From lake to fisheries: Interactive effect of climate and landuse changes hit on lake fish catch?

Global warming and unpredictable nature possess a negative impact on fisheries and the daily activities of other habitats. GIS and remote sensing approach is an effective tool to determine the morphological characteristics of the lake. The present study addresses the interactive effect of climate and landuse changes hit on fish catch in lake fisheries. We used a combination of the landscape disturbance index, vulnerability index, and loss index to construct a complete ecological risk assessment framework based on the landscape structure of regional ecosystems. The results indicate an increase from around 45%-76% in the percentage of land susceptible to moderate to ecological severe risk in the landscape from 2004 to 2023. Since 1950, temperature changes have increased by 0.4%, precipitation has decreased by 6%, and water levels have decreased by 4.2%, based on the results. The results indicate that landuse, water temperature, precipitation, and water depth significantly impact the aquaculture system. The findings strongly suggest integrating possible consequences of environmental change on fish yield for governance modeling techniques to minimize their effects.

A fully automated and explainable algorithm for predicting malignant transformation in oral epithelial dysplasia.

Oral epithelial dysplasia (OED) is a premalignant histopathological diagnosis given to lesions of the oral cavity. Its grading suffers from significant inter-/intra-observer variability, and does not reliably predict malignancy progression, potentially leading to suboptimal treatment decisions. To address this, we developed an artificial intelligence (AI) algorithm, that assigns an Oral Malignant Transformation (OMT) risk score based on the Haematoxylin and Eosin (H&E) stained whole slide images (WSIs). Our AI pipeline leverages an in-house segmentation model to detect and segment both nuclei and epithelium. Subsequently, a shallow neural network utilises interpretable morphological and spatial features, emulating histological markers, to predict progression. We conducted internal cross-validation on our development cohort (Sheffield; n = 193 cases) and independent validation on two external cohorts (Birmingham and Belfast; n = 89 cases). On external validation, the proposed OMTscore achieved an AUROC = 0.75 (Recall = 0.92) in predicting OED progression, outperforming other grading systems (Binary: AUROC = 0.72, Recall = 0.85). Survival analyses showed the prognostic value of our OMTscore (C-index = 0.60, p = 0.02), compared to WHO (C-index = 0.64, p = 0.003) and binary grades (C-index = 0.65, p < 0.001). Nuclear analyses elucidated the presence of peri-epithelial and intra-epithelial lymphocytes in highly predictive patches of transforming cases (p < 0.001). This is the first study to propose a completely automated, explainable, and externally validated algorithm for predicting OED transformation. Our algorithm shows comparable-to-human-level performance, offering a promising solution to the challenges of grading OED in routine clinical practice.

Exploring the content of epilepsy fellowship program websites: an analysis of information available to applicants.

BACKGROUND: Program websites are essential resources in the process of residency and fellowship application. We evaluated the information furnished on these resources by Epilepsy fellowship programs. The extent of information provided was compared across geographic zones, academic affiliation, and national ranking. METHODS: A list of Epilepsy fellowship programs was derived from the Fellowship and Residency Electronic Interactive Database (FREIDA). Links to program websites were obtained directly from FREIDA or using Google's search engine. Online data was categorized to reflect program information, education, recruitment, compensation, epilepsy center-specific information, and social media presence. Data points under each category were collected to develop a standardized scoring system. The frequency of criterion present was compared across geographic zones, academic affiliation, and national ranking using parametric and non-parametric statistical tests. Significance was determined at a p-value ≤ 0.05 for all cases. The study utilized IBM SPSS version 28 and Python 3.11.3. RESULTS: We analyzed 80 Epilepsy fellowship programs. The most reported feature was the program director's name and email (100.0%). The least reported features included board pass rates (1.3%), preparatory boot camp (8.8%), and post-fellowship placements (11.3%). Programs were found to be well-represented on X (88.8%), Facebook (81.3%), and Instagram (71.3%). Most (85.0%) of the programs were searchable through Google. The scores for program information, education, recruitment, compensation, epilepsy center-specific information, and social media visibility did not significantly vary based on location, academic affiliation, or rank status. CONCLUSIONS: Our results demonstrate that despite an online presence, there is much room for improvement in the content available to the applicant. To improve the Match process and attract a roster of well-informed fellows, Epilepsy fellowship programs should furnish program websites with up-to-date information relevant to program information, education, recruitment, compensation, and epilepsy center-specific information.

