A prognostic model for overall survival of patients with early-stage non-small cell lung cancer: a multicentre, retrospective study

Computer-Aided Assessment of Repigmentation Rates in Vitiligo Patients: Implications for Treatment Efficacy - A Retrospective Study

Precise evaluation of repigmentation in vitiligo patients is crucial for monitoring treatment efficacy and enhancing patient satisfaction. This study aimed to develop a computer-aided system for assessing repigmentation rates in vitiligo patients, providing valuable insights for clinical practice. A retrospective study was conducted at the Dermatology Department of Shenzhen People's Hospital between June 2019 and November 2022. Pre- and post-treatment images of vitiligo lesions under Wood's lamp were collected, involving 833 participants stratified by sex, age, and pigmentation patterns. Our results demonstrated that the marginal pigmentation pattern exhibited a higher repigmentation rate of 72% compared with the central non-follicular pattern at 45%. Males had a slightly higher average repigmentation rate of 0.37 in comparison to females at 0.33. Among age groups, individuals aged 0-20 years showed the highest average repigmentation rate at 0.41, while the oldest age group (61-80 years) displayed the lowest rate at 0.25. Analysis of multiple visits identified the marginal pattern as the most prevalent (60%), with a mean repigmentation rate of 40%. This study introduced a computational system for evaluating vitiligo repigmentation rates, enhancing our comprehension of patient responses, and ultimately contributing to enhanced clinical care.

Mapping the landscape of histomorphological cancer phenotypes using self-supervised learning on unannotated pathology slides

Cancer diagnosis and management depend upon the extraction of complex information from microscopy images by pathologists, which requires time-consuming expert interpretation prone to human bias. Supervised deep learning approaches have proven powerful, but are inherently limited by the cost and quality of annotations used for training. Therefore, we present Histomorphological Phenotype Learning, a self-supervised methodology requiring no labels and operating via the automatic discovery of discriminatory features in image tiles. Tiles are grouped into morphologically similar clusters which constitute an atlas of histomorphological phenotypes (HP-Atlas), revealing trajectories from benign to malignant tissue via inflammatory and reactive phenotypes. These clusters have distinct features which can be identified using orthogonal methods, linking histologic, molecular and clinical phenotypes. Applied to lung cancer, we show that they align closely with patient survival, with histopathologically recognised tumor types and growth patterns, and with transcriptomic measures of immunophenotype. These properties are maintained in a multi-cancer study.

Morphological diversity of cancer cells predicts prognosis across tumor types

BACKGROUND

Intratumor heterogeneity drives disease progression and treatment resistance, which can lead to poor patient outcomes. Here, we present a computational approach for quantification of cancer cell diversity in routine hematoxylin-eosin-stained histopathology images.

METHODS

We analyzed publicly available digitized whole-slide hematoxylin-eosin images for 2000 patients. Four tumor types were included: lung, head and neck, colon, and rectal cancers, representing major histology subtypes (adenocarcinomas and squamous cell carcinomas). We performed single-cell analysis on hematoxylin-eosin images and trained a deep convolutional autoencoder to automatically learn feature representations of individual cancer nuclei. We then computed features of intranuclear variability and internuclear diversity to quantify tumor heterogeneity. Finally, we used these features to build a machine-learning model to predict patient prognosis.

RESULTS

A total of 68 million cancer cells were segmented and analyzed for nuclear image features. We discovered multiple morphological subtypes of cancer cells (range = 15-20) that co-exist within the same tumor, each with distinct phenotypic characteristics. Moreover, we showed that a higher morphological diversity is associated with chromosome instability and genomic aneuploidy. A machine-learning model based on morphological diversity demonstrated independent prognostic values across tumor types (hazard ratio range = 1.62-3.23, P < .035) in validation cohorts and further improved prognostication when combined with clinical risk factors.

CONCLUSIONS

Our study provides a practical approach for quantifying intratumor heterogeneity based on routine histopathology images. The cancer cell diversity score can be used to refine risk stratification and inform personalized treatment strategies.

The activated CD36-Src axis promotes lung adenocarcinoma cell proliferation and actin remodeling-involved metastasis in high-fat environment

Obesity/overweight and lipid metabolism disorders have become increased risk factors for lung cancer. Fatty acid translocase CD36 promotes cellular uptake of fatty acids. Whether and how CD36 facilitates lung adenocarcinoma (LUAD) growth in high-fat environment is unknown. Here, we demonstrated that palmitic acid (PA) or high-fat diet (HFD) promoted LUAD cell proliferation and metastasis in a CD36-dependent manner. Mechanistically, CD36 translocated from cytoplasm to cell membrane and interacted with Src kinase upon PA stimulation in human LUAD cells. Akt and ERK, downstream of Src, were then activated to mediate LUAD cell proliferation and metastasis. Furthermore, PA treatment promoted CD36 sarcolemmal translocation, where it activated Rac1 and upregulated MMP-9 through Src-Akt/ERK pathway, resulting in redistribution of cortactin, N-WASP and Arp2/3, and finally led to occurrence of finger-like protrusions of actin on cell surface to enhance cell metastasis. Compared with normal-chew diet (NCD) mice, the HFD group exhibited higher level of blood free fatty acid (FFA) and cholesterol (TC), developed larger xenograft LUAD tumors and enhanced tumor cell metastatic potential, which were accompanied by obvious sarcolemmal actin remodeling and were blocked by simultaneous CD36 knockdown in LUAD cells. Consistently, xenografted and tail vein-injected scramble-RNA-A549 cells but not CD36-shRNA-A549 in HFD mice formed metastatic LUAD tumors on the lung. CD36 inhibitor SSO significantly inhibited LUAD cell metastasis to the lung. Collectively, CD36 initiates Src signaling to promote LUAD cell proliferation and actin remodeling-involved metastasis under high-fat environment. Our study provides the new insights that CD36 is a valid target for LUAD therapy.

USNAP: fast unique dense region detection and its application to lung cancer

MOTIVATION

Many real-world problems can be modeled as annotated graphs. Scalable graph algorithms that extract actionable information from such data are in demand since these graphs are large, varying in topology, and have diverse node/edge annotations. When these graphs change over time they create dynamic graphs, and open the possibility to find patterns across different time points. In this article, we introduce a scalable algorithm that finds unique dense regions across time points in dynamic graphs. Such algorithms have applications in many different areas, including the biological, financial, and social domains.

RESULTS

There are three important contributions to this manuscript. First, we designed a scalable algorithm, USNAP, to effectively identify dense subgraphs that are unique to a time stamp given a dynamic graph. Importantly, USNAP provides a lower bound of the density measure in each step of the greedy algorithm. Second, insights and understanding obtained from validating USNAP on real data show its effectiveness. While USNAP is domain independent, we applied it to four non-small cell lung cancer gene expression datasets. Stages in non-small cell lung cancer were modeled as dynamic graphs, and input to USNAP. Pathway enrichment analyses and comprehensive interpretations from literature show that USNAP identified biologically relevant mechanisms for different stages of cancer progression. Third, USNAP is scalable, and has a time complexity of O(m+mc log nc+nc log nc), where m is the number of edges, and n is the number of vertices in the dynamic graph; mc is the number of edges, and nc is the number of vertices in the collapsed graph.

