have recently been introduced for automated CRC subtype identification using H&E stained histopathological images, the correlation between CRC subtype genomic variants and their corresponding cellular morphology expressed by their imaging phenotypes is yet to be fully explored. The goal of this study was to determine such correlations by incorporating genomic variants in CNN models for CRC subtype classification from H&E images. We utilized the publicly available TCGA-CRC-DX dataset, which comprises whole slide images from 360 CRC-diagnosed patients (260 for training and 100 for testing). This dataset also provides information on CRC subtype classifications and genomic variations. We trained CNN models for CRC subtype classification that account for potential correlation between genomic variations within CRC subtypes and their corresponding cellular morphology patterns. We assessed the interplay between CRC subtypes' genomic variations and cellular morphology patterns by evaluating the CRC subtype classification accuracy of the different models in a stratified 5-fold cross-validation experimental setup using the area under the ROC curve (AUROC) and average precision (AP) as the performance metrics. The CNN models that account for potential correlation between genomic variations within CRC subtypes and their cellular morphology pattern achieved superior accuracy compared to the baseline CNN classification model that does not account for genomic variations when using either single-nucleotide-polymorphism (SNP) molecular features (AUROC: 0.824±0.02 vs. 0.761±0.04, p<0.05, AP: 0.652±0.06 vs. 0.58±0.08) or CpG-Island methylation phenotype (CIMP) molecular features (AUROC: 0.834±0.01 vs. 0.787±0.03, p<0.05, AP: 0.687±0.02 vs. 0.64±0.05). Combining the CNN models account for variations in CIMP and SNP further improved classification accuracy (AUROC: 0.847±0.01 vs. 0.787±0.03, p = 0.01, AP: 0.68±0.02 vs. 0.64±0.05). The improved accuracy of CNN models for CRC subtype classification that account for potential correlation between genomic variations within CRC subtypes and their corresponding cellular morphology as expressed by H&E imaging phenotypes may elucidate the biological cues impacting cancer histopathological imaging phenotypes. Moreover, considering CRC subtypes genomic variations has the potential to improve the accuracy of deep-learning models in discerning cancer subtype from histopathological imaging data.

Self-Supervised Representation Distribution Learning for Reliable Data Augmentation in Histopathology WSI Classification.

Multiple instance learning (MIL) based whole slide image (WSI) classification is often carried out on the representations of patches extracted from WSI with a pre-trained patch encoder. The performance of classification relies on both patch-level representation learning and MIL classifier training. Most MIL methods utilize a frozen model pre-trained on ImageNet or a model trained with self-supervised learning on histopathology image dataset to extract patch image representations and then fix these representations in the training of the MIL classifiers for efficiency consideration. However, the invariance of representations cannot meet the diversity requirement for training a robust MIL classifier, which has significantly limited the performance of the WSI classification. In this paper, we propose a Self-Supervised Representation Distribution Learning framework (SSRDL) for patch-level representation learning with an online representation sampling strategy (ORS) for both patch feature extraction and WSI-level data augmentation. The proposed method was evaluated on three datasets under three MIL frameworks. The experimental results have demonstrated that the proposed method achieves the best performance in histopathology image representation learning and data augmentation and outperforms state-of-the-art methods under different WSI classification frameworks. The code is available at https://github.com/lazytkm/SSRDL.

Computer-aided diagnosis system for grading brain tumor using histopathology images based on color and texture features