Effects of routine physical therapy with and without kinesio taping in improving gross motor function in sitting and standing in spastic diplegic cerebral palsy children.

OBJECTIVE: To determine the effectiveness of Kinesio taping along with routine physical therapy on improving gross motor function in sitting and standing among spastic diplegic Cerebral Palsy children. DESIGN: Randomized controlled trial. SETTINGS: University Teaching Hospital University of Lahore, Lahore. PARTICIPANTS: 53 participants with diagnosed spastic diplegic cerebral palsy were randomly allocated in control and experimental groups. INTERVENTION: 26 Participants were treated by kinesio taping which was applied in a criss-cross manner along with routine physical therapy program while the control group (n = 27) received NDT exercise program that comprises of stretching, functional reaching, weight-bearing exercises and walking. OUTCOME MEASURE: Gross motor function was assessed using 2 components of Gross Motor Function Classification System (GMFCS-88), i.e., sitting as well as standing at the base line and after every 3rd week for 12 weeks follow up. RESULTS: In study and control group the mean score of gross motor function for sitting at baseline was 33.96 ± 3.11 and 31.50 ± 3.32 respectively. After intervention, it changed to 47.70 ± 5.46 and 43.46 ± 1.81 respectively. Mean score for Gross Motor Function calculated at base line in study and control group for standing was 27.37 ± 1.14 and 26 ± 3.01 respectively. At the end of intervention, the score improved to 36.55 ± 4.27 and 33.69 ± 2.46 respectively. CONCLUSION: In comparison to control group, significant increase in gross motor function of intervention group was seen after the 12 weeks of intervention. In this way, over back muscles the application of kinesio tape in a Criss-Cross manner may be helpful. Also it can be used as an additional approach along with routine physical therapy to improve standing and sitting in spastic diplegic children.

Research on runoff process vectorization and integration of deep learning algorithms for flood forecasting.

Accurate multi-step ahead flood forecasting is crucial for flood prevention and mitigation efforts as well as optimizing water resource management. In this study, we propose a Runoff Process Vectorization (RPV) method and integrate it with three Deep Learning (DL) models, namely Long Short-Term Memory (LSTM), Temporal Convolutional Network (TCN), and Transformer, to develop a series of RPV-DL flood forecasting models, namely RPV-LSTM, RPV-TCN, and RPV-Transformer models. The models are evaluated using observed flood runoff data from nine typical basins in the middle Yellow River region. The key findings are as follows: Under the same lead time conditions, the RPV-DL models outperform the DL models in terms of Nash-Sutcliffe efficiency coefficient, root mean square error, and relative error for peak flows in the nine typical basins of the middle Yellow River region. Based on the comprehensive evaluation results of the train and test periods, the RPV-DL model outperforms the DL model by an average of 2.82%-22.21% in terms of NSE across nine basins, with RMSE and RE reductions of 10.86-28.81% and 36.14%-51.35%, respectively. The vectorization method significantly improves the accuracy of DL flood forecasting, and the RPV-DL models exhibit better predictive performance, particularly when the lead time is 4h-6h. When the lead time is 4-6h, the percentage improvement in NSE is 9.77%, 15.07%, and 17.94%. The RPV-TCN model shows superior performance in overcoming forecast errors among the nine basins. The research findings provide scientific evidence for flood prevention and mitigation efforts in river basins.

Interpretable baseflow segmentation and prediction based on numerical experiments and deep learning.