AVAILABILITY AND IMPLEMENTATION

The code of USNAP is available at https://www.cs.utoronto.ca/~juris/data/USNAP22.

SMILE: Cost-sensitive multi-task learning for nuclear segmentation and classification with imbalanced annotations

High throughput nuclear segmentation and classification of whole slide images (WSIs) is crucial to biological analysis, clinical diagnosis and precision medicine. With the advances of CNN algorithms and the continuously growing datasets, considerable progress has been made in nuclear segmentation and classification. However, few works consider how to reasonably deal with nuclear heterogeneity in the following two aspects: imbalanced data distribution and diversified morphology characteristics. The minority classes might be dominated by the majority classes due to the imbalanced data distribution and the diversified morphology characteristics may lead to fragile segmentation results. In this study, a cost-Sensitive MultI-task LEarning (SMILE) framework is conducted to tackle the data heterogeneity problem. Based on the most popular multi-task learning backbone in nuclei segmentation and classification, we propose a multi-task correlation attention (MTCA) to perform feature interaction of multiple high relevant tasks to learn better feature representation. A cost-sensitive learning strategy is proposed to solve the imbalanced data distribution by increasing the penalization for the error classification of the minority classes. Furthermore, we propose a novel post-processing step based on the coarse-to-fine marker-controlled watershed scheme to alleviate fragile segmentation when nuclei are with large size and unclear contour. Extensive experiments show that the proposed method achieves state-of-the-art performances on CoNSeP and MoNuSAC 2020 datasets. The code is available at: https://github.com/panxipeng/nuclear_segandcls.

Development and Validation of a Machine Learning Model for Detection and Classification of Tertiary Lymphoid Structures in Gastrointestinal Cancers

IMPORTANCE

Tertiary lymphoid structures (TLSs) are associated with a favorable prognosis and improved response to cancer immunotherapy. The current approach for evaluation of TLSs is limited by interobserver variability and high complexity and cost of specialized imaging techniques.

OBJECTIVE

To develop a machine learning model for automated and quantitative evaluation of TLSs based on routine histopathology images.

DESIGN, SETTING, AND PARTICIPANTS

In this multicenter, international diagnostic/prognostic study, an interpretable machine learning model was developed and validated for automated detection, enumeration, and classification of TLSs in hematoxylin-eosin-stained images. A quantitative scoring system for TLSs was proposed, and its association with survival was investigated in patients with 1 of 6 types of gastrointestinal cancers. Data analysis was performed between June 2021 and March 2022.

MAIN OUTCOMES AND MEASURES

The diagnostic accuracy for classification of TLSs into 3 maturation states and the association of TLS score with survival were investigated.

RESULTS

A total of 1924 patients with gastrointestinal cancer from 7 independent cohorts (median [IQR] age ranging from 57 [49-64] years to 68 [58-77] years; proportion by sex ranging from 214 of 409 patients who were male [52.3%] to 134 of 155 patients who were male [86.5%]). The machine learning model achieved high accuracies for detecting and classifying TLSs into 3 states (TLS1: 97.7%; 95% CI, 96.4%-99.0%; TLS2: 96.3%; 95% CI, 94.6%-98.0%; TLS3: 95.7%; 95% CI, 93.9%-97.5%). TLSs were detected in 62 of 155 esophageal cancers (40.0%) and up to 267 of 353 gastric cancers (75.6%). Across 6 cancer types, patients were stratified into 3 risk groups (higher and lower TLS score and no TLS) and survival outcomes compared between groups: higher vs lower TLS score (hazard ratio [HR]; 0.27; 95% CI, 0.18-0.41; P < .001) and lower TLS score vs no TLSs (HR, 0.65; 95% CI, 0.56-0.76; P < .001). TLS score remained an independent prognostic factor associated with survival after adjusting for clinicopathologic variables and tumor-infiltrating lymphocytes (eg, for colon cancer: HR, 0.11; 95% CI, 0.02-0.47; P = .003).

CONCLUSIONS AND RELEVANCE

In this study, an interpretable machine learning model was developed that may allow automated and accurate detection of TLSs on routine tissue slide. This model is complementary to the cancer staging system for risk stratification in gastrointestinal cancers.

Hagnifinder: Recovering magnification information of digital histological images using deep learning

Background and objective

Training a robust cancer diagnostic or prognostic artificial intelligent model using histology images requires a large number of representative cases with labels or annotations, which are difficult to obtain. The histology snapshots available in published papers or case reports can be used to enrich the training dataset. However, the magnifications of these invaluable snapshots are generally unknown, which limits their usage. Therefore, a robust magnification predictor is required for utilizing those diverse snapshot repositories consisting of different diseases. This paper presents a magnification prediction model named Hagnifinder for H&E-stained histological images.

Methods

Hagnifinder is a regression model based on a modified convolutional neural network (CNN) that contains 3 modules: Feature Extraction Module, Regression Module, and Adaptive Scaling Module (ASM). In the training phase, the Feature Extraction Module first extracts the image features. Secondly, the ASM is proposed to address the learned feature values uneven distribution problem. Finally, the Regression Module estimates the mapping between the regularized extracted features and the magnifications. We construct a new dataset for training a robust model, named Hagni40, consisting of 94 643 H&E-stained histology image patches at 40 different magnifications of 13 types of cancer based on The Cancer Genome Atlas. To verify the performance of the Hagnifinder, we measure the accuracy of the predictions by setting the maximum allowable difference values (0.5, 1, and 5) between the predicted magnification and the actual magnification. We compare Hagnifinder with state-of-the-art methods on a public dataset BreakHis and the Hagni40.

Results

The Hagnifinder provides consistent prediction accuracy, with a mean accuracy of 98.9%, across 40 different magnifications and 13 different cancer types when Resnet50 is used as the feature extractor. Compared with the state-of-the-art methods focusing on 4-5 levels of magnification classification, the Hagnifinder achieves the best and most comparable performance in the BreakHis and Hagni40 datasets.

Conclusions

The experimental results suggest that Hagnifinder can be a valuable tool for predicting the associated magnification of any given histology image.

Prognostic score and sex-specific nomograms to predict survival in resectable lung cancer: A French nationwide study from the Epithor cohort database

Background

Prognostic assessment in patients undergoing cancer treatments is of paramount importance to plan subsequent management. In resectable lung cancer availability of an easy-to use nomogram to predict long-term outcome would be extremely useful to identify high-risk patients in the era of perioperative targeted and immune therapies.