BackgroundCancer pathology shows disease development and associated molecular features. It provides extensive phenotypic information that is cancer-predictive and has potential implications for planning treatment. Based on the exceptional performance of computational approaches in the field of digital pathogenic, the use of rich phenotypic information in digital pathology images has enabled us to identify low-level gliomas (LGG) from high-grade gliomas (HGG). Because the differences between the textures are so slight, utilizing just one feature or a small number of features produces poor categorization results.MethodsIn this work, multiple feature extraction methods that can extract distinct features from the texture of histopathology image data are used to compare the classification outcomes. The successful feature extraction algorithms GLCM, LBP, multi-LBGLCM, GLRLM, color moment features, and RSHD have been chosen in this paper. LBP and GLCM algorithms are combined to create LBGLCM. The LBGLCM feature extraction approach is extended in this study to multiple scales using an image pyramid, which is defined by sampling the image both in space and scale. The preprocessing stage is first used to enhance the contrast of the images and remove noise and illumination effects. The feature extraction stage is then carried out to extract several important features (texture and color) from histopathology images. Third, the feature fusion and reduction step is put into practice to decrease the number of features that are processed, reducing the computation time of the suggested system. The classification stage is created at the end to categorize various brain cancer grades. We performed our analysis on the 821 whole-slide pathology images from glioma patients in the Cancer Genome Atlas (TCGA) dataset. Two types of brain cancer are included in the dataset: GBM and LGG (grades II and III). 506 GBM images and 315 LGG images are included in our analysis, guaranteeing representation of various tumor grades and histopathological features.ResultsThe fusion of textural and color characteristics was validated in the glioma patients using the 10-fold cross-validation technique with an accuracy equals to 95.8%, sensitivity equals to 96.4%, DSC equals to 96.7%, and specificity equals to 97.1%. The combination of the color and texture characteristics produced significantly better accuracy, which supported their synergistic significance in the predictive model. The result indicates that the textural characteristics can be an objective, accurate, and comprehensive glioma prediction when paired with conventional imagery.ConclusionThe results outperform current approaches for identifying LGG from HGG and provide competitive performance in classifying four categories of glioma in the literature. The proposed model can help stratify patients in clinical studies, choose patients for targeted therapy, and customize specific treatment schedules.

Classification of the Stages of Nonalcoholic Steatohepatitis via Federated General Visual Representation Learning

ABSTRACTThe excess buildup of fat within a patient's liver, even in the absence of any previous alcohol consumption, defines nonalcoholic fatty liver disease (NAFLD). Accurate classification of NAFLD subtypes, especially nonalcoholic steatohepatitis (NASH), through histology biopsies is vital for risk stratification and patient management, including liver transplantation decisions. Therapeutic options targeting liver molecular pathways are being explored, as approved therapies for NASH/NAFLD are currently lacking. Deep learning‐driven pathological diagnosis holds promise for precise assessments, and the application of the federated learning version of the Big Transfer (BiT) model in this study represents a significant advancement in NAFLD/NASH classification and staging using histopathological imaging data. The AUC performance results of federated learning settings of “mainclass” and “multiclass” scenarios are obtained respectively as 0.9299 and 0.9582. The AUC results of each “subclass” are achieved as 0.9376, 0.9435, 0.9867, and 0.9618 respectively, for ballooning, inflammation, steatosis, and fibrosis. The effectiveness of the federated learning configurations in the scenarios categorized as “main‐class” and “multi‐class” is reflected in their respective accuracy (ACC) performance outcomes, which are recorded as 0.7650 and 0.6653. Furthermore, the ACC achievements for each specific “sub‐class” namely, ballooning, inflammation, steatosis, and fibrosis are 0.9016, 0.8568, 0.9203, and 0.8815, respectively.

Multimodal artificial intelligence models from baseline histopathology to predict prognosis in HR+ HER2- early breast cancer: Subgroup analysis.

101 Background: Prognostic assessment in HR+ HER2- early breast cancer (EBC) remains challenging given relatively low rates of disease progression. Modern artificial intelligence (AI)-based techniques have already provided substantial medical progress, particularly in prostate cancer. We have leveraged ArteraAI’s multimodal artificial intelligence (MMAI) platform to develop a research-level prognostic model in HR+ HER2- EBC, based on the WSG PlanB and ADAPT trials. Here, we quantify the value added by MMAI within clinically relevant subgroups. Methods: Histopathology image data was generated from pre-treatment breast biopsy and surgical hematoxylin and eosin (H&amp;E) slides from the WSG PlanB and ADAPT trials. Patients with available images and complete data (n=5259) were allocated (stratified by trial, randomization arm and distant recurrence (DR)) to development (60%) and validation (40%) cohorts. An MMAI-based model using image data combined with clinical prognostic variables (age, T and N stage, tumor size) was developed to predict risk of DR. Univariable and multivariable Fine-Gray models were used to assess performance in the validation cohorts; subdistribution hazard ratios (sHR) refer to validation cohorts and are reported per standard deviation increase of the model scores (image-alone or combined). Pre-specified prognostic subgroups for analysis were defined by nodal status, menopausal status, and central tumor grade. All statistical tests were 2-sided at .05 significance. Results: The trained MMAI score was significantly associated with risk of DR in the validation cohort (sHR [95% CI] = 2.3 [2.0-2.8]) as a whole and in all considered subgroups. The score remained significant (sHR [95%CI] = 2.2 [1.7-2.8]) after adjusting for clinical prognostic factors. Moreover, the MMAI image component alone had significant prognostic value (sHR [95%CI] = 1.6 [1.3 - 1.9]) in the validation cohort. Remarkably, the MMAI image component alone had significant prognostic value separately within the G2 and G3 sub-groups, with sHR of about 1.5 per standard deviation increase, and also in most of the other predefined clinical subgroups. Conclusions: Preliminary results from the current MMAI breast model provide evidence that ArteraAI MMAI technology can be leveraged for outcome prediction in HR+ HER2- EBC using H&amp;E-stained images to further personalize breast cancer management. The ability of image-only AI models to provide significant prognostic value within grade subgroups suggests that self-supervised AI has identified some novel image features with prognostic value beyond grade. To put the results into the clinical context, comprehensive validation analyses will be presented at the meeting.