Baseflow is a crucial water source in the inland river basins of high-cold mountainous region, playing a significant role in maintaining runoff stability. It is challenging to select the most suitable baseflow separation method in data-scarce high-cold mountainous region and to evaluate effects of climate factors and underlying surface changes on baseflow variability and seasonal distribution characteristics. Here we attempt to address how meteorological factors and underlying surface changes affect baseflow using the Grey Wolf Optimizer Digital Filter Method (GWO-DFM) for rapid baseflow separation and the Long Short-Term Memory (LSTM) neural network model for baseflow prediction, clarifying interpretability of the LSTM model in baseflow forecasting. The proposed method was successfully implemented using a 63-year time series (1958-2020) of flow data from the Tai Lan River (TLR) basin in the high-cold mountainous region, along with 21 years of ERA5-land meteorological data and MODIS data (2000-2020). The results indicate that: (1) GWO-DFM can rapidly identify the optimal filtering parameters. It employs the arithmetic average of three methods, namely Chapman, Chapman-Maxwell and Eckhardt filter, as the best baseflow separation approach for the TLR basin. Additionally, the baseflow significantly increases after the second mutation of the baseflow rate. (2) Baseflow sources are mainly influenced by precipitation infiltration, glacier frozen soil layers, and seasonal ponding. (3) Solar radiation, temperature, precipitation, and NDVI are the primary factors influencing baseflow changes, with Nash-Sutcliffe efficiency coefficients exceeding 0.78 in both the LSTM model training and prediction periods. (4) Changes in baseflow are most influenced by solar radiation, temperature, and NDVI. This study systematically analyzes the changes in baseflow and response mechanisms in high-cold mountainous region, contributing to the management of water resources in mountainous basins under changing environmental conditions.

Doxorubicin encapsulated blend of sitagliptin-lignin polymeric drug delivery system for effective combination therapy against cancer.

In this research, a sitagliptin-lignin biopolymer (SL) containing zinc selenide quantum dots (ZnSe QDs) and doxorubicin (doxo) was synthesized. The fabricated polymeric drug delivery system was characterized via FTIR, XRD, SEM, TGA, IR, and DSC. SLQD-Doxo exhibited an irregular surface with a 32 nm diameter and well-defined surface chemistry. Drug loading efficiency was assessed at different concentrations, pH levels, time intervals, and temperatures, and drug kinetics were calculated. Maximum drug release was observed at 6 µmol concentration after 24 h, pH of 6.5 and 45 °C. The maximum drug encapsulation efficiency was 81.75 %. SLQD-Doxo demonstrated 24.4 ± 1.04 % anti-inflammatory activity, and the maximum lipoxygenase inhibition in a concentration-dependent manner was 71.45 ± 2.02 %, compared to indomethacin, a standard anticancer drug. The designed system was applied to breast cancer MCF-7 cells to evaluate anticancer activity. Cytotoxicity of SLQD-Doxo resulted in 24.48 ± 1.64 dead cells and 74.39 ± 4.12 viable cells. Lignin's polyphenolic nature resulted in good antioxidant activity of LLQD-Doxo. The combination of SLQD-Doxo was appropriate for drug delivery at high temperatures and acidic pH of tumor cells compared to healthy cells.

Distribution and bioaccumulation of trace elements in two Cyprinidae fish species in the Indus river, Pakistan, including the impact of hydraulic structure on macroinvertebrates' biodiversity.