Methods

We retrieved clinical, surgical and pathological data of all consecutive patients included in Epithor, the database of French Society of Thoracic and Cardiovascular Surgery, and operated on between 2003 and 2020 for non-small cell lung cancer in a curative intent. The primary endpoint was overall survival up to 5 years. We assessed prognostic significance of available variables using Cox modelling, in the whole dataset, and in men and in women separately, and performed temporal validation. Finally, we constructed two sex-specific nomograms. Survivals by fifths of score were assessed in the development and temporal validation sets.

Findings

The study included 62,633 patients (43,551 men and 19,082 women). Median survival time was 9.2 years. Nine factors had strong prognostic impact and were used to construct nomograms. The optimism-corrected c statistic for the prognostic score was 0.689 in the development sample, and 0.726 (95% CI 0.718-0.735) in the temporal validation sample. All differences between adjacent fifths of score were significant (P < 0.0001). Figures of 3-year OS by fifths of score were 92.2%, 83.0%, 74.3%, 64.0%, and 43.4%, respectively, in the development set and 93.3%, 88.4%, 81.0%, 73.7%, 55.7% in the temporal validation set. Performance of score was maintained when stratifying by stage of diseases.

Interpretation

In the present work, we report evidence that long-term overall survival after resection of NSCLC can be predicted by an easy to construct and use composite score taking into account both host and tumour related factors.

Funding

Epithor is funded by FSTCVS.

Multi-scale pathology image texture signature is a prognostic factor for resectable lung adenocarcinoma: a multi-center, retrospective study

BACKGROUND

Tumor histomorphology analysis plays a crucial role in predicting the prognosis of resectable lung adenocarcinoma (LUAD). Computer-extracted image texture features have been previously shown to be correlated with outcome. However, a comprehensive, quantitative, and interpretable predictor remains to be developed.

METHODS

In this multi-center study, we included patients with resectable LUAD from four independent cohorts. An automated pipeline was designed for extracting texture features from the tumor region in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) at multiple magnifications. A multi-scale pathology image texture signature (MPIS) was constructed with the discriminative texture features in terms of overall survival (OS) selected by the LASSO method. The prognostic value of MPIS for OS was evaluated through univariable and multivariable analysis in the discovery set (n = 111) and the three external validation sets (V₁, n = 115; V₂, n = 116; and V₃, n = 246). We constructed a Cox proportional hazards model incorporating clinicopathological variables and MPIS to assess whether MPIS could improve prognostic stratification. We also performed histo-genomics analysis to explore the associations between texture features and biological pathways.

RESULTS

A set of eight texture features was selected to construct MPIS. In multivariable analysis, a higher MPIS was associated with significantly worse OS in the discovery set (HR 5.32, 95%CI 1.72-16.44; P = 0.0037) and the three external validation sets (V₁: HR 2.63, 95%CI 1.10-6.29, P = 0.0292; V₂: HR 2.99, 95%CI 1.34-6.66, P = 0.0075; V₃: HR 1.93, 95%CI 1.15-3.23, P = 0.0125). The model that integrated clinicopathological variables and MPIS had better discrimination for OS compared to the clinicopathological variables-based model in the discovery set (C-index, 0.837 vs. 0.798) and the three external validation sets (V₁: 0.704 vs. 0.679; V₂: 0.728 vs. 0.666; V₃: 0.696 vs. 0.669). Furthermore, the identified texture features were associated with biological pathways, such as cytokine activity, structural constituent of cytoskeleton, and extracellular matrix structural constituent.

CONCLUSIONS

MPIS was an independent prognostic biomarker that was robust and interpretable. Integration of MPIS with clinicopathological variables improved prognostic stratification in resectable LUAD and might help enhance the quality of individualized postoperative care.

Computerized tumor-infiltrating lymphocytes density score predicts survival of patients with resectable lung adenocarcinoma

A high abundance of tumor-infiltrating lymphocytes (TILs) has a positive impact on the prognosis of patients with lung adenocarcinoma (LUAD). We aimed to develop and validate an artificial intelligence-driven pathological scoring system for assessing TILs on H&E-stained whole-slide images of LUAD. Deep learning-based methods were applied to calculate the densities of lymphocytes in cancer epithelium (DLCE) and cancer stroma (DLCS), and a risk score (WELL score) was built through linear weighting of DLCE and DLCS. Association between WELL score and patient outcome was explored in 793 patients with stage I-III LUAD in four cohorts. WELL score was an independent prognostic factor for overall survival and disease-free survival in the discovery cohort and validation cohorts. The prognostic prediction model-integrated WELL score demonstrated better discrimination performance than the clinicopathologic model in the four cohorts. This artificial intelligence-based workflow and scoring system could promote risk stratification for patients with resectable LUAD.

Integrative Histology-Genomic Analysis Predicts Hepatocellular Carcinoma Prognosis Using Deep Learning

Cancer prognosis analysis is of essential interest in clinical practice. In order to explore the prognostic power of computational histopathology and genomics, this paper constructs a multi-modality prognostic model for survival prediction. We collected 346 patients diagnosed with hepatocellular carcinoma (HCC) from The Cancer Genome Atlas (TCGA), each patient has 1-3 whole slide images (WSIs) and an mRNA expression file. WSIs were processed by a multi-instance deep learning model to obtain the patient-level survival risk scores; mRNA expression data were processed by weighted gene co-expression network analysis (WGCNA), and the top hub genes of each module were extracted as risk factors. Information from two modalities was integrated by Cox proportional hazard model to predict patient outcomes. The overall survival predictions of the multi-modality model (Concordance index (C-index): 0.746, 95% confidence interval (CI): ±0.077) outperformed these based on histopathology risk score or hub genes, respectively. Furthermore, in the prediction of 1-year and 3-year survival, the area under curve of the model achieved 0.816 and 0.810. In conclusion, this paper provides an effective workflow for multi-modality prognosis of HCC, the integration of histopathology and genomic information has the potential to assist clinical prognosis management.

Integrated pathological analysis to develop a Gal-9 based immune survival stratification to predict the outcome of lung large cell neuroendocrine carcinoma and its usefulness in immunotherapy

This study aimed to integrate the cell spatial organization to develop a Gal-9-based immune survival stratification in the lung large cell neuroendocrine carcinoma (LCNEC) and investigate its potentials to immunotherapy. The expression of Gal-9 and other twelve immune markers were evaluated in 122 cases of surgical LCNEC samples from our center using immunohistochemistry. The Gal-9-based immune survival stratification risk score was constructed and its predictive performance was evaluated. Then, we thoroughly explored the effects of Gal-9 and immune risk score on LCNEC immune pathways, immune micro-environment and immunotherapy sensitivity in different cohort and platform, and made a validation in pathology images using Histology-based Digital-Staining (HDS). In 122 LCNEC samples, 43 cases were positive Gal-9 expression on tumor cells (Gal-9 TC). Increased Gal-9 TC predicted worse overall survival. Gal-9's interaction with other immune markers added to the immune suppression and immune tolerance in LCNEC. Immune protein marker-based risk score consisting of Gal-9, CD3, CD4, PD-L1, and PD-1 was developed and validated to robustly discriminate survival high-risk or low-risk in LCNEC patients. The high-risk group characterized by immune-desert tumor had less various T cells. The low-risk group featuring immune-inflamed tumor was more likely to respond to anti-PD1 immunotherapy. HDS in 122 LCNEC samples' 108,369 cells validated that the high-risk group had more tumor cells, less stromal cells, less lymphocytes, higher tumor cell nucleic solidity and lower stromal cells nucleic solidity. An integrated pathological analysis confirms the Gal-9 based immune survival stratification is distinctively related to micro-environment status involved in immune suppression and immune tolerance and could act as a combinatorial biomarker to predict the outcome of LCNEC. These findings may help effectively stratify LCNEC patients sensitive to immunotherapy.

