Adapting SAM to Histopathology Images for Tumor Bud Segmentation in Colorectal Cancer.

Colorectal cancer (CRC) is the third most common cancer in the United States. Tumor Budding (TB) detection and quantification are crucial yet labor-intensive steps in determining the CRC stage through the analysis of histopathology images. To help with this process, we adapt the Segment Anything Model (SAM) on the CRC histopathology images to segment TBs using SAM-Adapter. In this approach, we automatically take task-specific prompts from CRC images and train the SAM model in a parameter-efficient way. We compare the predictions of our model with the predictions from a trained-from-scratch model using the annotations from a pathologist. As a result, our model achieves an intersection over union (IoU) of 0.65 and an instance-level Dice score of 0.75, which are promising in matching the pathologist's TB annotation. We believe our study offers a novel solution to identify TBs on H&E-stained histopathology images. Our study also demonstrates the value of adapting the foundation model for pathology image segmentation tasks.

Enhancing Colorectal Cancer Tumor Bud Detection Using Deep Learning from Routine H&E-Stained Slides.

Tumor budding refers to a cluster of one to four tumor cells located at the tumor-invasive front. While tumor budding is a prognostic factor for colorectal cancer, counting and grading tumor budding are time consuming and not highly reproducible. There could be high inter- and intra-reader disagreement on H&E evaluation. This leads to the noisy training (imperfect ground truth) of deep learning algorithms, resulting in high variability and losing their ability to generalize on unseen datasets. Pan-cytokeratin staining is one of the potential solutions to enhance the agreement, but it is not routinely used to identify tumor buds and can lead to false positives. Therefore, we aim to develop a weakly-supervised deep learning method for tumor bud detection from routine H&E-stained images that does not require strict tissue-level annotations. We also propose Bayesian Multiple Instance Learning (BMIL) that combines multiple annotated regions during the training process to further enhance the generalizability and stability in tumor bud detection. Our dataset consists of 29 colorectal cancer H&E-stained images that contain 115 tumor buds per slide on average. In six-fold cross-validation, our method demonstrated an average precision and recall of 0.94, and 0.86 respectively. These results provide preliminary evidence of the feasibility of our approach in improving the generalizability in tumor budding detection using H&E images while avoiding the need for non-routine immunohistochemical staining methods.

Few-shot Tumor Bud Segmentation Using Generative Model in Colorectal Carcinoma.

Current deep learning methods in histopathology are limited by the small amount of available data and time consumption in labeling the data. Colorectal cancer (CRC) tumor budding quantification performed using H&E-stained slides is crucial for cancer staging and prognosis but is subject to labor-intensive annotation and human bias. Thus, acquiring a large-scale, fully annotated dataset for training a tumor budding (TB) segmentation/detection system is difficult. Here, we present a DatasetGAN-based approach that can generate essentially an unlimited number of images with TB masks from a moderate number of unlabeled images and a few annotated images. The images generated by our model closely resemble the real colon tissue on H&E-stained slides. We test the performance of this model by training a downstream segmentation model, UNet++, on the generated images and masks. Our results show that the trained UNet++ model can achieve reasonable TB segmentation performance, especially at the instance level. This study demonstrates the potential of developing an annotation-efficient segmentation model for automatic TB detection and quantification.

Predicting response to neoadjuvant chemotherapy for colorectal liver metastasis using deep learning on prechemotherapy cross-sectional imaging.

BACKGROUND AND OBJECTIVES: Deep learning models (DLMs) are applied across domains of health sciences to generate meaningful predictions. DLMs make use of neural networks to generate predictions from discrete data inputs. This study employs DLM on prechemotherapy cross-sectional imaging to predict patients' response to neoadjuvant chemotherapy. METHODS: Adult patients with colorectal liver metastasis who underwent surgery after neoadjuvant chemotherapy were included. A DLM was trained on computed tomography images using attention-based multiple-instance learning. A logistic regression model incorporating clinical parameters of the Fong clinical risk score was used for comparison. Both model performances were benchmarked against the Response Evaluation Criteria in Solid Tumors criteria. A receiver operating curve was created and resulting area under the curve (AUC) was determined. RESULTS: Ninety-five patients were included, with 33,619 images available for study inclusion. Ninety-five percent of patients underwent 5-fluorouracil-based chemotherapy with oxaliplatin and/or irinotecan. Sixty percent of the patients were categorized as chemotherapy responders (30% reduction in tumor diameter). The DLM had an AUC of 0.77. The AUC for the clinical model was 0.41. CONCLUSIONS: Image-based DLM for prediction of response to neoadjuvant chemotherapy in patients with colorectal cancer liver metastases was superior to a clinical-based model. These results demonstrate potential to identify nonresponders to chemotherapy and guide select patients toward earlier curative resection.