The concentration of trace elements (chromium, lead, zinc, copper, manganese, and iron) was determined in water, sediment and tissues of two Cyprinidae fish species - Labeo rohita and Tor putitora - collected from the eight sampling stations of Indus River in 2022 for four successive seasons (autumn, winter, spring, summer), and also study the present condition of macroinvertebrates after the construction of hydraulic structure. The obtained results of trace element concentrations in the Indus River were higher than the acceptable drinking water standards by WHO. The nitrate concentration ranges from 5.2 to 59.6 mg l-1, turbidity ranges from 3.00 to 63.9 NTU, total suspended solids and ammonium ions are below the detection limit (<0.05). In the liver, highest dry wt trace elements (µg/g) such as Cr (4.32), Pb (7.07), Zn (58.26), Cu (8.38), Mn (50.27), and Fe (83.9) for the Labeo rohita; and Tor Putitora has significantly greater accumulated concentration (Cr, Pb, Zn, Cu, Mn, Fe) in muscle and liver than did Labeo rohita species. Additionally, lower number of macroinvertebrates were recorded during the monsoonal season than pre-monsoon and post-monsoon. Local communities surrounded by polluted environments are more probably to consume more fish and expose them to higher concentrations of toxic trace elements (lead and copper). The findings also provide a basis for broader ecological management of the Indus River, which significantly influenced human beings and socioeconomic disasters, particularly in the local community.

Mitosis detection, fast and slow: Robust and efficient detection of mitotic figures.

Counting of mitotic figures is a fundamental step in grading and prognostication of several cancers. However, manual mitosis counting is tedious and time-consuming. In addition, variation in the appearance of mitotic figures causes a high degree of discordance among pathologists. With advances in deep learning models, several automatic mitosis detection algorithms have been proposed but they are sensitive to domain shift often seen in histology images. We propose a robust and efficient two-stage mitosis detection framework, which comprises mitosis candidate segmentation (Detecting Fast) and candidate refinement (Detecting Slow) stages. The proposed candidate segmentation model, termed EUNet, is fast and accurate due to its architectural design. EUNet can precisely segment candidates at a lower resolution to considerably speed up candidate detection. Candidates are then refined using a deeper classifier network, EfficientNet-B7, in the second stage. We make sure both stages are robust against domain shift by incorporating domain generalization methods. We demonstrate state-of-the-art performance and generalizability of the proposed model on the three largest publicly available mitosis datasets, winning the two mitosis domain generalization challenge contests (MIDOG21 and MIDOG22). Finally, we showcase the utility of the proposed algorithm by processing the TCGA breast cancer cohort (1,124 whole-slide images) to generate and release a repository of more than 620K potential mitotic figures (not exhaustively validated).

AI-based intra-tumor heterogeneity score of Ki67 expression as a prognostic marker for early-stage ER+/HER2- breast cancer.

Early-stage estrogen receptor positive and human epidermal growth factor receptor negative (ER+/HER2-) luminal breast cancer (BC) is quite heterogeneous and accounts for about 70% of all BCs. Ki67 is a proliferation marker that has a significant prognostic value in luminal BC despite the challenges in its assessment. There is increasing evidence that spatial colocalization, which measures the evenness of different types of cells, is clinically important in several types of cancer. However, reproducible quantification of intra-tumor spatial heterogeneity remains largely unexplored. We propose an automated pipeline for prognostication of luminal BC based on the analysis of spatial distribution of Ki67 expression in tumor cells using a large well-characterized cohort (n = 2,081). The proposed Ki67 colocalization (Ki67CL) score can stratify ER+/HER2- BC patients with high significance in terms of BC-specific survival (p < 0.00001) and distant metastasis-free survival (p = 0.0048). Ki67CL score is shown to be highly significant compared with the standard Ki67 index. In addition, we show that the proposed Ki67CL score can help identify luminal BC patients who can potentially benefit from adjuvant chemotherapy.

LYSTO: The Lymphocyte Assessment Hackathon and Benchmark Dataset.

We introduce LYSTO, the Lymphocyte Assessment Hackathon, which was held in conjunction with the MICCAI 2019 Conference in Shenzhen (China). The competition required participants to automatically assess the number of lymphocytes, in particular T-cells, in images of colon, breast, and prostate cancer stained with CD3 and CD8 immunohistochemistry. Differently from other challenges setup in medical image analysis, LYSTO participants were solely given a few hours to address this problem. In this paper, we describe the goal and the multi-phase organization of the hackathon; we describe the proposed methods and the on-site results. Additionally, we present post-competition results where we show how the presented methods perform on an independent set of lung cancer slides, which was not part of the initial competition, as well as a comparison on lymphocyte assessment between presented methods and a panel of pathologists. We show that some of the participants were capable to achieve pathologist-level performance at lymphocyte assessment. After the hackathon, LYSTO was left as a lightweight plug-and-play benchmark dataset on grand-challenge website, together with an automatic evaluation platform.