DPA-P2PNet: Deformable Proposal-Aware P2PNet for Accurate Point-Based Cell Detection

Point-based cell detection (PCD), which pursues high-performance cell sensing under low-cost data annotation, has garnered increased attention in computational pathology community. Unlike mainstream PCD methods that rely on intermediate density map representations, the Point-to-Point network (P2PNet) has recently emerged as an end-to-end solution for PCD, demonstrating impressive cell detection accuracy and efficiency. Nevertheless, P2PNet is limited to decoding from a single-level feature map due to the scale-agnostic property of point proposals, which is insufficient to leverage multi-scale information. Moreover, the spatial distribution of pre-set point proposals is biased from that of cells, leading to inaccurate cell localization. To lift these limitations, we present DPA-P2PNet in this work. The proposed method directly extracts multi-scale features for decoding according to the coordinates of point proposals on hierarchical feature maps. On this basis, we further devise deformable point proposals to mitigate the positional bias between proposals and potential cells to promote cell localization. Inspired by practical pathological diagnosis that usually combines high-level tissue structure and low-level cell morphology for accurate cell classification, we propose a multi-field-of-view (mFoV) variant of DPA-P2PNet to accommodate additional large FoV images with tissue information as model input. Finally, we execute the first self-supervised pre-training on immunohistochemistry histopathology image data and evaluate the suitability of four representative self-supervised methods on the PCD task. Experimental results on three benchmarks and a large-scale and real-world interval dataset demonstrate the superiority of our proposed models over the state-of-the-art counterparts. Codes and pre-trained weights are available at https://github.com/windygoo/DPA-P2PNet.

Orbital Solitary Fibrous Tumors: A 20-Year Cases Series Study in a Single Center.

To investigate the clinical manifestations, imaging, pathology, and prognosis of orbital solitary fibrous tumors (OSFTs). In addition, the surgical incisions and the treatment outcomes were also evaluated. A total of 89 patients with pathologically confirmed primary OSFTs were enrolled onto this study. Clinical and histopathological characteristics, imaging data, treatment modalities, and follow-up time, including tumor recurrence and death, were documented. The outcome measures included overall survival and disease-free survival time. Among 89 eligible cases, the median age of presentation was 39 years (range: 5-80 years) at the initial diagnosis. The most common presenting symptom was painless proptosis (54, 60.67%), then palpable mass (31, 34.83%), swelling (29, 32.58%), and impaired ocular motility (27, 30.34%). Tumor-related severe impaired vision was found in 11 patients (12.36%), including no-light-perception blindness (6, 6.74%), light-perception (2, 2.25%), and hand-movement (1, 1.12%). The preoperative imaging (computed tomography and magnetic resonance imaging) accurate diagnostic rate of OSFTs was 17.98% (16, 95% CI: 0.098-0.261), and misdiagnosis rate was 25.84% (23, 95% CI: 0.166-0.351). Grossly intact masses were excised for 27 patients (30.34%). Among the 89 patients, 33 (37.08%) were recurrences, and the median of these recurrent patients' interval between the first and the last operation was 7.33 years (range: 0.12-29.69 years). In 81 patients with complete follow-up data, the median course of the disease was 9.64 years (range: 1.55-33.65 years) from the onset OSFT. The overall survival rate of the 81 patients was 93.83% with a median course of 8.48 years (range: 0.38-30.4 years) from diagnosis of OSFT, and the disease-free survival rate of 81 patients was 91.36% with a median follow-up of 4.76 years (range: 0.08-19.22 years) after the last surgery. Of all the 81 patients, 5 patients (6.17%) developed local recurrence, and 3 patients (3.70%) died from tumor-related diseases, including pulmonary metastasis (2, 2.47%) and complications from intracranial lesions (1, 1.23%). Ten patients (11.24%) received postoperational radiation therapy, including 125I seeds implantation (5, 6.17%) and external beam radiotherapy (5, 6.17%), and remained no recurrence. In this series, OSFTs showed long courses and easy recurrence. Although it was very important to choose a proper surgical incision for intact resection of OSFTs at the initial surgery to avoiding recurrence, preoperative imaging is of very limited use since it is not able to identify OSFTs effectively. Postoperative radiotherapy may be beneficial to reduce the recurrence of OSFTs with malignant pathologic features.