Analysis of gene expression dynamics and differential expression in viral infections using generalized linear models and quasi-likelihood methods.

Introduction: Our study undertakes a detailed exploration of gene expression dynamics within human lung organ tissue equivalents (OTEs) in response to Influenza A virus (IAV), Human metapneumovirus (MPV), and Parainfluenza virus type 3 (PIV3) infections. Through the analysis of RNA-Seq data from 19,671 genes, we aim to identify differentially expressed genes under various infection conditions, elucidating the complexities of virus-host interactions. Methods: We employ Generalized Linear Models (GLMs) with Quasi-Likelihood (QL) F-tests (GLMQL) and introduce the novel Magnitude-Altitude Score (MAS) and Relaxed Magnitude-Altitude Score (RMAS) algorithms to navigate the intricate landscape of RNA-Seq data. This approach facilitates the precise identification of potential biomarkers, highlighting the host's reliance on innate immune mechanisms. Our comprehensive methodological framework includes RNA extraction, library preparation, sequencing, and Gene Ontology (GO) enrichment analysis to interpret the biological significance of our findings. Results: The differential expression analysis unveils significant changes in gene expression triggered by IAV, MPV, and PIV3 infections. The MAS and RMAS algorithms enable focused identification of biomarkers, revealing a consistent activation of interferon-stimulated genes (e.g., IFIT1, IFIT2, IFIT3, OAS1) across all viruses. Our GO analysis provides deep insights into the host's defense mechanisms and viral strategies exploiting host cellular functions. Notably, changes in cellular structures, such as cilium assembly and mitochondrial ribosome assembly, indicate a strategic shift in cellular priorities. The precision of our methodology is validated by a 92% mean accuracy in classifying respiratory virus infections using multinomial logistic regression, demonstrating the superior efficacy of our approach over traditional methods. Discussion: This study highlights the intricate interplay between viral infections and host gene expression, underscoring the need for targeted therapeutic interventions. The stability and reliability of the MAS/RMAS ranking method, even under stringent statistical corrections, and the critical importance of adequate sample size for biomarker reliability are significant findings. Our comprehensive analysis not only advances our understanding of the host's response to viral infections but also sets a new benchmark for the identification of biomarkers, paving the way for the development of effective diagnostic and therapeutic strategies.

Employing machine learning to enhance fracture recovery insights through gait analysis.

This study aimed to explore the potential of gait analysis coupled with supervised machine learning models as a predictive tool for assessing post-injury complications such as infection, malunion, or hardware irritation among individuals with lower extremity fractures. We prospectively identified participants with lower extremity fractures at a tertiary academic center. These participants underwent gait analysis with a chest-mounted inertial measurement unit device. Using customized software, the raw gait data were preprocessed, emphasizing 12 essential gait variables. The data were standardized, and several machine learning models, including XGBoost, logistic regression, support vector machine, LightGBM, and Random Forest, were trained, tested, and evaluated. Special attention was given to class imbalance, addressed using the synthetic minority oversampling technique (SMOTE). Additionally, we introduced a novel methodology to compute the post-injury recovery rate for gait variables, which operates independently of the time difference between the gait analyses of different participants. XGBoost was identified as the optimal model both before and after the application of SMOTE. Before using SMOTE, the model achieved an average test area under the ROC curve (AUC) of 0.90, with a 95% confidence interval (CI) of [0.79, 1.00], and an average test accuracy of 86%, with a 95% CI of [75%, 97%]. Through feature importance analysis, a pivotal role was attributed to the duration between the occurrence of the injury and the initial gait analysis. Data patterns over time revealed early aggressive physiological compensations, followed by stabilization phases, underscoring the importance of prompt gait analysis. χ2 analysis indicated a statistically significant higher readmission rate among participants with underlying medical conditions (p = 0.04). Although the complication rate was also higher in this group, the association did not reach statistical significance (p = 0.06), suggesting a more pronounced impact of medical conditions on readmission rates rather than on complications. This study highlights the transformative potential of integrating advanced machine learning techniques like XGBoost with gait analysis for orthopedic care. The findings underscore a shift toward a data-informed, proactive approach in orthopedics, enhancing patient outcomes through early detection and intervention. The χ2 analysis added crucial insights into the broader clinical implications, advocating for a comprehensive treatment strategy that accounts for the patient's overall health profile. The research paves the way for personalized, predictive medical care in orthopedics, emphasizing the importance of timely and tailored patient assessments.