Artificial Intelligence-Based Mitosis Scoring in Breast Cancer: Clinical Application.

In recent years, artificial intelligence (AI) has demonstrated exceptional performance in mitosis identification and quantification. However, the implementation of AI in clinical practice needs to be evaluated against the existing methods. This study is aimed at assessing the optimal method of using AI-based mitotic figure scoring in breast cancer (BC). We utilized whole slide images from a large cohort of BC with extended follow-up comprising a discovery (n = 1715) and a validation (n = 859) set (Nottingham cohort). The Cancer Genome Atlas of breast invasive carcinoma (TCGA-BRCA) cohort (n = 757) was used as an external test set. Employing automated mitosis detection, the mitotic count was assessed using 3 different methods, the mitotic count per tumor area (MCT; calculated by dividing the number of mitotic figures by the total tumor area), the mitotic index (MI; defined as the average number of mitotic figures per 1000 malignant cells), and the mitotic activity index (MAI; defined as the number of mitotic figures in 3 mm2 area within the mitotic hotspot). These automated metrics were evaluated and compared based on their correlation with the well-established visual scoring method of the Nottingham grading system and Ki67 score, clinicopathologic parameters, and patient outcomes. AI-based mitotic scores derived from the 3 methods (MCT, MI, and MAI) were significantly correlated with the clinicopathologic characteristics and patient survival (P < .001). However, the mitotic counts and the derived cutoffs varied significantly between the 3 methods. Only MAI and MCT were positively correlated with the gold standard visual scoring method used in Nottingham grading system (r = 0.8 and r = 0.7, respectively) and Ki67 scores (r = 0.69 and r = 0.55, respectively), and MAI was the only independent predictor of survival (P < .05) in multivariate Cox regression analysis. For clinical applications, the optimum method of scoring mitosis using AI needs to be considered. MAI can provide reliable and reproducible results and can accurately quantify mitotic figures in BC.

CoNIC Challenge: Pushing the frontiers of nuclear detection, segmentation, classification and counting.

Nuclear detection, segmentation and morphometric profiling are essential in helping us further understand the relationship between histology and patient outcome. To drive innovation in this area, we setup a community-wide challenge using the largest available dataset of its kind to assess nuclear segmentation and cellular composition. Our challenge, named CoNIC, stimulated the development of reproducible algorithms for cellular recognition with real-time result inspection on public leaderboards. We conducted an extensive post-challenge analysis based on the top-performing models using 1,658 whole-slide images of colon tissue. With around 700 million detected nuclei per model, associated features were used for dysplasia grading and survival analysis, where we demonstrated that the challenge's improvement over the previous state-of-the-art led to significant boosts in downstream performance. Our findings also suggest that eosinophils and neutrophils play an important role in the tumour microevironment. We release challenge models and WSI-level results to foster the development of further methods for biomarker discovery.

Consistency regularisation in varying contexts and feature perturbations for semi-supervised semantic segmentation of histology images.

