A digital score of tumour-associated stroma infiltrating lymphocytes predicts survival in head and neck squamous cell carcinoma

The prognostic value of tumor-infiltrating lymphocytes in head and neck squamous cell carcinoma: A systematic review and meta-analysis

Head and neck squamous cell carcinoma (HNSCC) is experiencing a rising incidence and mortality worldwide, emphasizing the need for reliable prognostic markers. Tumor-infiltrating lymphocytes (TILs) have emerged as a promising biomarker for predicting HNSCC prognosis, yet no systematic reviews have exclusively focused on hematoxylin and eosin (H&E)-stained formalin-fixed paraffin-embedded (FFPE) samples, which are routinely used in clinical practice. This systematic review and meta-analysis followed the PRISMA guidelines to examine the prognostic value of TILs in HNSCC using H&E-stained FFPE samples. Data were pooled from 43 studies, including 26 studies in a meta-analysis, analyzing 5037 HNSCC samples. We found that a high TIL count associated with a significantly improved overall survival (OS) (HR 0.47, 95 % CI 0.41-0.55, p < 0.0001), disease-free survival (DFS) (HR 0.55, 95 % CI 0.41-0.55, p < 0.0001), and disease-specific survival (DSS) (HR 0.58, 95 % CI 0.46-0.73, p < 0.0001). The heterogeneity was moderate for the pooled analysis (OS: I² = 40 %; DFS: I² = 39 %; DSS: I² = 51 %), but low for the subgroup analysis based on tumor site in oral, oropharyngeal, laryngeal, and nasopharyngeal cancer (OS and DFS: I² = 0-14 %). This review is the first to systematically evaluate TILs in HNSCC using H&E-stained samples, confirming their prognostic value. A high TIL count is associated with improved survival outcomes, suggesting their potential as prognostic biomarkers in clinical settings.

Immune-scoring in head and neck squamous cell carcinoma: a scoping review

INTRODUCTION

Head and neck squamous cell carcinomas (HNSCCs) have an increasing incidence, high recurrence, and an overall unfavorable prognosis despite numerous treatment options. The distinct immune landscape of HNSCC suggests a potential for immune-related biomarkers to aid classification and treatment planning.

AREAS COVERED

Immunoscore, a multiplex measure of tumor-infiltrating immune cells, is currently approved in colorectal carcinoma and is under investigation in various other cancer types. Recent studies have tried to implement the immunoscore and other novel immune cell-based scoring systems in HNSCC as predictors of survival. This study provides an overview of tumor-infiltrating immune cells and their prognostic significance, as well as a comparative summary of studies introducing an immunoscore in HNSCC.

EXPERT OPINION

With sufficient insight of the current literature, future studies could lead to the definition and validation of a new immune-based classification system for HNSCC. Such a classification strategy could be the basis for patient selection and, thus, optimize treatment outcomes and reduce unwanted complications. The heterogeneity of HNSCC subtypes, as well as the intratumoral variability of immune infiltrates, should be accounted for in the immunoscore.

An algorithm for standardization of tumor Infiltrating lymphocyte evaluation in head and neck cancers

PURPOSE

The prognostic and predictive significance of pathologist-read tumor infiltrating lymphocytes (TILs) in head and neck cancers have been demonstrated through multiple studies over the years. TILs have not been broadly adopted clinically, perhaps due to substantial inter-observer variability. In this study, we developed a machine-based algorithm for TIL evaluation in head and neck cancers and validated its prognostic value in independent cohorts.

EXPERIMENTAL DESIGN

A network classifier called NN3-17 was trained to identify and calculate tumor cells, lymphocytes, fibroblasts and "other" cells on hematoxylin-eosin stained sections using the QuPath software. These measurements were used to construct three predefined TIL variables. A retrospective collection of 154 head and neck squamous cell cancer cases was used as the discovery set to identify optimal association of TIL variables and survival. Two independent cohorts of 234 cases were used for validation.

RESULTS

We found that electronic TIL variables were associated with favorable prognosis in both the HPV-positive and -negative cases. After adjusting for clinicopathologic factors, Cox regression analysis demonstrated that electronic total TILs% (p = 0.025) in the HPV-positive and electronic stromal TILs% (p < 0.001) in the HPV-negative population were independent markers of disease specific outcomes (disease free survival).