An Artificial Intelligence-Based Tool for Data Analysis and Prognosis in Cancer Patients: Results from the Clarify Study

Simple Summary

Cancer is associated with significant morbimortality worldwide. Although significant advances have been made in the last few decades in terms of early detection and treatment, providing personalized care remains a challenge. Artificial intelligence (AI) has emerged as a means of improving cancer care with the use of computer science. Identification of risk factors for poor prognosis and patient profiling with AI techniques and tools is feasible and has potential application in clinical settings, including surveillance management. The goal of this study is to present an AI-based solution tool for cancer patients data analysis and improve their management by identifying clinical factors associated with relapse and survival, developing a prognostic model that identifies features associated with poor prognosis, and stratifying patients by risk.

Abstract

Background: Artificial intelligence (AI) has contributed substantially in recent years to the resolution of different biomedical problems, including cancer. However, AI tools with significant and widespread impact in oncology remain scarce. The goal of this study is to present an AI-based solution tool for cancer patients data analysis that assists clinicians in identifying the clinical factors associated with poor prognosis, relapse and survival, and to develop a prognostic model that stratifies patients by risk. Materials and Methods: We used clinical data from 5275 patients diagnosed with non-small cell lung cancer, breast cancer, and non-Hodgkin lymphoma at Hospital Universitario Puerta de Hierro-Majadahonda. Accessible clinical parameters measured with a wearable device and quality of life questionnaires data were also collected. Results: Using an AI-tool, data from 5275 cancer patients were analyzed, integrating clinical data, questionnaires data, and data collected from wearable devices. Descriptive analyses were performed in order to explore the patients’ characteristics, survival probabilities were calculated, and a prognostic model identified low and high-risk profile patients. Conclusion: Overall, the reconstruction of the population’s risk profile for the cancer-specific predictive model was achieved and proved useful in clinical practice using artificial intelligence. It has potential application in clinical settings to improve risk stratification, early detection, and surveillance management of cancer patients.

Add-on individualizing prediction of nasopharyngeal carcinoma using deep-learning based on MRI: a multicentre, validation study

In nasopharyngeal carcinoma, deep-learning extracted signatures on MR images might be correlated with survival. In this study, we sought to develop an individualizing model using deep-learning MRI signatures and clinical data to predict survival and to estimate the benefit of induction chemotherapy on survivals of patients with nasopharyngeal carcinoma. Two thousand ninety-seven patients from three independent hospitals were identified and randomly assigned. When the deep-learning signatures of the primary tumor and clinically involved gross cervical lymph nodes extracted from MR images were added to the clinical data and TNM staging for the progression-free survival prediction model, the combined model achieved better prediction performance. Its application is among patients deciding on treatment regimens. Under the same conditions, with the increasing MRI signatures, the survival benefits achieved by induction chemotherapy are increased. In nasopharyngeal carcinoma, these prediction models are the first to provide an individualized estimation of survivals and model the benefit of induction chemotherapy on survivals.

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3D-CNN was employed to extract the MRI signatures of nasopharyngeal carcinoma

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The prediction model combined MRI signature, clinical data, TNM staging, and treatment

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The model improved the prediction of progression-free survival and overall survival

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The model can accurately predict individualized survival and decide treatment regimen

Histopathological Tissue Segmentation of Lung Cancer with Bilinear CNN and Soft Attention

Automatic tissue segmentation in whole-slide images (WSIs) is a critical task in hematoxylin and eosin- (H&E-) stained histopathological images for accurate diagnosis and risk stratification of lung cancer. Patch classification and stitching the classification results can fast conduct tissue segmentation of WSIs. However, due to the tumour heterogeneity, large intraclass variability and small interclass variability make the classification task challenging. In this paper, we propose a novel bilinear convolutional neural network- (Bilinear-CNN-) based model with a bilinear convolutional module and a soft attention module to tackle this problem. This method investigates the intraclass semantic correspondence and focuses on the more distinguishable features that make feature output variations relatively large between interclass. The performance of the Bilinear-CNN-based model is compared with other state-of-the-art methods on the histopathological classification dataset, which consists of 107.7 k patches of lung cancer. We further evaluate our proposed algorithm on an additional dataset from colorectal cancer. Extensive experiments show that the performance of our proposed method is superior to that of previous state-of-the-art ones and the interpretability of our proposed method is demonstrated by Grad-CAM.

The state of the art for artificial intelligence in lung digital pathology

Lung diseases carry a significant burden of morbidity and mortality worldwide. The advent of digital pathology (DP) and an increase in computational power have led to the development of artificial intelligence (AI)-based tools that can assist pathologists and pulmonologists in improving clinical workflow and patient management. While previous works have explored the advances in computational approaches for breast, prostate, and head and neck cancers, there has been a growing interest in applying these technologies to lung diseases as well. The application of AI tools on radiology images for better characterization of indeterminate lung nodules, fibrotic lung disease, and lung cancer risk stratification has been well documented. In this article, we discuss methodologies used to build AI tools in lung DP, describing the various hand-crafted and deep learning-based unsupervised feature approaches. Next, we review AI tools across a wide spectrum of lung diseases including cancer, tuberculosis, idiopathic pulmonary fibrosis, and COVID-19. We discuss the utility of novel imaging biomarkers for different types of clinical problems including quantification of biomarkers like PD-L1, lung disease diagnosis, risk stratification, and prediction of response to treatments such as immune checkpoint inhibitors. We also look briefly at some emerging applications of AI tools in lung DP such as multimodal data analysis, 3D pathology, and transplant rejection. Lastly, we discuss the future of DP-based AI tools, describing the challenges with regulatory approval, developing reimbursement models, planning clinical deployment, and addressing AI biases. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Prognostic impact of an integrative analysis of [18F]FDG PET parameters and infiltrating immune cell scores in lung adenocarcinoma

Background

High levels of ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) tumor uptake are associated with worse prognosis in patients with non-small cell lung cancer (NSCLC). Meanwhile, high levels of immune cell infiltration in primary tumor have been linked to better prognosis in NSCLC. We conducted this study for precisely stratified prognosis of the lung adenocarcinoma patients using the integration of ¹⁸F-FDG positron emission tomography (PET) parameters and infiltrating immune cell scores as assessed by a genomic analysis.