Semantic segmentation of various tissue and nuclei types in histology images is fundamental to many downstream tasks in the area of computational pathology (CPath). In recent years, Deep Learning (DL) methods have been shown to perform well on segmentation tasks but DL methods generally require a large amount of pixel-wise annotated data. Pixel-wise annotation sometimes requires expert's knowledge and time which is laborious and costly to obtain. In this paper, we present a consistency based semi-supervised learning (SSL) approach that can help mitigate this challenge by exploiting a large amount of unlabelled data for model training thus alleviating the need for a large annotated dataset. However, SSL models might also be susceptible to changing context and features perturbations exhibiting poor generalisation due to the limited training data. We propose an SSL method that learns robust features from both labelled and unlabelled images by enforcing consistency against varying contexts and feature perturbations. The proposed method incorporates context-aware consistency by contrasting pairs of overlapping images in a pixel-wise manner from changing contexts resulting in robust and context invariant features. We show that cross-consistency training makes the encoder features invariant to different perturbations and improves the prediction confidence. Finally, entropy minimisation is employed to further boost the confidence of the final prediction maps from unlabelled data. We conduct an extensive set of experiments on two publicly available large datasets (BCSS and MoNuSeg) and show superior performance compared to the state-of-the-art methods.

AI-enabled routine H&E image based prognostic marker for early-stage luminal breast cancer.

Breast cancer (BC) grade is a well-established subjective prognostic indicator of tumour aggressiveness. Tumour heterogeneity and subjective assessment result in high degree of variability among observers in BC grading. Here we propose an objective Haematoxylin & Eosin (H&E) image-based prognostic marker for early-stage luminal/Her2-negative BReAst CancEr that we term as the BRACE marker. The proposed BRACE marker is derived from AI based assessment of heterogeneity in BC at a detailed level using the power of deep learning. The prognostic ability of the marker is validated in two well-annotated cohorts (Cohort-A/Nottingham: n = 2122 and Cohort-B/Coventry: n = 311) on early-stage luminal/HER2-negative BC patients treated with endocrine therapy and with long-term follow-up. The BRACE marker is able to stratify patients for both distant metastasis free survival (p = 0.001, C-index: 0.73) and BC specific survival (p < 0.0001, C-index: 0.84) showing comparable prediction accuracy to Nottingham Prognostic Index and Magee scores, which are both derived from manual histopathological assessment, to identify luminal BC patients that may be likely to benefit from adjuvant chemotherapy.

Clinical and Etiological Exploration of Ventilator-Associated Pneumonia in the Intensive Care Unit of a Developing Country.

Background Ventilator-associated pneumonia (VAP) is a critical concern in the intensive care unit (ICU), with significant implications for patient outcomes. This retrospective cross-sectional study aimed to determine the prevalence of VAP in an ICU of a developing country, identify the predominant etiological factors, assess patient outcomes, and underscore the need for tailored interventions in high-risk patient groups. Methods This retrospective cross-sectional study included 589 ICU patients who underwent ventilator-assisted breathing for over 48 hours. Among them, 151 developed VAP. The diagnosis was made on clinical, laboratory, and radiological findings, and tracheal aspirate cultures. Exclusions included pediatric patients, less than 48 hours of ventilation, and pre-existing lung infections. Patient data encompassed gender, age, comorbidities, outcomes, admission reasons, isolated microorganisms, and clinical findings. Results 151 patients out of the 589 developed VAP. The age of the patients ranged between 31 to 69 years and the mean age was 45.43 ± 8.92 years. Clinical diagnoses upon ICU admission varied, including sepsis, trauma, stroke, and metabolic disorders. Chest X-rays commonly revealed atelectasis (19.2%), consolidation (21.9%), pleural effusion (11.9%), and lobar pneumonia (45.7%). Tracheal aspirate cultures predominantly isolated multidrug-resistant gram-negative rods, with methicillin-resistant gram-positive cocci and fungal pneumonia prevalent in neutropenic sepsis cases. Notably, only 54 (35.8%) of patients survived, with significantly poorer outcomes observed in sepsis, neutropenic sepsis, and stroke cases compared to trauma and post-operative admissions. Conclusion Multidrug-resistant organisms and the spread of nosocomial infections are the predominant causes of VAP in the ICU. This emphasizes the urgent need for multifaceted interventions to prevent and manage VAP effectively. Developing and implementing targeted strategies, considering the unique challenges faced in resource-constrained healthcare settings can aid in decreasing the mortality associated with it.

Development and validation of artificial intelligence-based prescreening of large-bowel biopsies taken in the UK and Portugal: a retrospective cohort study.