CONCLUSIONS

Neural network TIL variables demonstrated independent prognostic value in validation cohorts of HPV-positive and HPV-negative head and neck cancers. These objective variables can be calculated by an open-source software and could be considered for testing in a prospective setting to assess potential clinical implications.

Attention De-sparsification Matters: Inducing diversity in digital pathology representation learning

We propose DiRL, a Diversity-inducing Representation Learning technique for histopathology imaging. Self-supervised learning (SSL) techniques, such as contrastive and non-contrastive approaches, have been shown to learn rich and effective representations of digitized tissue samples with limited pathologist supervision. Our analysis of vanilla SSL-pretrained models' attention distribution reveals an insightful observation: sparsity in attention, i.e, models tends to localize most of their attention to some prominent patterns in the image. Although attention sparsity can be beneficial in natural images due to these prominent patterns being the object of interest itself, this can be sub-optimal in digital pathology; this is because, unlike natural images, digital pathology scans are not object-centric, but rather a complex phenotype of various spatially intermixed biological components. Inadequate diversification of attention in these complex images could result in crucial information loss. To address this, we leverage cell segmentation to densely extract multiple histopathology-specific representations, and then propose a prior-guided dense pretext task, designed to match the multiple corresponding representations between the views. Through this, the model learns to attend to various components more closely and evenly, thus inducing adequate diversification in attention for capturing context-rich representations. Through quantitative and qualitative analysis on multiple tasks across cancer types, we demonstrate the efficacy of our method and observe that the attention is more globally distributed.

Immune Landscape of Epstein-Barr Virus-Associated Gastric Cancer: Analysis From a Western Academic Institution

INTRODUCTION

Epstein-Barr virus-associated gastric cancer (EBVaGC) may be a meaningful biomarker for potential benefit from immunotherapy. Further investigation is needed to characterize the immune landscape of EBVaGC. We assessed our institutional frequency of surgically treated EBVaGC and analyzed the immunologic biomarker profile and tumor-infiltrating lymphocyte (TIL) phenotypes of a series of EBVaGC compared to non-EBVaGC cases.

METHODS

Available tissue samples from all patients with biopsy-confirmed gastric adenocarcinoma who underwent resection with curative intent from 2012 to 2020 at our institution were collected. In situ hybridization was used to assess EBV status; multiplex immunohistochemistry was performed to assess mismatch repair status, Programmed Death-Ligand 1 (PD-L1) expression, and phenotypic characterization of TILs.

RESULTS

Sixty-eight samples were included in this study. EBVaGC was present in 3/68 (4%) patients. Among all patients, 27/68 (40%) had positive PD-L1 expression; two of three (67%) EBVaGC patients exhibited positive PD-L1 expression. Compared to non-EBVaGC, EBV-positive tumors showed 5-fold to 10-fold higher density of TILs in both tumor and stroma and substantially elevated CD8+ T cell to Tregulatory cell ratio. The memory subtypes of CD8+ and CD4+ T cells were upregulated in EBVaGC tumors and stromal tissue compared to non-EBVaGC.

CONCLUSIONS

The incidence of surgically resected EBVaGC at our center was 4%. EBVaGC tumors harbor elevated levels of TILs, including memory subtypes, within both tumor and tumor-related stroma. Robust TIL presence and upregulated PD-L1 positivity in EBVaGC may portend promising responses to immunotherapy agents. Further investigation into routine EBV testing and TIL phenotype of patients with gastric cancer to predict response to immunotherapy may be warranted.

Exploring the frontiers: tumor immune microenvironment and immunotherapy in head and neck squamous cell carcinoma

The global prevalence of head and neck malignancies positions them as the sixth most common form of cancer, with the head and neck squamous cell carcinoma (HNSCC) representing the predominant histological subtype. Despite advancements in multidisciplinary approaches and molecular targeted therapies, the therapeutic outcomes for HNSCC have only marginally improved, particularly in cases of recurrent or metastatic HNSCC (R/MHNSCC). This situation underscores the critical necessity for the development of innovative therapeutic strategies. Such strategies are essential not only to enhance the efficacy of HNSCC treatment but also to minimize the incidence of associated complications, thus improving overall patient prognosis. Cancer immunotherapy represents a cutting-edge cancer treatment that leverages the immune system for targeting and destroying cancer cells. It's applied to multiple cancers, including melanoma and lung cancer, offering precision, adaptability, and the potential for long-lasting remission through immune memory. It is observed that while HNSCC patients responsive to immunotherapy often experience prolonged therapeutic benefits, only a limited subset demonstrates such responsiveness. Additionally, significant clinical challenges remain, including the development of resistance to immunotherapy. The biological characteristics, dynamic inhibitory changes, and heterogeneity of the tumor microenvironment (TME) in HNSCC play critical roles in its pathogenesis, immune evasion, and therapeutic resistance. This review aims to elucidate the functions and mechanisms of anti-tumor immune cells and extracellular components within the HNSCC TME. It also introduces several immunosuppressive agents commonly utilized in HNSCC immunotherapy, examines factors influencing the effectiveness of these treatments, and provides a comprehensive summary of immunotherapeutic strategies relevant to HNSCC.