Differential diagnosis of frontotemporal dementia subtypes with explainable deep learning on structural MRI.

Background: Frontotemporal dementia (FTD) represents a collection of neurobehavioral and neurocognitive syndromes that are associated with a significant degree of clinical, pathological, and genetic heterogeneity. Such heterogeneity hinders the identification of effective biomarkers, preventing effective targeted recruitment of participants in clinical trials for developing potential interventions and treatments. In the present study, we aim to automatically differentiate patients with three clinical phenotypes of FTD, behavioral-variant FTD (bvFTD), semantic variant PPA (svPPA), and nonfluent variant PPA (nfvPPA), based on their structural MRI by training a deep neural network (DNN). Methods: Data from 277 FTD patients (173 bvFTD, 63 nfvPPA, and 41 svPPA) recruited from two multi-site neuroimaging datasets: the Frontotemporal Lobar Degeneration Neuroimaging Initiative and the ARTFL-LEFFTDS Longitudinal Frontotemporal Lobar Degeneration databases. Raw T1-weighted MRI data were preprocessed and parcellated into patch-based ROIs, with cortical thickness and volume features extracted and harmonized to control the confounding effects of sex, age, total intracranial volume, cohort, and scanner difference. A multi-type parallel feature embedding framework was trained to classify three FTD subtypes with a weighted cross-entropy loss function used to account for unbalanced sample sizes. Feature visualization was achieved through post-hoc analysis using an integrated gradient approach. Results: The proposed differential diagnosis framework achieved a mean balanced accuracy of 0.80 for bvFTD, 0.82 for nfvPPA, 0.89 for svPPA, and an overall balanced accuracy of 0.84. Feature importance maps showed more localized differential patterns among different FTD subtypes compared to groupwise statistical mapping. Conclusion: In this study, we demonstrated the efficiency and effectiveness of using explainable deep-learning-based parallel feature embedding and visualization framework on MRI-derived multi-type structural patterns to differentiate three clinically defined subphenotypes of FTD: bvFTD, nfvPPA, and svPPA, which could help with the identification of at-risk populations for early and precise diagnosis for intervention planning.

Translating prognostic quantification of c-MYC and BCL2 from tissue microarrays to whole slide images in diffuse large B-cell lymphoma using deep learning.

BACKGROUND: c-MYC and BCL2 positivity are important prognostic factors for diffuse large B-cell lymphoma. However, manual quantification is subject to significant intra- and inter-observer variability. We developed an automated method for quantification in whole-slide images of tissue sections where manual quantification requires evaluating large areas of tissue with possibly heterogeneous staining. We train this method using annotations of tumor positivity in smaller tissue microarray cores where expression and staining are more homogeneous and then translate this model to whole-slide images. METHODS: Our method applies a technique called attention-based multiple instance learning to regress the proportion of c-MYC-positive and BCL2-positive tumor cells from pathologist-scored tissue microarray cores. This technique does not require annotation of individual cell nuclei and is trained instead on core-level annotations of percent tumor positivity. We translate this model to scoring of whole-slide images by tessellating the slide into smaller core-sized tissue regions and calculating an aggregate score. Our method was trained on a public tissue microarray dataset from Stanford and applied to whole-slide images from a geographically diverse multi-center cohort produced by the Lymphoma Epidemiology of Outcomes study. RESULTS: In tissue microarrays, the automated method had Pearson correlations of 0.843 and 0.919 with pathologist scores for c-MYC and BCL2, respectively. When utilizing standard clinical thresholds, the sensitivity/specificity of our method was 0.743 / 0.963 for c-MYC and 0.938 / 0.951 for BCL2. For double-expressors, sensitivity and specificity were 0.720 and 0.974. When translated to the external WSI dataset scored by two pathologists, Pearson correlation was 0.753 & 0.883 for c-MYC and 0.749 & 0.765 for BCL2, and sensitivity/specificity was 0.857/0.991 & 0.706/0.930 for c-MYC, 0.856/0.719 & 0.855/0.690 for BCL2, and 0.890/1.00 & 0.598/0.952 for double-expressors. Survival analysis demonstrates that for progression-free survival, model-predicted TMA scores significantly stratify double-expressors and non double-expressors (p = 0.0345), whereas pathologist scores do not (p = 0.128). CONCLUSIONS: We conclude that proportion of positive stains can be regressed using attention-based multiple instance learning, that these models generalize well to whole slide images, and that our models can provide non-inferior stratification of progression-free survival outcomes.