BACKGROUND: Histopathological examination is a crucial step in the diagnosis and treatment of many major diseases. Aiming to facilitate diagnostic decision making and improve the workload of pathologists, we developed an artificial intelligence (AI)-based prescreening tool that analyses whole-slide images (WSIs) of large-bowel biopsies to identify typical, non-neoplastic, and neoplastic biopsies. METHODS: This retrospective cohort study was conducted with an internal development cohort of slides acquired from a hospital in the UK and three external validation cohorts of WSIs acquired from two hospitals in the UK and one clinical laboratory in Portugal. To learn the differential histological patterns from digitised WSIs of large-bowel biopsy slides, our proposed weakly supervised deep-learning model (Colorectal AI Model for Abnormality Detection [CAIMAN]) used slide-level diagnostic labels and no detailed cell or region-level annotations. The method was developed with an internal development cohort of 5054 biopsy slides from 2080 patients that were labelled with corresponding diagnostic categories assigned by pathologists. The three external validation cohorts, with a total of 1536 slides, were used for independent validation of CAIMAN. Each WSI was classified into one of three classes (ie, typical, atypical non-neoplastic, and atypical neoplastic). Prediction scores of image tiles were aggregated into three prediction scores for the whole slide, one for its likelihood of being typical, one for its likelihood of being non-neoplastic, and one for its likelihood of being neoplastic. The assessment of the external validation cohorts was conducted by the trained and frozen CAIMAN model. To evaluate model performance, we calculated area under the convex hull of the receiver operating characteristic curve (AUROC), area under the precision-recall curve, and specificity compared with our previously published iterative draw and rank sampling (IDaRS) algorithm. We also generated heat maps and saliency maps to analyse and visualise the relationship between the WSI diagnostic labels and spatial features of the tissue microenvironment. The main outcome of this study was the ability of CAIMAN to accurately identify typical and atypical WSIs of colon biopsies, which could potentially facilitate automatic removing of typical biopsies from the diagnostic workload in clinics. FINDINGS: A randomly selected subset of all large bowel biopsies was obtained between Jan 1, 2012, and Dec 31, 2017. The AI training, validation, and assessments were done between Jan 1, 2021, and Sept 30, 2022. WSIs with diagnostic labels were collected between Jan 1 and Sept 30, 2022. Our analysis showed no statistically significant differences across prediction scores from CAIMAN for typical and atypical classes based on anatomical sites of the biopsy. At 0·99 sensitivity, CAIMAN (specificity 0·5592) was more accurate than an IDaRS-based weakly supervised WSI-classification pipeline (0·4629) in identifying typical and atypical biopsies on cross-validation in the internal development cohort (p<0·0001). At 0·99 sensitivity, CAIMAN was also more accurate than IDaRS for two external validation cohorts (p<0·0001), but not for a third external validation cohort (p=0·10). CAIMAN provided higher specificity than IDaRS at some high-sensitivity thresholds (0·7763 vs 0·6222 for 0·95 sensitivity, 0·7126 vs 0·5407 for 0·97 sensitivity, and 0·5615 vs 0·3970 for 0·99 sensitivity on one of the external validation cohorts) and showed high classification performance in distinguishing between neoplastic biopsies (AUROC 0·9928, 95% CI 0·9927-0·9929), inflammatory biopsies (0·9658, 0·9655-0·9661), and atypical biopsies (0·9789, 0·9786-0·9792). On the three external validation cohorts, CAIMAN had AUROC values of 0·9431 (95% CI 0·9165-0·9697), 0·9576 (0·9568-0·9584), and 0·9636 (0·9615-0·9657) for the detection of atypical biopsies. Saliency maps supported the representation of disease heterogeneity in model predictions and its association with relevant histological features. INTERPRETATION: CAIMAN, with its high sensitivity in detecting atypical large-bowel biopsies, might be a promising improvement in clinical workflow efficiency and diagnostic decision making in prescreening of typical colorectal biopsies. FUNDING: The Pathology Image Data Lake for Analytics, Knowledge and Education Centre of Excellence; the UK Government's Industrial Strategy Challenge Fund; and Innovate UK on behalf of UK Research and Innovation.