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

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

A Novel Deep Learning Algorithm for Human Papillomavirus Infection Prediction in Head and Neck Cancers Using Routine Histology Images

The etiology of head and neck squamous cell carcinoma (HNSCC) involves multiple carcinogens, such as alcohol, tobacco, and infection with human papillomavirus (HPV). Because HPV infection influences the prognosis, treatment, and survival of patients with HNSCC, it is important to determine the HPV status of these tumors. In this article, we propose a novel deep learning pipeline for HPV infection status prediction with state-of-the-art performance in HPV detection using only whole-slide images of routine hematoxylin and eosin-stained HNSCC sections. We show that our Digital-HPV score generated from hematoxylin and eosin slides produces statistically significant patient stratifications in terms of overall and disease-specific survival. In addition, quantitative profiling of the spatial tumor microenvironment and analysis of the immune profiles show relatively high levels of lymphocytic infiltration in tumor and tumor-associated stroma. High levels of B cells and T cells and low macrophage levels were also identified in HPV-positive patients compared to HPV-negative patients, confirming different immune response patterns elicited by HPV infection in patients with HNSCC.

Lymphocyte detection for cancer analysis using a novel fusion block based channel boosted CNN

Tumor-infiltrating lymphocytes, specialized immune cells, are considered an important biomarker in cancer analysis. Automated lymphocyte detection is challenging due to its heterogeneous morphology, variable distribution, and presence of artifacts. In this work, we propose a novel Boosted Channels Fusion-based CNN "BCF-Lym-Detector" for lymphocyte detection in multiple cancer histology images. The proposed network initially selects candidate lymphocytic regions at the tissue level and then detects lymphocytes at the cellular level. The proposed "BCF-Lym-Detector" generates diverse boosted channels by utilizing the feature learning capability of different CNN architectures. In this connection, a new adaptive fusion block is developed to combine and select the most relevant lymphocyte-specific features from the generated enriched feature space. Multi-level feature learning is used to retain lymphocytic spatial information and detect lymphocytes with variable appearances. The assessment of the proposed "BCF-Lym-Detector" show substantial improvement in terms of F-score (0.93 and 0.84 on LYSTO and NuClick, respectively), which suggests that the diverse feature extraction and dynamic feature selection enhanced the feature learning capacity of the proposed network. Moreover, the proposed technique's generalization on unseen test sets with a good recall (0.75) and F-score (0.73) shows its potential use for pathologists' assistance.

Computational pathology in cancer diagnosis, prognosis, and prediction - present day and prospects

Computational pathology refers to applying deep learning techniques and algorithms to analyse and interpret histopathology images. Advances in artificial intelligence (AI) have led to an explosion in innovation in computational pathology, ranging from the prospect of automation of routine diagnostic tasks to the discovery of new prognostic and predictive biomarkers from tissue morphology. Despite the promising potential of computational pathology, its integration in clinical settings has been limited by a range of obstacles including operational, technical, regulatory, ethical, financial, and cultural challenges. Here, we focus on the pathologists' perspective of computational pathology: we map its current translational research landscape, evaluate its clinical utility, and address the more common challenges slowing clinical adoption and implementation. We conclude by describing contemporary approaches to drive forward these techniques. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Modelling the Tumour Microenvironment, but What Exactly Do We Mean by "Model"?