Results

Using an RNA sequencing dataset, the patients were divided into three subtype groups. Additionally, 24 different immune cell scores and cytolytic scores (CYT) were obtained. In ¹⁸F-FDG PET scans, PET parameters of the primary tumors were obtained. An ANOVA test, a Chi-square test and a correlation analysis were also conducted. A Kaplan–Meier survival analysis with the log-rank test and multivariable Cox regression test was performed to evaluate prognostic values of the parameters. The terminal respiratory unit (TRU) group demonstrated lower ¹⁸F-FDG PET parameters, more females, and lower stages than the other groups. Meanwhile, the proximal inflammatory (PI) group showed a significantly higher CYT score compared to the other groups (P = .001). Also, CYT showed a positive correlation with tumor-to-liver maximum standardized uptake value ratio (TLR) in the PI group (P = .027). A high TLR (P = .01) score of ¹⁸F-FDG PET parameters and a high T follicular helper cell (TFH) score (P = .005) of immune cell scores were associated with prognosis with opposite tendencies. Furthermore, TLR and TFH were predictive of overall survival even after adjusting for clinicopathologic features and others (P = .024 and .047).

Conclusions

A high TLR score was found to be associated with worse prognosis, while high CD8 T cell and TFH scores predicted better prognosis in lung adenocarcinoma. Furthermore, TLR and TFH can be used to predict prognosis independently in patients with lung adenocarcinoma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13550-022-00908-9.

Spatial interplay patterns of cancer nuclei and tumor-infiltrating lymphocytes (TILs) predict clinical benefit for immune checkpoint inhibitors

Immune checkpoint inhibitors (ICIs) show prominent clinical activity across multiple advanced tumors. However, less than half of patients respond even after molecule-based selection. Thus, improved biomarkers are required. In this study, we use an image analysis to capture morphologic attributes relating to the spatial interaction and architecture of tumor cells and tumor-infiltrating lymphocytes (TILs) from digitized H&E images. We evaluate the association of image features with progression-free (PFS) and overall survival in non-small cell lung cancer (NSCLC) (N = 187) and gynecological cancer (N = 39) patients treated with ICIs. We demonstrated that the classifier trained with NSCLC alone was associated with PFS in independent NSCLC cohorts and also in gynecological cancer. The classifier was also associated with clinical outcome independent of clinical factors. Moreover, the classifier was associated with PFS even with low PD-L1 expression. These findings suggest that image analysis can be used to predict clinical end points in patients receiving ICI.

Towards a national strategy for digital pathology in Switzerland

Precision medicine is entering a new era of digital diagnostics; the availability of integrated digital pathology (DP) and structured clinical datasets has the potential to become a key catalyst for biomedical research, education and business development. In Europe, national programs for sharing of this data will be crucial for the development, testing, and validation of machine learning-enabled tools supporting clinical decision-making. Here, the Swiss Digital Pathology Consortium (SDiPath) discusses the creation of a Swiss Digital Pathology Infrastructure (SDPI), which aims to develop a unified national DP network bringing together the Swiss Personalized Health Network (SPHN) with Swiss university hospitals and subsequent inclusion of cantonal and private institutions. This effort builds on existing developments for the national implementation of structured pathology reporting. Opening this national infrastructure and data to international researchers in a sequential rollout phase can enable the large-scale integration of health data and pooling of resources for research purposes and clinical trials. Therefore, the concept of a SDPI directly synergizes with the priorities of the European Commission communication on the digital transformation of healthcare on an international level, and with the aims of the Swiss State Secretariat for Economic Affairs (SECO) for advancing research and innovation in the digitalization domain. SDPI directly addresses the needs of existing national and international research programs in neoplastic and non-neoplastic diseases by providing unprecedented access to well-curated clinicopathological datasets for the development and implementation of novel integrative methods for analysis of clinical outcomes and treatment response. In conclusion, a SDPI would facilitate and strengthen inter-institutional collaboration in technology, clinical development, business and research at a national and international scale, promoting improved patient care via precision medicine.

Multi-Scale Spatial Analysis of the Tumor Microenvironment Reveals Features of Cabozantinib and Nivolumab Efficacy in Hepatocellular Carcinoma

Background

Concomitant inhibition of vascular endothelial growth factor (VEGF) and programmed cell death protein 1 (PD-1) or its ligand PD-L1 is a standard of care for patients with advanced hepatocellular carcinoma (HCC), but only a minority of patients respond, and responses are usually transient. Understanding the effects of therapies on the tumor microenvironment (TME) can provide insights into mechanisms of therapeutic resistance.

Methods

14 patients with HCC were treated with the combination of cabozantinib and nivolumab through the Johns Hopkins Sidney Kimmel Comprehensive Cancer Center. Among them, 12 patients (5 responders + 7 non-responders) underwent successful margin negative resection and are subjects to tissue microarray (TMA) construction containing 37 representative tumor region cores. Using the TMAs, we performed imaging mass cytometry (IMC) with a panel of 27-cell lineage and functional markers. All multiplexed images were then segmented to generate a single-cell dataset that enables (1) tumor-immune compartment analysis and (2) cell community analysis based on graph-embedding methodology. Results from these hierarchies are merged into response-associated biological process patterns.

Results

Image processing on 37 multiplexed-images discriminated 59,453 cells and was then clustered into 17 cell types. Compartment analysis showed that at immune-tumor boundaries from NR, PD-L1 level on tumor cells is significantly higher than remote regions; however, Granzyme B expression shows the opposite pattern. We also identify that the close proximity of CD8+ T cells to arginase 1hi (Arg1hi) macrophages, rather than CD4+ T cells, is a salient feature of the TME in non-responders. Furthermore, cell community analysis extracted 8 types of cell-cell interaction networks termed cellular communities (CCs). We observed that in non-responders, macrophage-enriched CC (MCC) and lymphocyte-enriched CC (LCC) strongly communicate with tumor CC, whereas in responders, such communications were undermined by the engagement between MCC and LCC.

Conclusion

These results demonstrate the feasibility of a novel application of multiplexed image analysis that is broadly applicable to quantitative analysis of pathology specimens in immuno-oncology and provides further evidence that CD163-Arg1hi macrophages may be a therapeutic target in HCC. The results also provide critical information for the development of mechanistic quantitative systems pharmacology models aimed at predicting outcomes of clinical trials.