A Federated Learning Approach to Tumor Detection in Colon Histology Images.

Federated learning (FL), a relatively new area of research in medical image analysis, enables collaborative learning of a federated deep learning model without sharing the data of participating clients. In this paper, we propose FedDropoutAvg, a new federated learning approach for detection of tumor in images of colon tissue slides. The proposed method leverages the power of dropout, a commonly employed scheme to avoid overfitting in neural networks, in both client selection and federated averaging processes. We examine FedDropoutAvg against other FL benchmark algorithms for two different image classification tasks using a publicly available multi-site histopathology image dataset. We train and test the proposed model on a large dataset consisting of 1.2 million image tiles from 21 different sites. For testing the generalization of all models, we select held-out test sets from sites that were not used during training. We show that the proposed approach outperforms other FL methods and reduces the performance gap (to less than 3% in terms of AUC on independent test sites) between FL and a central deep learning model that requires all data to be shared for centralized training, demonstrating the potential of the proposed FedDropoutAvg model to be more generalizable than other state-of-the-art federated models. To the best of our knowledge, ours is the first study to effectively utilize the dropout strategy in a federated setting for tumor detection in histology images.

Evaluation of tumour infiltrating lymphocytes in luminal breast cancer using artificial intelligence.

BACKGROUND: Tumour infiltrating lymphocytes (TILs) are a prognostic parameter in triple-negative and human epidermal growth factor receptor 2 (HER2)-positive breast cancer (BC). However, their role in luminal (oestrogen receptor positive and HER2 negative (ER + /HER2-)) BC remains unclear. In this study, we used artificial intelligence (AI) to assess the prognostic significance of TILs in a large well-characterised cohort of luminal BC. METHODS: Supervised deep learning model analysis of Haematoxylin and Eosin (H&E)-stained whole slide images (WSI) was applied to a cohort of 2231 luminal early-stage BC patients with long-term follow-up. Stromal TILs (sTILs) and intratumoural TILs (tTILs) were quantified and their spatial distribution within tumour tissue, as well as the proportion of stroma involved by sTILs were assessed. The association of TILs with clinicopathological parameters and patient outcome was determined. RESULTS: A strong positive linear correlation was observed between sTILs and tTILs. High sTILs and tTILs counts, as well as their proximity to stromal and tumour cells (co-occurrence) were associated with poor clinical outcomes and unfavourable clinicopathological parameters including high tumour grade, lymph node metastasis, large tumour size, and young age. AI-based assessment of the proportion of stroma composed of sTILs (as assessed visually in routine practice) was not predictive of patient outcome. tTILs was an independent predictor of worse patient outcome in multivariate Cox Regression analysis. CONCLUSION: AI-based detection of TILs counts, and their spatial distribution provides prognostic value in luminal early-stage BC patients. The utilisation of AI algorithms could provide a comprehensive assessment of TILs as a morphological variable in WSIs beyond eyeballing assessment.

WSIC: a Python package and command-line interface for fast whole slide image conversion.

Summary: Whole slide images (WSIs) are multi-gigapixel images of tissue sections, which are used in digital and computational pathology workflows. WSI datasets are commonly heterogeneous collections of proprietary or niche specialized formats which are challenging to handle. This note describes an open-source Python application for efficiently converting between WSI formats, including common, open, and emerging cloud-friendly formats. WSIC is a software tool that can quickly convert WSI files across various formats. It has a high performance and maintains the resolution metadata of the original images. WSIC is ideal for pre-processing large-scale WSI datasets with different file types. Availability and implementation: Source code is available on GitHub at https://github.com/John-P/wsic/ under a permissive licence. WSIC is also available as a package on PyPI at https://pypi.org/project/WSIC/.