Multi-Modal Federated Learning for Cancer Staging over Non-IID Datasets with Unbalanced Modalities.

The use of machine learning (ML) for cancer staging through medical image analysis has gained substantial interest across medical disciplines. When accompanied by the innovative federated learning (FL) framework, ML techniques can further overcome privacy concerns related to patient data exposure. Given the frequent presence of diverse data modalities within patient records, leveraging FL in a multi-modal learning framework holds considerable promise for cancer staging. However, existing works on multi-modal FL often presume that all data-collecting institutions have access to all data modalities. This oversimplified approach neglects institutions that have access to only a portion of data modalities within the system. In this work, we introduce a novel FL architecture designed to accommodate not only the heterogeneity of data samples, but also the inherent heterogeneity/non-uniformity of data modalities across institutions. We shed light on the challenges associated with varying convergence speeds observed across different data modalities within our FL system. Subsequently, we propose a solution to tackle these challenges by devising a distributed gradient blending and proximity-aware client weighting strategy tailored for multi-modal FL. To show the superiority of our method, we conduct experiments using The Cancer Genome Atlas program (TCGA) datalake considering different cancer types and three modalities of data: mRNA sequences, histopathological image data, and clinical information. Our results further unveil the impact and severity of class-based vs type-based heterogeneity across institutions on the model performance, which widens the perspective to the notion of data heterogeneity in multi-modal FL literature.

Apparent diffusion coefficient histogram analysis for differentiating fibroblastic meningiomas from non-fibroblastic WHO grade 1 meningiomas

PurposeTo investigate the role of apparent diffusion coefficient (ADC) histogram analysis in differentiating fibroblastic meningiomas (FM) from non-fibroblastic WHO grade 1 meningiomas (nFM). MethodsThis retrospective study analyzed the histopathological and diagnostic imaging data of 220 patients with histopathologically confirmed FM and nFM. The whole tumors were delineated on axial ADC images, and histogram parameters (mean, variance, skewness, kurtosis, as well as the 1st, 10th, 50th, 90th, and 99th percentile ADC [ADCp1, ADCp10, ADCp50, ADCp90, and ADCp99, respectively]) were obtained. Multivariate logistic regression analysis was used to identify the most valuable variables for discriminating FM from nFM WHO grade 1 meningiomas, and their diagnostic efficacy in differentiating FM from nFM before surgery was assessed using receiver operating characteristic (ROC) curves. ResultsThe mean, variance, ADCp50, ADCp90, and ADCp99 of the FM group were all lower than those of the nFM group (P < 0.05), there was significant difference in location and sex (P < 0.05). Multivariate logistic regression showed ADCp99 (P < 0.001) and location (P = 0.007) were the most valuable parameters in the discrimination of FM and nFM WHO grade 1 meningiomas. The diagnostic efficacy was achieved an AUC of 0.817(95% CI, 0.759–0.866), the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value were 66.4%, 83.6%, 75.0%, 80.2%, and 71.3%, respectively. ConclusionADC histogram analysis is helpful in noninvasive differentiation of FM and nFM WHO grade 1 meningiomas, and combined ADCp99 and location have the best diagnostic efficacy.