The Oxford English Dictionary includes 17 definitions for the word "model" as a noun and another 11 as a verb. Therefore, context is necessary to understand the meaning of the word model. For instance, "model railways" refer to replicas of railways and trains at a smaller scale and a "model student" refers to an exemplary individual. In some cases, a specific context, like cancer research, may not be sufficient to provide one specific meaning for model. Even if the context is narrowed, specifically, to research related to the tumour microenvironment, "model" can be understood in a wide variety of ways, from an animal model to a mathematical expression. This paper presents a review of different "models" of the tumour microenvironment, as grouped by different definitions of the word into four categories: model organisms, in vitro models, mathematical models and computational models. Then, the frequencies of different meanings of the word "model" related to the tumour microenvironment are measured from numbers of entries in the MEDLINE database of the United States National Library of Medicine at the National Institutes of Health. The frequencies of the main components of the microenvironment and the organ-related cancers modelled are also assessed quantitatively with specific keywords. Whilst animal models, particularly xenografts and mouse models, are the most commonly used "models", the number of these entries has been slowly decreasing. Mathematical models, as well as prognostic and risk models, follow in frequency, and these have been growing in use.

Artificial Intelligence for Image Analysis in Oral Squamous Cell Carcinoma: A Review

Head and neck tumor differential diagnosis and prognosis have always been a challenge for oral pathologists due to their similarities and complexity. Artificial intelligence novel applications can function as an auxiliary tool for the objective interpretation of histomorphological digital slides. In this review, we present digital histopathological image analysis applications in oral squamous cell carcinoma. A literature search was performed in PubMed MEDLINE with the following keywords: "artificial intelligence" OR "deep learning" OR "machine learning" AND "oral squamous cell carcinoma". Artificial intelligence has proven to be a helpful tool in histopathological image analysis of tumors and other lesions, even though it is necessary to continue researching in this area, mainly for clinical validation.

Systemic Immune-Inflammatory Index, Tumor-Infiltrating Lymphocytes, and Clinical Outcomes in Esophageal Squamous Cell Carcinoma Receiving Concurrent Chemoradiotherapy

Background

Systemic inflammation may be involved in the entire cancer process as a promoter and is associated with antitumor immunity. The systemic immune-inflammation index (SII) has been shown to be a promising prognostic factor. However, the relationship between SII and tumor-infiltrating lymphocytes (TIL) have not been established in esophageal cancer (EC) patients receiving concurrent chemoradiotherapy (CCRT).

Methods

Retrospective analysis of 160 patients with EC was performed, peripheral blood cell counts were collected, and TIL concentration was assessed in H&E-stained sections. Correlations of SII and clinical outcomes with TIL were analyzed. Cox proportional hazard model and Kaplan-Meier method were used to perform survival outcomes.

Results

Compared with high SII, low SII had longer overall survival (OS) (P = 0.036, hazard ratio (HR) = 0.59) and progression-free survival (PFS) (P = 0.041, HR = 0.60). Low TIL showed worse OS (P < 0.001, HR = 2.42) and PFS (P < 0.001, HR = 3.05). In addition, research have shown that the distribution of SII, platelet-to-lymphocyte ratio, and neutrophil-to-lymphocyte ratio were negatively associated with the TIL state, while lymphocyte-to-monocyte ratio presented a positive correlation. Combination analysis observed that SII^low + TIL^high had the best prognosis of all combinations, with a median OS and PFS of 36 and 22 months, respectively. The worst prognosis was identified as SII^high + TIL^low, with a median OS and PFS of only 8 and 4 months.

Conclusion

SII and TIL as independent predictors of clinical outcomes in EC receiving CCRT. Furthermore, the predictive power of the two combinations is much higher than a single variable.

The Prognostic Role of the Immune Microenvironment in Sinonasal Intestinal-Type Adenocarcinoma: A Computer-Assisted Image Analysis of CD3+ and CD8+ Tumor-Infiltrating Lymphocytes

The prognostic value of conventional histopathological parameters in the sinonasal intestinal-type adenocarcinoma (ITAC) has been debated and novel variables should be investigated. Increasing evidence demonstrated that the evolution of cancer is strongly dependent upon the complex interactions within tumor microenvironment. The aim of this retrospective study was to assess the features of immune microenvironment in terms of CD3⁺ and CD8⁺ cells in a series of ITAC and explore their prognostic role, as well as their relations with clinicopathological variables. A computer-assisted image analysis of CD3⁺ and CD8⁺ tumor-infiltrating lymphocytes (TIL) density was conducted on surgical specimens of 51 patients with ITAC that underwent a curative treatment including surgery. ITAC displays variable TIL density, which is associated with OS. In a univariate model, the density of CD3⁺ TIL was significantly related to OS (p = 0.012), whereas the association with CD8+ TIL density resulted in being non-significant (p = 0.056). Patients with intermediate CD3⁺ TIL density were associated with the best outcome, whereas 5-year OS was the lowest for intermediate CD8⁺ TIL density. CD3⁺ TIL density maintained a significant association with OS in the multivariable analysis. TIL density was not significantly related to demographic and clinicopathological variables. CD3⁺ TIL density was independently associated with OS in a non-linear fashion and patients with intermediate CD3⁺ TIL density had the best outcome. Though based on a preliminary analysis on a relatively small series of patients, this finding makes TIL density a potential independent prognostic factor of ITAC.

