Pathologists' workload and patient safety

Dense Dilated Multi-Scale Supervised Attention-Guided Network for histopathology image segmentation

Over the last couple of decades, the introduction and proliferation of whole-slide scanners led to increasing interest in the research of digital pathology. Although manual analysis of histopathological images is still the gold standard, the process is often tedious and time consuming. Furthermore, manual analysis also suffers from intra- and interobserver variability. Separating structures or grading morphological changes can be difficult due to architectural variability of these images. Deep learning techniques have shown great potential in histopathology image segmentation that drastically reduces the time needed for downstream tasks of analysis and providing accurate diagnosis. However, few algorithms have clinical implementations. In this paper, we propose a new deep learning model Dense Dilated Multiscale Supervised Attention-Guided (D2MSA) Network for histopathology image segmentation that makes use of deep supervision coupled with a hierarchical system of novel attention mechanisms. The proposed model surpasses state-of-the-art performance while using similar computational resources. The performance of the model has been evaluated for the tasks of gland segmentation and nuclei instance segmentation, both of which are clinically relevant tasks to assess the state and progress of malignancy. Here, we have used histopathology image datasets for three different types of cancer. We have also performed extensive ablation tests and hyperparameter tuning to ensure the validity and reproducibility of the model performance. The proposed model is available at www.github.com/shirshabose/D2MSA-Net.

Multicenter automatic detection of invasive carcinoma on breast whole slide images

Breast cancer is one of the most prevalent cancers worldwide and pathologists are closely involved in establishing a diagnosis. Tools to assist in making a diagnosis are required to manage the increasing workload. In this context, artificial intelligence (AI) and deep-learning based tools may be used in daily pathology practice. However, it is challenging to develop fast and reliable algorithms that can be trusted by practitioners, whatever the medical center. We describe a patch-based algorithm that incorporates a convolutional neural network to detect and locate invasive carcinoma on breast whole-slide images. The network was trained on a dataset extracted from a reference acquisition center. We then performed a calibration step based on transfer learning to maintain the performance when translating on a new target acquisition center by using a limited amount of additional training data. Performance was evaluated using classical binary measures (accuracy, recall, precision) for both centers (referred to as "test reference dataset" and "test target dataset") and at two levels: patch and slide level. At patch level, accuracy, recall, and precision of the model on the reference and target test sets were 92.1% and 96.3%, 95% and 87.8%, and 73.9% and 70.6%, respectively. At slide level, accuracy, recall, and precision were 97.6% and 92.0%, 90.9% and 100%, and 100% and 70.8% for test sets 1 and 2, respectively. The high performance of the algorithm at both centers shows that the calibration process is efficient. This is performed using limited training data from the new target acquisition center and requires that the model is trained beforehand on a large database from a reference center. This methodology allows the implementation of AI diagnostic tools to help in routine pathology practice.

MTU: A multi-tasking U-net with hybrid convolutional learning and attention modules for cancer classification and gland Segmentation in Colon Histopathological Images