Integrating Adam Optimizer to Enhance Efficiency of Transfer Learning Model for Diagnosing Cancers

Despite huge advancements in medical fields, diseases like cancer continue to plague people since we are still prone to them. The main purpose is to design a critical analysis of cancers, including breast, pancreatic, colon, skin, lung, and other cancers from the year 1990 to 2019, to identify some of the rising factors of both lung and colon cancers that cause unnecessary deaths. Thus, early detection on the other hand greatly improves the chances of survival rates in humans. At that place, Transfer Learning (TL) techniques have made significant improvements these days.&#x0D; Material and Methods: We herein implemented the exploratory data analysis (EDA) technique to analyze 29 types of cancer deaths worldwide since 1990th century. Between them, we have emphasized lung cancer (LC) and colon cancers (CC) for our study and taken 25,000 histopathology image data from the publicly available Kaggle repository. Further, we proposed a novel methodology using the EfficientNetB7 model defining each step to classify five types of lung and colon tissues (two benign and three malignant) from the histopathological images. Although, an algorithm has been designed that clearly describes the workflow of our proposed method. This experiment has been done through input_shape=(X, Y, 3) on the ‘ImageNet’ dataset, Adam optimizer, and accuracy metrics using Python 3.8.8 software on Jupyter 6.4.3 environment.&#x0D; Results: We evaluated the model progress using 50 epochs during the training phase, which resulted in more than 98% accuracy and less than 2% loss in both the training/ validation phases. Besides that, we designed a classification report that describes the performance of our model, where we achieved more than 98.07% accuracy score, 98% precision score, 98% of recall score, and 98% of f1-score. These results signify that implementing an optimization technique on the EfficientNetB7 model improves the overall performance as compared to 93.19% for LC 1 and 93.91% for CC 2 for the RestNet50 model.&#x0D; Conclusion: A novelty of this research specifies the methodology to predict LC and CC by applying the EfficientNetB7 model to the cancer dataset which might be beneficial for both doctors as well as healthcare firms.

Attention-based generative adversarial networks improve prognostic outcome prediction of cancer from multimodal data.

The prediction of prognostic outcome is critical for the development of efficient cancer therapeutics and potential personalized medicine. However, due to the heterogeneity and diversity of multimodal data of cancer, data integration and feature selection remain a challenge for prognostic outcome prediction. We proposed a deep learning method with generative adversarial network based on sequential channel-spatial attention modules (CSAM-GAN), a multimodal data integration and feature selection approach, for accomplishing prognostic stratification tasks in cancer. Sequential channel-spatial attention modules equipped with an encoder-decoder are applied for the input features of multimodal data to accurately refine selected features. A discriminator network was proposed to make the generator and discriminator learning in an adversarial way to accurately describe the complex heterogeneous information of multiple modal data. We conducted extensive experiments with various feature selection and classification methods and confirmed that the CSAM-GAN via the multilayer deep neural network (DNN) classifier outperformed these baseline methods on two different multimodal data sets with miRNA expression, mRNA expression and histopathological image data: lower-grade glioma and kidney renal clear cell carcinoma. The CSAM-GAN via the multilayer DNN classifier bridges the gap between heterogenous multimodal data and prognostic outcome prediction.

Synthetic Data Generation for Morphological Analyses of Histopathology Images with Deep Learning Models

In this study, we introduce a new synthetic data generation procedure for augmentation of histopathology image data. This is an extension to our previous research in which we proved the possibility to apply deep learning models for morphological analysis of tumor cells, trained on synthetic data only. The medical problem considered is related to the Ki-67 protein proliferation index calculation. We focused on the problem of cell counting in cell conglomerates, which are considered as structures composed of overlapping tumor cells. The lack of large and standardized data sets is a critical problem in medical image classification. Classical augmentation procedures are not sufficient. Therefore, in this research, we expanded our previous augmentation approach for histopathology images and we proved the possibility to apply it for a cell-counting problem.

A progressive phased attention model fused histopathology image features and gene features for lung cancer staging prediction.