Acidic environment could modulate the interferon-γ expression: Implication on modulation of cancer and immune cells' interactions

Background

In rapidly growing solid tumors, insufficient vascularization and poor oxygen supply result in an acidic tumor microenvironment, which can alter immune response.

Objective

To investigate the role of the acidic microenvironment in immune response modulation along with cancer and immune cells' interactions.

Method

To mimic the tumor microenvironment conditions, T cells (Jurkat), macrophages (THP-1), and HeLa (cervical) cells were cultured under acidic conditions (pH 6.9, pH 6.5) and physiological pH (7.4). The HeLa cell culture medium was exploited as a tumor cell conditioned medium. Real-time PCR was carried out to quantify the mRNA levels, while flow cytometry and western blot hybridization was carried out to ascertain the levels of different proteins.

Results

The acidic microenvironment around the T cells (Jurkat) and macrophage cells (THP-1) could lead to the downregulation of the interferon gamma (IFN-γ). An increase in IFN-γ expression was observed when Jurkat and macrophage cells were cultured in HeLa cells conditioned medium (HCM) at low pH (pH 6.9, pH 6.5). The HeLa cells under acidic environment (pH 6.9, pH 6.5) upregulated interleukin 18 levels and secreted it as exosome anchored. Additionally, enhanced nuclear localization of NF-κB was observed in Jurkat and THP-1 cells cultured in HCM (pH 6.9, pH 6.5). Jurkat and THP-1 cultured in HCM revealed enhanced cytotoxicity against the HeLa cells upon reverting the pH of the medium from acidic to physiological pH (pH 7.4).

Conclusion

Collectively, these results suggest that the acidic microenvironment acted as a key barrier to cancer and immune cells' interactions.

A pathomic approach for tumor-infiltrating lymphocytes classification on breast cancer digital pathology images

Background and objectives

The detection of tumor-infiltrating lymphocytes (TILs) could aid in the development of objective measures of the infiltration grade and can support decision-making in breast cancer (BC). However, manual quantification of TILs in BC histopathological whole slide images (WSI) is currently based on a visual assessment, thus resulting not standardized, not reproducible, and time-consuming for pathologists. In this work, a novel pathomic approach, aimed to apply high-throughput image feature extraction techniques to analyze the microscopic patterns in WSI, is proposed. In fact, pathomic features provide additional information concerning the underlying biological processes compared to the WSI visual interpretation, thus providing more easily interpretable and explainable results than the most frequently investigated Deep Learning based methods in the literature.

Methods

A dataset containing 1037 regions of interest with tissue compartments and TILs annotated on 195 TNBC and HER2+ BC hematoxylin and eosin (H&E)-stained WSI was used. After segmenting nuclei within tumor-associated stroma using a watershed-based approach, 71 pathomic features were extracted from each nucleus and reduced using a Spearman's correlation filter followed by a nonparametric Wilcoxon rank-sum test and least absolute shrinkage and selection operator. The relevant features were used to classify each candidate nucleus as either TILs or non-TILs using 5 multivariable machine learning classification models trained using 5-fold cross-validation (1) without resampling, (2) with the synthetic minority over-sampling technique and (3) with downsampling. The prediction performance of the models was assessed using ROC curves.

Results

21 features were selected, with most of them related to the well-known TILs properties of having regular shape, clearer margins, high peak intensity, more homogeneous enhancement and different textural pattern than other cells. The best performance was obtained by Random-Forest with ROC AUC of 0.86, regardless of resampling technique.

Conclusions

The presented approach holds promise for the classification of TILs in BC H&E-stained WSI and could provide support to pathologists for a reliable, rapid and interpretable clinical assessment of TILs in BC.

Clinical significance of overall assessment of tumor-infiltrating lymphocytes in oropharyngeal cancer: A meta-analysis

BACKGROUND

The overall assessment of tumor-infiltrating lymphocytes (TILs) evaluated using hematoxylin and eosin (HE) staining has been recently studied in oropharyngeal squamous cell carcinoma (OPSCC).