One label is all you need: Interpretable AI-enhanced histopathology for oncology.

Artificial Intelligence (AI)-enhanced histopathology presents unprecedented opportunities to benefit oncology through interpretable methods that require only one overall label per hematoxylin and eosin (H&E) slide with no tissue-level annotations. We present a structured review of these methods organized by their degree of verifiability and by commonly recurring application areas in oncological characterization. First, we discuss morphological markers (tumor presence/absence, metastases, subtypes, grades) in which AI-identified regions of interest (ROIs) within whole slide images (WSIs) verifiably overlap with pathologist-identified ROIs. Second, we discuss molecular markers (gene expression, molecular subtyping) that are not verified via H&E but rather based on overlap with positive regions on adjacent tissue. Third, we discuss genetic markers (mutations, mutational burden, microsatellite instability, chromosomal instability) that current technologies cannot verify if AI methods spatially resolve specific genetic alterations. Fourth, we discuss the direct prediction of survival to which AI-identified histopathological features quantitatively correlate but are nonetheless not mechanistically verifiable. Finally, we discuss in detail several opportunities and challenges for these one-label-per-slide methods within oncology. Opportunities include reducing the cost of research and clinical care, reducing the workload of clinicians, personalized medicine, and unlocking the full potential of histopathology through new imaging-based biomarkers. Current challenges include explainability and interpretability, validation via adjacent tissue sections, reproducibility, data availability, computational needs, data requirements, domain adaptability, external validation, dataset imbalances, and finally commercialization and clinical potential. Ultimately, the relative ease and minimum upfront cost with which relevant data can be collected in addition to the plethora of available AI methods for outcome-driven analysis will surmount these current limitations and achieve the innumerable opportunities associated with AI-driven histopathology for the benefit of oncology.

Attention2Minority: A salient instance inference-based multiple instance learning for classifying small lesions in whole slide images.

Multiple instance learning (MIL) models have achieved remarkable success in analyzing whole slide images (WSIs) for disease classification problems. However, with regard to giga-pixel WSI classification problems, current MIL models are often incapable of differentiating a WSI with extremely small tumor lesions. This minute tumor-to-normal area ratio in a MIL bag inhibits the attention mechanism from properly weighting the areas corresponding to minor tumor lesions. To overcome this challenge, we propose salient instance inference MIL (SiiMIL), a weakly-supervised MIL model for WSI classification. We introduce a novel representation learning for histopathology images to identify representative normal keys. These keys facilitate the selection of salient instances within WSIs, forming bags with high tumor-to-normal ratios. Finally, an attention mechanism is employed for slide-level classification based on formed bags. Our results show that salient instance inference can improve the tumor-to-normal area ratio in the tumor WSIs. As a result, SiiMIL achieves 0.9225 AUC and 0.7551 recall on the Camelyon16 dataset, which outperforms the existing MIL models. In addition, SiiMIL can generate tumor-sensitive attention heatmaps that is more interpretable to pathologists than the widely used attention-based MIL method. Our experiments imply that SiiMIL can accurately identify tumor instances, which could only take up less than 1% of a WSI, so that the ratio of tumor to normal instances within a bag can increase by two to four times.

Predicting obstructive sleep apnea severity from craniofacial images using ensemble machine learning models.