Identifying the stage of lung cancer accurately from histopathology images and gene is very important for the diagnosis and treatment of lung cancer. Despite the substantial progress achieved by existing methods, it remains challenging due to large intra-class variances, and a high degree of inter-class similarities. In this paper, we propose a phased Multimodal Multi-scale Attention Model (MMAM) that predicts lung cancer stages using histopathology image data and gene data. The model consists of two phases. In Phase1, we propose a Staining Difference Elimination Network (SDEN) to eliminate staining differences between different histopathology images, In Phase2, it utilizes the image feature extractor provided by Phase1 to extract image features, and sends the multi-scale image features together with gene features into our Adaptive Enhanced Attention Fusion (AEAF) module for multimodal multi-scale features fusion to enable prediction of lung cancer staging. We evaluated the proposed MMAM on the TCGA lung cancer dataset, and achieved 88.51% AUC and 88.17% accuracy on classification prediction of lung cancer stages I, II, III, and IV. The method can help doctors diagnose the stage of lung cancer patients and can benefit from multimodal data.

Patient-level data meta-analysis of a multi-modal artificial intelligence (MMAI) prognostic biomarker in high-risk prostate cancer: Results from six NRG/RTOG phase III randomized trials.

299 Background: Recently, an MMAI prognostic biomarker, ArteraAI Prostate, was trained and validated in localized prostate cancer to more accurately risk stratify patients for multiple endpoints compared to NCCN risk groups (Esteva et al., 2022). Prognostication within an NCCN risk group remains clinically important given the multiple treatment decisions required within each risk group (e.g., radiotherapy dose or hormone therapy use). Herein, we validated the MMAI biomarker in high-risk prostate cancer where an increasing number of therapeutic decisions is required. Methods: This study leveraged histopathology image and clinical data from patients with at least one high-risk feature (HRF; cT3-cT4, Gleason 8-10, PSA &gt; 20 ng/mL, primary Gleason pattern 5) from six NRG/RTOG phase III randomized trials (n=1,088). Patients from two trials not part of the initial MMAI biomarker training/validation (RTOG 0521 [n=344] and 9902 [n=318]) and the MMAI validation cohort (RTOG 9202, 9408, 9413, and 9910 [n=426]) were included. Fine-Gray, cumulative incidence, and time dependent area under the curve (tdAUC) analyses were performed for time to distant metastasis (DM) and prostate cancer-specific mortality (PCSM) for standard clinicopathologic variables (age, PSA, Gleason score, T-stage, number of HRFs) and the MMAI model, as a continuous score (per standard deviation increase) and categorically by quartile. Death from other causes were treated as competing risks. Results: The analyzed cohort had a median follow-up of 10.4 years. Median PSA was 21 ng/mL, 60% had Gleason 8-10 disease, 37% had cT3-T4 disease, and 20% were African American. On univariable analysis, the MMAI model was significantly associated with DM (subdistribution hazard ratio [sHR] 2.05, 95% CI 1.74-2.43, p&lt;0.001) and PCSM (sHR 2.04, 95% CI 1.73-2.42, &lt;0.001). On multivariable analysis, the MMAI model, adjusting for either age, PSA, Gleason score, T-stage, or number of HRFs, was the only variable significantly associated with DM. TdAUC was highest for the MMAI biomarker for both 5-year DM (0.71), compared to PSA (0.56), Gleason score (0.61), T-stage (0.63), or number of HRFs (0.64), and for 5-year PCSM (0.75), compared to clinicopathologic variables (range 0.53-0.63). The estimated 10-year DM and 15-year PCSM rates for MMAI quartile 1 vs 4 were 8% vs 31% and 8% vs 34%, respectively. Conclusions: Our novel MMAI prognostic biomarker was successfully validated across six phase III randomized trials with long-term follow-up to be independently prognostic over standard clinical and pathologic variables for men with high-risk prostate cancer. Despite all patients having high-risk disease, the MMAI biomarker identified those with highly variable risks for DM and PCSM. This tool can help enable personalized, shared decision making for patients and providers.