Plasma-Based Measurements of Tumor Heterogeneity Correlate with Clinical Outcomes in Metastatic Colorectal Cancer

Sequencing circulating tumor DNA (ctDNA) from liquid biopsies may better assess tumor heterogeneity than limited sampling of tumor tissue. Here, we explore ctDNA-based heterogeneity and its correlation with treatment outcome in STEAM, which assessed efficacy and safety of concurrent and sequential FOLFOXIRI-bevacizumab (BEV) vs. FOLFOX-BEV for first-line treatment of metastatic colorectal cancer. We sequenced 146 pre-induction and 89 post-induction patient plasmas with a 198-kilobase capture-based assay, and applied Mutant-Allele Tumor Heterogeneity (MATH), a traditionally tissue-based calculation of allele frequency distribution, on somatic mutations detected in plasma. Higher levels of MATH, particularly in the post-induction sample, were associated with shorter progression-free survival (PFS). Patients with high MATH vs. low MATH in post-induction plasma had shorter PFS (7.2 vs. 11.7 months; hazard ratio, 3.23; 95% confidence interval, 1.85−5.63; log-rank p < 0.0001). These results suggest ctDNA-based tumor heterogeneity may have potential prognostic value in metastatic cancers.

Prognostic Impact of EGFR Amplification and Visceral Pleural Invasion in Early Stage Pulmonary Squamous Cell Carcinomas Patients after Surgical Resection of Primary Tumor

Over the last few decades, an increasing amount of information has been accumulated on biomarkers in non-small cell lung cancer (NSCLC). Despite these advances, most biomarkers have been identified in the adenocarcinoma histological subtype (AC). However, the application of molecular-targeted therapies in the prognosis and treatment of SCC in the clinical setting is very limited, becoming one of the main focus areas in research. Here, we prospectively analyzed the frequency of numerical/structural abnormalities of chromosomes 5, 7, 8, 9, 13 and 22 with FISH in 48 pulmonary SCC patients. From a total of 12 probes, only abnormalities of the 7p12 and 22q12 chromosomal regions were identified as unique genetic variables associated with the prognosis of the disease. The study for these two chromosomal regions was extended to 108 patients with SCC. Overall, chromosome losses were observed more frequently than chromosome gains, i.e., 61% versus 19% of all the chromosome abnormalities detected. The highest levels of genetic amplification were detected for the 5p15.2, 7p12, 8q24 and 22q11 chromosome bands, of which several genes are potentially involved in the pathogenesis of SCC, among others, include the EGFR gene at chromosome 7p12. Patients who displayed EGFR amplification (n = 13; 12%) were mostly older than 65 years (p = 0.07) and exclusively patients in early T-primary tumor stage (pT1−pT2; p = 0.03) with a significantly shortened overall survival (OS) (p ≤ 0.001). Regarding prognosis, the clinical, biological, and histopathologic characteristics of the disease that displayed a significant adverse influence on OS in the univariate analysis included patients older than 65 years (p = 0.02), the presence of lymph node involvement (p = 0.005), metastasis (p = 0.01) and, visceral pleural invasion (VPI) at diagnosis (p = 0.04). EGFR amplification also conferred an adverse impact on patient OS in the whole series (p = 0.02) and especially in patients in early stages (pT1−pT2; p = 0.01). A multivariate analysis of the prognostic factors for OS showed that the most informative combination of independent variables to predict an adverse outcome was the presence of VPI and/or EGFR amplification (p < 0.001). Based on these two variables, a scoring system was built to stratify patients into low- (no adverse features: score 0; n = 69), intermediate- (one adverse feature: score 1; n = 29) and high-risk (two adverse features: score 2; n = 5) groups, with significantly different (p = 0.001) OS rates at 50 months, which were as following: 32%, 28% and 0%, respectively. In the present study, we show that the presence of a high level of 7p12 (EGFR) amplification, exclusively detected in early stage SCC (pT1−pT2), is an independent adverse prognostic factor for OS. The identification of the EGFR gene copy number using FISH techniques may provide a more accurate diagnosis of high-risk populations after the complete resection of the primary tumor. When combined with VPI, three groups of pulmonary SCC were clearly identified that show the extent of the disease. This is of such importance that further prospective studies are necessary in larger series of SCC patients to be classified at the time of diagnosis. This could be achieved with the combined assessment of 7p12 amplification and VPI in primary tumor samples.

A new magnetic resonance imaging tumour response grading scheme for locally advanced rectal cancer

BACKGROUND

The potential of using magnetic resonance image tumour-regression grading (MRI-TRG) system to predict pathological TRG is debatable for locally advanced rectal cancer treated by neoadjuvant radiochemotherapy.

METHODS

Referring to the American Joint Committee on Cancer/College of American Pathologists (AJCC/CAP) TRG classification scheme, a new four-category MRI-TRG system based on the volumetric analysis of the residual tumour and radiochemotherapy induced anorectal fibrosis was established. The agreement between them was evaluated by Kendall's tau-b test, while Kaplan-Meier analysis was used to calculate survival outcomes.

RESULTS

In total, 1033 patients were included. Good agreement between MRI-TRG and AJCC/CAP TRG classifications was observed (k = 0.671). Particularly, as compared with other pairs, MRI-TRG 0 displayed the highest sensitivity [90.1% (95% CI: 84.3-93.9)] and specificity [92.8% (95% CI: 90.4-94.7)] in identifying AJCC/CAP TRG 0 category patients. Except for the survival ratios that were comparable between the MRI-TRG 0 and MRI-TRG 1 categories, any two of the four categories had distinguished 3-year prognosis (all P < 0.05). Cox regression analysis further proved that the MRI-TRG system was an independent prognostic factor (all P < 0.05).

CONCLUSION

The new MRI-TRG system might be a surrogate for AJCC/CAP TRG classification scheme. Importantly, the system is a reliable and non-invasive way to identify patients with complete pathological responses.

Integrated Analysis of MATH-Based Subtypes Reveals a Novel Screening Strategy for Early-Stage Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is a frequently diagnosed cancer type, and many patients have already reached an advanced stage when diagnosed. Thus, it is crucial to develop a novel and efficient approach to diagnose and classify lung adenocarcinoma at an early stage. In our study, we combined in silico analysis and machine learning to develop a new five-gene–based diagnosis strategy, which was further verified in independent cohorts and in vitro experiments. Considering the heterogeneity in cancer, we used the MATH (mutant-allele tumor heterogeneity) algorithm to divide patients with early-stage LUAD into two groups (C1 and C2). Specifically, patients in C2 had lower intratumor heterogeneity and higher abundance of immune cells (including B cell, CD4 T cell, CD8 T cell, macrophage, dendritic cell, and neutrophil). In addition, patients in C2 had a higher likelihood of immunotherapy response and overall survival advantage than patients in C1. Combined drug sensitivity analysis (CTRP/PRISM/CMap/GDSC) revealed that BI-2536 might serve as a new therapeutic compound for patients in C1. In order to realize the application value of our study, we constructed the classifier (to classify early-stage LUAD patients into C1 or C2 groups) with multiple machine learning and bioinformatic analyses. The 21-gene–based classification model showed high accuracy and strong generalization ability, and it was verified in four independent validation cohorts. In summary, our research provided a new strategy for clinicians to make a quick preliminary assisting diagnosis of early-stage LUAD and make patient classification at the intratumor heterogeneity level. All data, codes, and study processes have been deposited to Github and are available online.