A clinically comparable multi-tasking computerized deep U-Net-based model is demonstrated in this paper. It intends to offer clinical gland morphometric information and cancer grade classification to be provided as referential opinions for pathologists in order to abate human errors. It embraces enhanced feature learning capability that aids in extraction of potent multi-scale features; efficacious semantic gap recovery during feature concatenation; and successful interception of resolution-degradation and vanishing gradient problems while performing moderate computations. It is proposed by integrating three unique novel structural components namely Hybrid Convolutional Learning Units in the encoder and decoder, Attention Learning Units in skip connection, and Multi-Scalar Dilated Transitional Unit as the transitional layer in the traditional U-Net architecture. These units are composed of the amalgamated phenomenon of multi-level convolutional learning through conventional, atrous, residual, depth-wise, and point-wise convolutions which are further incorporated with target-specific attention learning and enlarged effectual receptive field size. Also, pre-processing techniques of patch-sampling, augmentation (color and morphological), stain-normalization, etc. are employed to burgeon its generalizability. To build network invariance towards digital variability, exhaustive experiments are conducted using three public datasets (Colorectal Adenocarcinoma Gland (CRAG), Gland Segmentation (GlaS) challenge, and Lung Colon-25000 (LC-25K) dataset)) and then its robustness is verified using an in-house private dataset of Hospital Colon (HosC). For the cancer classification, the proposed model achieved results of Accuracy (CRAG(95%), GlaS(97.5%), LC-25K(99.97%), HosC(99.45%)), Precision (CRAG(0.9678), GlaS(0.9768), LC-25K(1), HosC(1)), F1-score (CRAG(0.968), GlaS(0.977), LC 25K(0.9997), HosC(0.9965)), and Recall (CRAG(0.9677), GlaS(0.9767), LC-25K(0.9994), HosC(0.9931)). For the gland detection and segmentation, the proposed model achieved competitive results of F1-score (CRAG(0.924), GlaS(Test A(0.949), Test B(0.918)), LC-25K(0.916), HosC(0.959)); Object-Dice Index (CRAG(0.959), GlaS(Test A(0.956), Test B(0.909)), LC-25K(0.929), HosC(0.922)), and Object-Hausdorff Distance (CRAG(90.47), GlaS(Test A(23.17), Test B(71.53)), LC-25K(96.28), HosC(85.45)). In addition, the activation mappings for testing the interpretability of the classification decision-making process are reported by utilizing techniques of Local Interpretable Model-Agnostic Explanations, Occlusion Sensitivity, and Gradient-Weighted Class Activation Mappings. This is done to provide further evidence about the model's self-learning capability of the comparable patterns considered relevant by pathologists without any pre-requisite for annotations. These activation mapping visualization outcomes are evaluated by proficient pathologists, and they delivered these images with a class-path validation score of (CRAG(9.31), GlaS(9.25), LC-25K(9.05), and HosC(9.85)). Furthermore, the seg-path validation score of (GlaS (Test A(9.40), Test B(9.25)), CRAG(9.27), LC-25K(9.01), HosC(9.19)) given by multiple pathologists is included for the final segmented outcomes to substantiate the clinical relevance and suitability for facilitation at the clinical level. The proposed model will aid pathologists to formulate an accurate diagnosis by providing a referential opinion during the morphology assessment of histopathology images. It will reduce unintentional human error in cancer diagnosis and consequently will enhance patient survival rate.

Current Developments of Artificial Intelligence in Digital Pathology and Its Future Clinical Applications in Gastrointestinal Cancers

The implementation of DP will revolutionize current practice by providing pathologists with additional tools and algorithms to improve workflow. Furthermore, DP will open up opportunities for development of AI-based tools for more precise and reproducible diagnosis through computational pathology. One of the key features of AI is its capability to generate perceptions and recognize patterns beyond the human senses. Thus, the incorporation of AI into DP can reveal additional morphological features and information. At the current rate of AI development and adoption of DP, the interest in computational pathology is expected to rise in tandem. There have already been promising developments related to AI-based solutions in prostate cancer detection; however, in the GI tract, development of more sophisticated algorithms is required to facilitate histological assessment of GI specimens for early and accurate diagnosis. In this review, we aim to provide an overview of the current histological practices in AP laboratories with respect to challenges faced in image preprocessing, present the existing AI-based algorithms, discuss their limitations and present clinical insight with respect to the application of AI in early detection and diagnosis of GI cancer.

Triage-driven diagnosis of Barrett's esophagus for early detection of esophageal adenocarcinoma using deep learning

Deep learning methods have been shown to achieve excellent performance on diagnostic tasks, but how to optimally combine them with expert knowledge and existing clinical decision pathways is still an open challenge. This question is particularly important for the early detection of cancer, where high-volume workflows may benefit from (semi-)automated analysis. Here we present a deep learning framework to analyze samples of the Cytosponge-TFF3 test, a minimally invasive alternative to endoscopy, for detecting Barrett's esophagus, which is the main precursor of esophageal adenocarcinoma. We trained and independently validated the framework on data from two clinical trials, analyzing a combined total of 4,662 pathology slides from 2,331 patients. Our approach exploits decision patterns of gastrointestinal pathologists to define eight triage classes of varying priority for manual expert review. By substituting manual review with automated review in low-priority classes, we can reduce pathologist workload by 57% while matching the diagnostic performance of experienced pathologists.

Accurate diagnosis of colorectal cancer based on histopathology images using artificial intelligence

BACKGROUND

Accurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients' treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on daily image analyses.

METHODS

Based on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC diagnosis using weakly labeled pathological whole-slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, > 14,680 WSIs, from > 9631 subjects that covered diverse and representative clinical cases from multi-independent-sources across China, the USA, and Germany.

RESULTS

Our innovative AI tool consistently and nearly perfectly agreed with (average Kappa statistic 0.896) and even often better than most of the experienced expert pathologists when tested in diagnosing CRC WSIs from multicenters. The average area under the receiver operating characteristics curve (AUC) of AI was greater than that of the pathologists (0.988 vs 0.970) and achieved the best performance among the application of other AI methods to CRC diagnosis. Our AI-generated heatmap highlights the image regions of cancer tissue/cells.