Deep Learning Based Methods for Breast Cancer Diagnosis: A Systematic Review and Future Direction

Breast cancer is one of the precarious conditions that affect women, and a substantive cure has not yet been discovered for it. With the advent of Artificial intelligence (AI), recently, deep learning techniques have been used effectively in breast cancer detection, facilitating early diagnosis and therefore increasing the chances of patients' survival. Compared to classical machine learning techniques, deep learning requires less human intervention for similar feature extraction. This study presents a systematic literature review on the deep learning-based methods for breast cancer detection that can guide practitioners and researchers in understanding the challenges and new trends in the field. Particularly, different deep learning-based methods for breast cancer detection are investigated, focusing on the genomics and histopathological imaging data. The study specifically adopts the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), which offer a detailed analysis and synthesis of the published articles. Several studies were searched and gathered, and after the eligibility screening and quality evaluation, 98 articles were identified. The results of the review indicated that the Convolutional Neural Network (CNN) is the most accurate and extensively used model for breast cancer detection, and the accuracy metrics are the most popular method used for performance evaluation. Moreover, datasets utilized for breast cancer detection and the evaluation metrics are also studied. Finally, the challenges and future research direction in breast cancer detection based on deep learning models are also investigated to help researchers and practitioners acquire in-depth knowledge of and insight into the area.

Tunicate swarm algorithm with deep convolutional neural network-driven colorectal cancer classification from histopathological imaging data

&lt;abstract&gt; &lt;p&gt;Colorectal cancer (CRC) is one of the most popular cancers among both men and women, with increasing incidence. The enhanced analytical load data from the pathology laboratory, integrated with described intra- and inter-variabilities through the calculation of biomarkers, has prompted the quest for robust machine-based approaches in combination with routine practice. In histopathology, deep learning (DL) techniques have been applied at large due to their potential for supporting the analysis and forecasting of medically appropriate molecular phenotypes and microsatellite instability. Considering this background, the current research work presents a metaheuristics technique with deep convolutional neural network-based colorectal cancer classification based on histopathological imaging data (MDCNN-C3HI). The presented MDCNN-C3HI technique majorly examines the histopathological images for the classification of colorectal cancer (CRC). At the initial stage, the MDCNN-C3HI technique applies a bilateral filtering approach to get rid of the noise. Then, the proposed MDCNN-C3HI technique uses an enhanced capsule network with the Adam optimizer for the extraction of feature vectors. For CRC classification, the MDCNN-C3HI technique uses a DL modified neural network classifier, whereas the tunicate swarm algorithm is used to fine-tune its hyperparameters. To demonstrate the enhanced performance of the proposed MDCNN-C3HI technique on CRC classification, a wide range of experiments was conducted. The outcomes from the extensive experimentation procedure confirmed the superior performance of the proposed MDCNN-C3HI technique over other existing techniques, achieving a maximum accuracy of 99.45%, a sensitivity of 99.45% and a specificity of 99.45%.&lt;/p&gt; &lt;/abstract&gt;

Anonymization of whole slide images in histopathology for research and education.

The exchange of health-related data is subject to regional laws and regulations, such as the General Data Protection Regulation (GDPR) in the EU or the Health Insurance Portability and Accountability Act (HIPAA) in the United States, resulting in non-trivial challenges for researchers and educators when working with these data. In pathology, the digitization of diagnostic tissue samples inevitably generates identifying data that can consist of sensitive but also acquisition-related information stored in vendor-specific file formats. Distribution and off-clinical use of these Whole Slide Images (WSIs) are usually done in these formats, as an industry-wide standardization such as DICOM is yet only tentatively adopted and slide scanner vendors currently do not provide anonymization functionality. We developed a guideline for the proper handling of histopathological image data particularly for research and education with regard to the GDPR. In this context, we evaluated existing anonymization methods and examined proprietary format specifications to identify all sensitive information for the most common WSI formats. This work results in a software library that enables GDPR-compliant anonymization of WSIs while preserving the native formats. Based on the analysis of proprietary formats, all occurrences of sensitive information were identified for file formats frequently used in clinical routine, and finally, an open-source programming library with an executable CLI tool and wrappers for different programming languages was developed. Our analysis showed that there is no straightforward software solution to anonymize WSIs in a GDPR-compliant way while maintaining the data format. We closed this gap with our extensible open-source library that works instantaneously and offline.

RetCCL: Clustering-guided contrastive learning for whole-slide image retrieval.