Obstructive sleep apnea (OSA) is a prevalent disease affecting 10 to 15% of Americans and nearly one billion people worldwide. It leads to multiple symptoms including daytime sleepiness; snoring, choking, or gasping during sleep; fatigue; headaches; non-restorative sleep; and insomnia due to frequent arousals. Although polysomnography (PSG) is the gold standard for OSA diagnosis, it is expensive, not universally available, and time-consuming, so many patients go undiagnosed due to lack of access to the test. Given the incomplete access and high cost of PSG, many studies are seeking alternative diagnosis approaches based on different data modalities. Here, we propose a machine learning model to predict OSA severity from 2D frontal view craniofacial images. In a cross-validation study of 280 patients, our method achieves an average AUC of 0.780. In comparison, the craniofacial analysis model proposed by a recent study only achieves 0.638 AUC on our dataset. The proposed model also outperforms the widely used STOP-BANG OSA screening questionnaire, which achieves an AUC of 0.52 on our dataset. Our findings indicate that deep learning has the potential to significantly reduce the cost of OSA diagnosis.

Dynamic 2-deoxy-D-glucose-enhanced multispectral optoacoustic tomography for assessing metabolism and vascular hemodynamics of breast cancer.

Clinical tools for measuring tumor vascular hemodynamics, such as dynamic contrast-enhanced MRI, are clinically important to assess tumor properties. Here we explored the use of multispectral optoacoustic tomography (MSOT), which has a high spatial and temporal resolution, to measure the intratumoral pharmacokinetics of a near-infrared-dye-labeled 2-Deoxyglucose, 2-DG-800, in orthotropic 2-LMP breast tumors in mice. As uptake of 2-DG-800 is dependent on both vascular properties, and glucose transporter activity - a widely-used surrogate for metabolism, we evaluate hemodynamics of 2-DG-MP by fitting the dynamic MSOT signal of 2-DG-800 into two-compartment models including the extended Tofts model (ETM) and reference region model (RRM). We showed that dynamic 2-DG-enhanced MSOT (DGE-MSOT) is powerful in acquiring hemodynamic rate constants, including Ktrans and Kep, via systemically injecting a low dose of 2-DG-800 (0.5 µmol/kg b.w.). In our study, both ETM and RRM are efficient in deriving hemodynamic parameters in the tumor. Area-under-curve (AUC) values (which correlate to metabolism), and Ktrans and Kep values, can effectively distinguish tumor from muscle. Hemodynamic parameters also demonstrated correlations to hemoglobin, oxyhemoglobin, and blood oxygen level (SO2) measurements by spectral unmixing of the MSOT data. Together, our study for the first time demonstrated the capability of DGE-MSOT in assessing vascular hemodynamics of tumors.

NRK-ABMIL: Subtle Metastatic Deposits Detection for Predicting Lymph Node Metastasis in Breast Cancer Whole-Slide Images.

The early diagnosis of lymph node metastasis in breast cancer is essential for enhancing treatment outcomes and overall prognosis. Unfortunately, pathologists often fail to identify small or subtle metastatic deposits, leading them to rely on cytokeratin stains for improved detection, although this approach is not without its flaws. To address the need for early detection, multiple-instance learning (MIL) has emerged as the preferred deep learning method for automatic tumor detection on whole slide images (WSIs). However, existing methods often fail to identify some small lesions due to insufficient attention to small regions. Attention-based multiple-instance learning (ABMIL)-based methods can be particularly problematic because they may focus too much on normal regions, leaving insufficient attention for small-tumor lesions. In this paper, we propose a new ABMIL-based model called normal representative keyset ABMIL (NRK-ABMIL), which addresseses this issue by adjusting the attention mechanism to give more attention to lesions. To accomplish this, the NRK-ABMIL creates an optimal keyset of normal patch embeddings called the normal representative keyset (NRK). The NRK roughly represents the underlying distribution of all normal patch embeddings and is used to modify the attention mechanism of the ABMIL. We evaluated NRK-ABMIL on the publicly available Camelyon16 and Camelyon17 datasets and found that it outperformed existing state-of-the-art methods in accurately identifying small tumor lesions that may spread over a few patches. Additionally, the NRK-ABMIL also performed exceptionally well in identifying medium/large tumor lesions.

DNA-caged nanoparticles via electrostatic self-assembly.