CONCLUSIONS

This first-ever generalizable AI system can handle large amounts of WSIs consistently and robustly without potential bias due to fatigue commonly experienced by clinical pathologists. It will drastically alleviate the heavy clinical burden of daily pathology diagnosis and improve the treatment for CRC patients. This tool is generalizable to other cancer diagnosis based on image recognition.

Association of Intensive Endoscopic Screening Burden With Gastric Cancer Detection

IMPORTANCE

The rapidly increasing number of gastric cancer examinations performed over a short period might influence screening performance. Accessing the association between calendar month and gastric cancer detection rates might improve policy and guide institutional support.

OBJECTIVE

To evaluate the association between the increased number of examinations over a certain period and gastric cancer detection rates among a large population included in the Korean National Cancer Screening Program (KNCSP).

DESIGN, SETTING, AND PARTICIPANTS

This retrospective, population-based cohort study used data from the KNCSP comprising 26 765 665 men and women aged 40 years or older who participated in the screening program between January 1, 2013, and December 31, 2016. Data were analyzed from November 1, 2019, to March 31, 2020.

EXPOSURES

Gastric cancer screening with endoscopy.

MAIN OUTCOMES AND MEASURES

The primary outcome was monthly gastric cancer detection rates in the KNCSP. A negative binomial regression model was used to evaluate the association between the screening month and detection rates.

RESULTS

In total, 21 535 222 individuals underwent endoscopy (mean [SD] age, 55.61 [10.61] years; 11 761 709 women [54.62%]). The quarterly number of participants was the highest in the last quarter of the study period (2013-2014: 4 094 951 [41.39%], 2015-2016: 4 911 629 [42.19%]); this proportion was 2.48 to 2.84 times greater than that of the first quarter. Cancer detection rates were the lowest in December (2013-2014: 0.22; 95% CI, 0.22-0.23; 2015-2016: 0.21; 95% CI, 0.21-0.22); this was approximately a 40.0% to 45.0% reduction compared with the rates in January. The age group was the significant factor for monthly detection rates. After adjustment for the age group and taking account of the number of screenings, the estimated coefficient range for the screening month was negative and the detection rate in December was significantly different than in January for both the consequent cycles (2013-2014: -0.05 to -0.18; P < .001; and 2015-2016: -0.06 to -0.19; P < .001). In the multivariable logistic model, the association of calendar month with detected cancer remained after adjusting for other confounding factors (December, 2013-2014: odds ratio, 0.82; 95% CI, 0.76-0.87; P < .001; 2015-2016: odds ratio, 0.83; 95% CI, 0.79-0.89; P < .001).

CONCLUSIONS AND RELEVANCE

The findings of this cohort study suggest that the workload of endoscopists increased with the increasing number of examinations toward the end of the year, as demonstrated by the decreased cancer detection rates. These findings may help to improve gastric cancer detection rates of screening programs by controlling the monthly screening number and policy modifications.

Characteristics and diagnostic performance of pathologists who enjoy interpreting melanocytic lesions

Diagnostic discrepancy among pathologists interpreting melanocytic skin lesions (MSL) is an ongoing concern for patient care. Given that job satisfaction could impact patient care, this study aimed to characterize which pathologists enjoy interpreting MSL and estimate the association between enjoyment and diagnostic accuracy. Pathologists' demographics, training, and experience were obtained by a cross-sectional survey. Associations between these characteristics and self-reported enjoyment when interpreting MSL were estimated by Pearson's Chi-square tests. Diagnostic accuracy was determined by comparing pathologists' MSL interpretations with reference standard diagnoses. Associations between enjoyment and diagnostic accuracy were evaluated by generalized estimating equations (GEE) models. One hundred and eighty-seven (90%) pathologists completed the study. Seventy percent agreed that interpreting MSL is enjoyable. Pathologists who enjoyed interpreting MSL were more likely to be board certified and/or fellowship trained in dermatopathology (P=0.008), have ?10 years of experience (P=0.010) and have an MSL caseload of ?60 per month (P=&lt;0.001). After adjustment, there was no association between enjoyment and diagnostic accuracy. Our data suggest that job dissatisfaction does not adversely affect diagnostic accuracy in the interpretation of melanocytic lesions, which is of importance given the progressive increase in annual biopsy rates and the attendant work demands imposed on pathologists.