Computer extracted features of nuclear morphology in hematoxylin and eosin images distinguish Stage II and IV colon tumors

We assessed the utility of quantitative features of colon cancer nuclei, extracted from digitized hematoxylin and eosin-stained whole slide images (WSIs), to distinguish between Stage II from Stage IV colon cancers. Our discovery cohort comprised 100 Stage II and Stage IV colon cancer cases sourced from the University Hospitals Cleveland Medical Center (UHCMC). We performed initial (independent) model validation on 51 (143) Stage II and 79 (54) Stage IV colon cancer cases from UHCMC (The Cancer Genome Atlas's Colon Adenocarcinoma, TCGA-COAD, cohort). Our approach comprised the following steps, (1) a fully convolutional deep neural network with VGG-18 architecture was trained to locate cancer on WSIs, (2) another deep-learning model based on Mask-RCNN with Resnet-50 architecture was used to segment all nuclei from within the identified cancer region, (3) a total of 26,641 quantitative morphometric features pertaining to nuclear shape, size, and texture were extracted from within and outside tumor nuclei, (4) a random forest classifier was trained to distinguish between Stage II and Stage IV colon cancers using the 5 most discriminatory features selected by the Wilcoxon rank-sum test. Our trained classifier using these top 5 features yielded an AUC of 0.81 and 0.78, respectively, on the held-out cases in UHCMC and TCGA validation sets. For 197 TCGA-COAD cases, the Cox-proportional hazards model yielded a hazard ratio of 2.20 (95% CI: 1.24-3.88) with a concordance index of 0.71 using only top-five features for risk stratification of overall survival. The Kaplan-Meier estimate also showed statistically significant separation between the low-risk and high-risk patients with a log-rank p-value of 0.0097. Finally, unsupervised clustering of the top-five features revealed that Stage IV colon cancers with peritoneal spread were morphologically more similar to Stage II colon cancers with no long-term metastases than Stage IV colon cancers with hematogenous spread. This article is protected by copyright. All rights reserved.

Non-invasive scoring of cellular atypia in keratinocyte cancers in 3D LC-OCT images using Deep Learning

Diagnosis based on histopathology for skin cancer detection is today's gold standard and relies on the presence or absence of biomarkers and cellular atypia. However it suffers drawbacks: it requires a strong expertise and is time-consuming. Moreover the notion of atypia or dysplasia of the visible cells used for diagnosis is very subjective, with poor inter-rater agreement reported in the literature. Lastly, histology requires a biopsy which is an invasive procedure and only captures a small sample of the lesion, which is insufficient in the context of large fields of cancerization. Here we demonstrate that the notion of cellular atypia can be objectively defined and quantified with a non-invasive in-vivo approach in three dimensions (3D). A Deep Learning (DL) algorithm is trained to segment keratinocyte (KC) nuclei from Line-field Confocal Optical Coherence Tomography (LC-OCT) 3D images. Based on these segmentations, a series of quantitative, reproducible and biologically relevant metrics is derived to describe KC nuclei individually. We show that, using those metrics, simple and more complex definitions of atypia can be derived to discriminate between healthy and pathological skins, achieving Area Under the ROC Curve (AUC) scores superior than 0.965, largely outperforming medical experts on the same task with an AUC of 0.766. All together, our approach and findings open the door to a precise quantitative monitoring of skin lesions and treatments, offering a promising non-invasive tool for clinical studies to demonstrate the effects of a treatment and for clinicians to assess the severity of a lesion and follow the evolution of pre-cancerous lesions over time.

A Convolutional Neural Network-Based Intelligent Medical System with Sensors for Assistive Diagnosis and Decision-Making in Non-Small Cell Lung Cancer

In many regions of the world, early diagnosis of non-small cell lung cancer (NSCLC) is a major challenge due to the large population and lack of medical resources, which is difficult toeffectively address via limited physician manpower alone. Therefore, we developed a convolutional neural network (CNN)-based assisted diagnosis and decision-making intelligent medical system with sensors. This system analyzes NSCLC patients' medical records using sensors to assist staging a diagnosis and provides recommended treatment plans to physicians. To address the problem of unbalanced case samples across pathological stages, we used transfer learning and dynamic sampling techniques to reconstruct and iteratively train the model to improve the accuracy of the prediction system. In this paper, all data for training and testing the system were obtained from the medical records of 2,789,675 patients with NSCLC, which were recorded in three hospitals in China over a five-year period. When the number of case samples reached 8000, the system achieved an accuracy rate of 0.84, which is already close to that of the doctors (accuracy: 0.86). The experimental results proved that the system can quickly and accurately analyze patient data and provide decision information support for physicians.

Integrating pathomics with radiomics and genomics for cancer prognosis: A brief review

In the last decade, the focus of computational pathology research community has shifted from replicating the pathological examination for diagnosis done by pathologists to unlocking and discovering "sub-visual" prognostic image cues from the histopathological image. While we are getting more knowledge and experience in digital pathology, the emerging goal is to integrate other-omics or modalities that will contribute for building a better prognostic assay. In this paper, we provide a brief review of representative works that focus on integrating pathomics with radiomics and genomics for cancer prognosis. It includes: correlation of pathomics and genomics; fusion of pathomics and genomics; fusion of pathomics and radiomics. We also present challenges, potential opportunities, and avenues for future work.

Heterochronous Metastases of Lung Adenocarcinoma to Pancreas and Liver: A Case Report from Pathological Perspectives

Immunohistochemistry (IHC) is a vital tool to distinguish tumor metastases from primary lesions in addition to morphologic analysis. In this study, a 64-year-old female with a past surgical history of lung adenocarcinoma 11 years ago was presented with recurrence of liver nodular lesions after multiple surgical procedures, including the Whipple procedure for pancreatic head adenocarcinoma and cytoreductive surgery for liver metastasis. Liver biopsy and review of the previous specimens, based on IHC analyses, suggested heterochronous metastases of lung adenocarcinoma to the digestive systems in a long-time span, instead of primary pancreatic adenocarcinoma. This case demonstrates the potential for misdiagnoses from morphologic analysis alone and suggests the necessity of IHC analyses to avoid misjudgment on tumor phenotypes, when a previous oncologic history is presented.