DNA-modified nanoparticles enable DNA sensing and therapeutics in nanomedicine and are also crucial for nanoparticle self-assembly with DNA-based materials. However, methods to conjugate DNA to nanoparticle surfaces are limited, inefficient, and lack control. Inspired by DNA tile nanotechnology, we demonstrate a new approach to nanoparticle modification based on electrostatic attraction between negatively charged DNA tiles and positively charged nanoparticles. This approach does not disrupt nanoparticle surfaces and leverages the programmability of DNA nanotechnology to control DNA presentation. We demonstrated this approach using a vareity of nanoparticles, including polymeric micelles, polystyrene beads, gold nanoparticles, and superparamagnetic iron oxide nanoparticles with sizes ranging from 5-20 nm in diameter. DNA cage formation was confirmed through transmission electron microscopy (TEM), neutralization of zeta potential, and a series of fluorescence experiments. DNA cages present "handle" sequences that can be used for reversible target attachment or self-assembly. Handle functionality was verified in solution, at the solid-liquid interface, and inside fixed cells, corresponding to applications in biosensing, DNA microarrays, and erasable immunocytochemistry. These experiments demonstrate the versatility of the electrostatic DNA caging approach and provide a new pathway to nanoparticle modification with DNA that will empower further applications of these materials in medicine and materials science.

Metaplastic Breast Cancer: Current Understanding and Future Directions.

Metaplastic breast cancers (MBC) encompass a group of highly heterogeneous tumors which share the ability to differentiate into squamous, mesenchymal or neuroectodermal components. While often termed rare breast tumors, given the relatively high prevalence of breast cancer, they are seen with some frequency. Depending upon the definition applied, MBC represents 0.2% to 1% of breast cancers diagnosed in the United States. Less is known about the epidemiology of MBC globally, though a growing number of reports are providing information on this. These tumors are often more advanced at presentation relative to breast cancer broadly. While more indolent subtypes exist, the majority of MBC subtypes are associated with inferior survival. MBC is most commonly of triple-negative phenotype. In less common hormone receptor positive MBCs, hormone receptor status appears not to be prognostic. In contrast, relatively rare HER2-positive MBCs are associated with superior outcomes. Multiple potentially targetable molecular features are overrepresented in MBC including DNA repair deficiency signatures and PIK3/AKT/mTOR and WNT pathways alterations. Data on the prevalence of targets for novel antibody-drug conjugates is also emerging. While chemotherapy appears to be less active in MBC than in other breast cancer subtypes, efficacy is seen in some MBCs. Disease-specific trials, as well as reports of exceptional responses, may provide clues for novel approaches to this often hard-to-treat breast cancer. Strategies which harness newer research tools, such as large data and artificial intelligence hold the promise of overcoming historic barriers to the study of uncommon tumors and could markedly advance disease-specific understanding in MBC.

Machine learning-based analytics of the impact of the Covid-19 pandemic on alcohol consumption habit changes among United States healthcare workers.

The COVID-19 pandemic is a global health concern that has spread around the globe. Machine Learning is promising in the fight against the COVID-19 pandemic. Machine learning and artificial intelligence have been employed by various healthcare providers, scientists, and clinicians in medical industries in the fight against COVID-19 disease. In this paper, we discuss the impact of the Covid-19 pandemic on alcohol consumption habit changes among healthcare workers in the United States during the first wave of the Covid-19 pandemic. We utilize multiple supervised and unsupervised machine learning methods and models such as decision trees, logistic regression, support vector machines, multilayer perceptron, XGBoost, CatBoost, LightGBM, AdaBoost, Chi-Squared Test, mutual information, KModes clustering and the synthetic minority oversampling technique on a mental health survey data obtained from the University of Michigan Inter-University Consortium for Political and Social Research to investigate the links between COVID-19-related deleterious effects and changes in alcohol consumption habits among healthcare workers. Through the interpretation of the supervised and unsupervised methods, we have concluded that healthcare workers whose children stayed home during the first wave in the US consumed more alcohol. We also found that the work schedule changes due to the Covid-19 pandemic led to a change in alcohol use habits. Changes in food consumption, age, gender, geographical characteristics, changes in sleep habits, the amount of news consumption, and screen time are also important predictors of an increase in alcohol use among healthcare workers in the United States.

BCR-Net: A deep learning framework to predict breast cancer recurrence from histopathology images.