Benefiting from the large-scale archiving of digitized whole-slide images (WSIs), computer-aided diagnosis has been well developed to assist pathologists in decision-making. Content-based WSI retrieval can be a new approach to find highly correlated WSIs in a historically diagnosed WSI archive, which has the potential usages for assisted clinical diagnosis, medical research, and trainee education. During WSI retrieval, it is particularly challenging to encode the semantic content of histopathological images and to measure the similarity between images for interpretable results due to the gigapixel size of WSIs. In this work, we propose a Retrieval with Clustering-guided Contrastive Learning (RetCCL) framework for robust and accurate WSI-level image retrieval, which integrates a novel self-supervised feature learning method and a global ranking and aggregation algorithm for much improved performance. The proposed feature learning method makes use of existing large-scale unlabeled histopathological image data, which helps learn universal features that could be used directly for subsequent WSI retrieval tasks without extra fine-tuning. The proposed WSI retrieval method not only returns a set of WSIs similar to a query WSI, but also highlights patches or sub-regions of each WSI that share high similarity with patches of the query WSI, which helps pathologists interpret the searching results. Our WSI retrieval framework has been evaluated on the tasks of anatomical site retrieval and cancer subtype retrieval using over 22,000 slides, and the performance exceeds other state-of-the-art methods significantly (around 10% for the anatomic site retrieval in terms of average mMV@10). Besides, the patch retrieval using our learned feature representation offers a performance improvement of 24% on the TissueNet dataset in terms of mMV@5 compared with using ImageNet pre-trained features, which further demonstrates the effectiveness of the proposed CCL feature learning method.

N6-methyladenosine-related lncRNAs in combination with computational histopathology and radiomics predict the prognosis of bladder cancer

ObjectivesIdentification of m6A- related lncRNAs associated with BC diagnosis and prognosis. MethodsFrom the TCGA database, we obtained transcriptome data and corresponding clinical information (including histopathological and CT imaging data) for 408 patients. And bioinformatics, computational histopathology, and radiomics were used to identify and analyze diagnostic and prognostic biomarkers of m6A-related lncRNAs in BC. Results3 significantly high-expressed m6A-related lncRNAs were significantly associated with the prognosis of BC. The BC samples were divided into two subgroups based on the expression of the 3 lncRNAs. The overall survival of patients in cluster 2 was significantly lower than that in cluster 1. The immune landscape results showed that the expression of PD-L1, T cells follicular helper, NK cells resting, and mast cells activated in cluster 2 were significantly higher, and naive B cells, plasma cells, T cells regulatory (Tregs), and mast cells resting were significantly lower. Computational histopathology results showed a significantly higher percentage of tumor-infiltrating lymphocytes (TILs) in cluster 2. The radiomics results show that the 3 eigenvalues of diagnostics image-original minimum, diagnostics image-original maximum, and original GLCM inverse variance are significantly higher in cluster 2. High expression of 2 bridge genes in the PPI network of 30 key immune genes predicts poorer disease-free survival, while immunohistochemistry showed that their expression levels were significantly higher in high-grade BC than in low-grade BC and normal tissue. ConclusionBased on the results of immune landscape, computational histopathology, and radiomics, these 3 m6A-related lncRNAs may be diagnostic and prognostic biomarkers for BC.

LAD-GCN: Automatic diagnostic framework for quantitative estimation of growth patterns during clinical evaluation of lung adenocarcinoma.

Quantitative estimation of growth patterns is important for diagnosis of lung adenocarcinoma and prediction of prognosis. However, the growth patterns of lung adenocarcinoma tissue are very dependent on the spatial organization of cells. Deep learning for lung tumor histopathological image analysis often uses convolutional neural networks to automatically extract features, ignoring this spatial relationship. In this paper, a novel fully automated framework is proposed for growth pattern evaluation in lung adenocarcinoma. Specifically, the proposed method uses graph convolutional networks to extract cell structural features; that is, cells are extracted and graph structures are constructed based on histopathological image data without graph structure. A deep neural network is then used to extract the global semantic features of histopathological images to complement the cell structural features obtained in the previous step. Finally, the structural features and semantic features are fused to achieve growth pattern prediction. Experimental studies on several datasets validate our design, demonstrating that methods based on the spatial organization of cells are appropriate for the analysis of growth patterns.