22-O-(N-Boc-L-glycine) ester of renieramycin M inhibits migratory activity and suppresses epithelial-mesenchymal transition in human lung cancer cells

The incidence of metastasis stage crucially contributes to high recurrence and mortality rate in lung cancer patients. Unfortunately, no available treatment inhibits migration, a key metastasis process in lung cancer. In this study, the effect of 22-O-(N-Boc-L-glycine) ester of renieramycin M (22-Boc-Gly-RM), a semi-synthetic amino ester derivative of bistetrahydroisoquinolinequinone alkaloid isolated from Xestospongia sp., on migratory behavior of human lung cancer cells was investigated. Following 24 h of treatment, 22-Boc-Gly-RM at non-toxic concentrations (0.5-1 μM) effectively restrained motility of human lung cancer H460 cells assessed through wound healing, transwell migration, and multicellular spheroid models. The capability to invade through matrix component was also repressed in H460 cells cultured with 0.1-1 µM 22-Boc-Gly-RM. The dose-dependent reduction of phalloidin-stained actin stress fibers corresponded with the downregulated Rac1-GTP level presented via western blot analysis in 22-Boc-Gly-RM-treated cells. Treatment with 0.1-1 μM of 22-Boc-Gly-RM obviously caused suppression of p-FAK/p-Akt signal and consequent inhibition of epithelial-to-mesenchymal transition (EMT), which was evidenced with augmented level of E-cadherin and reduction of N-cadherin expression. The alteration of invasion-related proteins in 22-Boc-Gly-RM-treated H460 cells was indicated by the diminution of matrix metalloproteinases (MT1-MMP, MMP-2, MMP-7, and MMP-9), as well as the upregulation of tissue inhibitors of metalloproteinases (TIMP), TIMP2, and TIMP3. Thus, 22-Boc-Gly-RM is a promising candidate for anti-metastasis treatment in lung cancer through inhibition of migratory features associated with suppression on EMT.

Perspectives in pathomics in head and neck cancer

PURPOSE OF REVIEW

Pathology is the cornerstone of cancer care. Pathomics, which represents the use of artificial intelligence in digital pathology, is an emerging and promising field that will revolutionize medical and surgical pathology in the coming years. This review provides an overview of pathomics, its current and future applications and its most relevant applications in Head and Neck cancer care.

RECENT FINDINGS

The number of studies investigating the use of artificial intelligence in pathology is rapidly growing, especially as the utilization of deep learning has shown great potential with Whole Slide Images. Even though numerous steps still remain before its clinical use, Pathomics has been used for varied applications comprising of computer-assisted diagnosis, molecular anomalies prediction, tumor microenvironment and biomarker identification as well as prognosis evaluation. The majority of studies were performed on the most frequent cancers, notably breast, prostate, and lung. Interesting results were also found in Head and Neck cancers.

SUMMARY

Even if its use in Head and Neck cancer care is still low, Pathomics is a powerful tool to improve diagnosis, identify prognostic factors and new biomarkers. Important challenges lie ahead before its use in a clinical practice, notably the lack of information on how AI makes its decisions, the slow deployment of digital pathology, and the need for extensively validated data in order to obtain authorities approval. Regardless, pathomics will most likely improve pathology in general, including Head and Neck cancer care in the coming years.

Reimagining T Staging Through Artificial Intelligence and Machine Learning Image Processing Approaches in Digital Pathology

Tumor stage and grade, visually assessed by pathologists from evaluation of pathology images in conjunction with radiographic imaging techniques, have been linked to outcome, progression, and survival for a number of cancers. The gold standard of staging in oncology has been the TNM (tumor-node-metastasis) staging system. Though histopathological grading has shown prognostic significance, it is subjective and limited by interobserver variability even among experienced surgical pathologists. Recently, artificial intelligence (AI) approaches have been applied to pathology images toward diagnostic-, prognostic-, and treatment prediction-related tasks in cancer. AI approaches have the potential to overcome the limitations of conventional TNM staging and tumor grading approaches, providing a direct prognostic prediction of disease outcome independent of tumor stage and grade. Broadly speaking, these AI approaches involve extracting patterns from images that are then compared against previously defined disease signatures. These patterns are typically categorized as either (1) handcrafted, which involve domain-inspired attributes, such as nuclear shape, or (2) deep learning (DL)-based representations, which tend to be more abstract. DL approaches have particularly gained considerable popularity because of the minimal domain knowledge needed for training, mostly only requiring annotated examples corresponding to the categories of interest. In this article, we discuss AI approaches for digital pathology, especially as they relate to disease prognosis, prediction of genomic and molecular alterations in the tumor, and prediction of treatment response in oncology. We also discuss some of the potential challenges with validation, interpretability, and reimbursement that must be addressed before widespread clinical deployment. The article concludes with a brief discussion of potential future opportunities in the field of AI for digital pathology and oncology.

Smart wearable devices as a psychological intervention for healthy lifestyle and quality of life: a randomized controlled trial

PURPOSE

Creating a healthy lifestyle is important across different life stages. Commercial smart wearable devices are an innovative and interesting approach as an early psychological intervention for modifying health-related behaviors. Therefore, the purpose of this study was to explore the effects of smart wearable devices on health-promoting lifestyles and quality of life.

METHODS

The study design was a three-parallel randomized controlled trial with a 3-month intervention. Two commercial smart wearable devices (smartwatches and smart bracelets) with different levels of complicated functions were applied as a psychological intervention in comparison with a smartphone app as the control group. Participants were healthy young adults with a median age of 26 years. Outcome measurements were conducted by self-administered questionnaires. Chi-square tests and ANOVA were performed for testing the difference of participants at baseline, and generalized estimating equations were performed for testing the effect of the intervention.

RESULTS

At the beginning, 81 participants were recruited and 73 participants completed the study. Results of a healthy lifestyle demonstrated significant group effects of exercise and a significant effect of the interaction for self-actualization and stress management in the experimental group with a smartwatch (Self-actualization: MD = 0.35[- 0.10,0.80]; Exercise: MD = 0.21[- 0.33 0.75]; Stress management: MD = 0.36[- 0.04,0.76]) by comparing with only using mobile app (Self-actualization: MD =  - 0.03[- 0.25,0.18]; Exercise: MD =  - 0.12[- 0.38,0.14]; Stress management, MD =  - 0.28[- 0.55,0.00]). The significant effect of group-by-time interaction for self-actualization was found in the experimental group with a smart bracelet (MD = 0.05[- 0.30,0.20]) by comparing with the control group. The GEE-adjusted model indicated significant effects of the interaction on the comprehensive, physical, and mental quality of life in the experimental group with the smartwatch (Comprehensive: MD = 0.24[- 0.04,0.52]; Physical: MD = 0.67[0.26,1.09]; Mental: MD = 0.72[0.29,1.16]) by comparing with the control group (Comprehensive: MD =  - 1.57[- 2.55, - 0.59]; Physical: MD = 0.25[0.00,0.50]; Mental: MD = 0.08[- 0.11,0.27]).

CONCLUSION

From a psychological perspective, smart wearable devices have potential benefits of shaping a healthy lifestyle and improving the quality of life. Enhancing the utility of commercial well-designed smart wearable devices is an innovative and effective strategy for promoting public health.