Breast cancer is the most common malignancy in women, with over 40,000 deaths annually in the United States alone. Clinicians often rely on the breast cancer recurrence score, Oncotype DX (ODX), for risk stratification of breast cancer patients, by using ODX as a guide for personalized therapy. However, ODX and similar gene assays are expensive, time-consuming, and tissue destructive. Therefore, developing an AI-based ODX prediction model that identifies patients who will benefit from chemotherapy in the same way that ODX does would give a low-cost alternative to the genomic test. To overcome this problem, we developed a deep learning framework, Breast Cancer Recurrence Network (BCR-Net), which automatically predicts ODX recurrence risk from histopathology slides. Our proposed framework has two steps. First, it intelligently samples discriminative features from whole-slide histopathology images of breast cancer patients. Then, it automatically weights all features through a multiple instance learning model to predict the recurrence score at the slide level. On a dataset of H&E and Ki67 breast cancer resection whole slides images (WSIs) from 99 anonymized patients, the proposed framework achieved an overall AUC of 0.775 (68.9% and 71.1% accuracies for low and high risk) on H&E WSIs and overall AUC of 0.811 (80.8% and 79.2% accuracies for low and high risk) on Ki67 WSIs of breast cancer patients. Our findings provide strong evidence for automatically risk-stratify patients with a high degree of confidence. Our experiments reveal that the BCR-Net outperforms the state-of-the-art WSI classification models. Moreover, BCR-Net is highly efficient with low computational needs, making it practical to deploy in limited computational settings.

Machine Learning Models for Predicting the Outcomes of Surgical Treatment of Colorectal Liver Metastases.

BACKGROUND: Surgical intervention remains the cornerstone of a multidisciplinary approach in the treatment of colorectal liver metastases (CLM). Nevertheless, patient outcomes vary greatly. While predictive tools can assist decision-making and patient counseling, decades of efforts have yet to result in generating a universally adopted tool in clinical practice. STUDY DESIGN: An international collaborative database of CLM patients who underwent surgical therapy between 2000 and 2018 was used to select 1,004 operations for this study. Two different machine learning methods were applied to construct 2 predictive models for recurrence and death, using 128 clinicopathologic variables: gradient-boosted trees (GBTs) and logistic regression with bootstrapping (LRB) in a leave-one-out cross-validation. RESULTS: Median survival after resection was 47.2 months, and disease-free survival was 19.0 months, with a median follow-up of 32.0 months in the cohort. Both models had good predictive power, with GBT demonstrating a superior performance in predicting overall survival (area under the receiver operating curve [AUC] 0.773, 95% CI 0.743 to 0.801 vs LRB: AUC 0.648, 95% CI 0.614 to 0.682) and recurrence (AUC 0.635, 95% CI 0.599 to 0.669 vs LRB: AUC 0.570, 95% CI 0.535 to 0.601). Similarly, better performances were observed predicting 3- and 5-year survival, as well as 3- and 5-year recurrence, with GBT methods generating higher AUCs. CONCLUSIONS: Machine learning provides powerful tools to create predictive models of survival and recurrence after surgery for CLM. The effectiveness of both machine learning models varies, but on most occasions, GBT outperforms LRB. Prospective validation of these models lays the groundwork to adopt them in clinical practice.

Advances in Artificial Intelligence to Diagnose Otitis Media: State of the Art Review.

OBJECTIVE: Otitis media (OM) is a model disease for developing, validating, and implementing artificial intelligence (AI) techniques. We aim to review the state of the art applications of AI used to diagnose OM in pediatric and adult populations. DATA SOURCES: Several comprehensive databases were searched to identify all articles that applied AI technologies to diagnose OM. REVIEW METHODS: Relevant articles from January 2010 through May 2021 were identified by title and abstract. Articles were excluded if they did not discuss AI in conjunction with diagnosing OM. References of included studies and relevant review articles were cross-referenced to identify any additional studies. CONCLUSION: Title and abstract screening resulted in full-text retrieval of 40 articles that met initial screening parameters. Of this total, secondary review articles (n = 7) and commentary-based articles (n = 2) were removed, as were articles that did not specifically discuss AI and OM diagnosis (n = 5), leaving 25 articles for review. Applications of AI technologies specific to diagnosing OM included machine learning and natural language processing (n = 23) and prototype approaches (n = 2). IMPLICATIONS FOR PRACTICE: This review emphasizes the utility of AI techniques to automate and aid in diagnosing OM. Although these techniques are still in the development and testing stages, AI has the potential to improve the practice of otolaryngologists and primary care clinicians by increasing the efficiency and accuracy of diagnoses.