METHODS

We conducted a systematic review of Scopus, Ovid Medline, PubMed, Web of Science, and Cochrane Library to retrieve studies assessing TILs in HE-stained sections of OPSCC. We used fixed-effect models and random-effect models to estimate the pooled hazard ratios (HRs) and confidence intervals (CIs) for disease-free survival (DFS), overall survival (OS) and disease-specific survival (DSS).

RESULTS

Eleven studies were identified that had analyzed the prognostic significance of TILs in OPSCC using HE-stained specimens. Our meta-analyses showed that a high infiltration of TILs was significantly associated with improved DFS (HR 0.39, 95%CI 0.24-0.65, P = 0.0003), OS (HR 0.38, 95%CI 0.29-0.50, P < 0.0001), and DSS (HR 0.32, 95%CI 0.19-0.53, P < 0.0001).

CONCLUSION

Findings of our meta-analysis support a growing body of evidence indicating that assessment of TILs in OPSCC using HE-stained sections has reliable prognostic value. The clinical significance of such assessment of TILs has been reported repeatedly in many studies on OPSCC. The assessment is cost-effective, feasible, easy to transfer from lab to clinic, and therefore can be incorporated in daily practice.

Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network

Medical image analysis places a significant focus on breast cancer, which poses a significant threat to women's health and contributes to many fatalities. An early and precise diagnosis of breast cancer through digital mammograms can significantly improve the accuracy of disease detection. Computer-aided diagnosis (CAD) systems must analyze the medical imagery and perform detection, segmentation, and classification processes to assist radiologists with accurately detecting breast lesions. However, early-stage mammography cancer detection is certainly difficult. The deep convolutional neural network has demonstrated exceptional results and is considered a highly effective tool in the field. This study proposes a computational framework for diagnosing breast cancer using a ResNet-50 convolutional neural network to classify mammogram images. To train and classify the INbreast dataset into benign or malignant categories, the framework utilizes transfer learning from the pretrained ResNet-50 CNN on ImageNet. The results revealed that the proposed framework achieved an outstanding classification accuracy of 93%, surpassing other models trained on the same dataset. This novel approach facilitates early diagnosis and classification of malignant and benign breast cancer, potentially saving lives and resources. These outcomes highlight that deep convolutional neural network algorithms can be trained to achieve highly accurate results in various mammograms, along with the capacity to enhance medical tools by reducing the error rate in screening mammograms.

Artificial intelligence to identify genetic alterations in conventional histopathology

Precision oncology relies on the identification of targetable molecular alterations in tumor tissues. In many tumor types, a limited set of molecular tests is currently part of standard diagnostic workflows. However, universal testing for all targetable alterations, especially rare ones, is limited by the cost and availability of molecular assays. From 2017 to 2021, multiple studies have shown that artificial intelligence (AI) methods can predict the probability of specific genetic alterations directly from conventional hematoxylin and eosin (H&E) tissue slides. Although these methods are currently less accurate than gold-standard testing (e.g. immunohistochemistry, polymerase chain reaction or next-generation sequencing), they could be used as pre-screening tools to reduce the workload of genetic analyses. In this systematic literature review, we summarize the state of the art in predicting molecular alterations from H&E using AI. We found that AI methods perform reasonably well across multiple tumor types, although few algorithms have been broadly validated. In addition, we found that genetic alterations in FGFR, IDH, PIK3CA, BRAF, TP53 and DNA repair pathways are predictable from H&E in multiple tumor types, while many other genetic alterations have rarely been investigated or were only poorly predictable. Finally, we discuss the next steps for the implementation of AI-based surrogate tests in diagnostic workflows. This article is protected by copyright. All rights reserved.

TTCNN: A Breast Cancer Detection and Classification towards Computer-Aided Diagnosis Using Digital Mammography in Early Stages

Breast cancer is a major research area in the medical image analysis field; it is a dangerous disease and a major cause of death among women. Early and accurate diagnosis of breast cancer based on digital mammograms can enhance disease detection accuracy. Medical imagery must be detected, segmented, and classified for computer-aided diagnosis (CAD) systems to help the radiologists for accurate diagnosis of breast lesions. Therefore, an accurate breast cancer detection and classification approach is proposed for screening of mammograms. In this paper, we present a deep learning system that can identify breast cancer in mammogram screening images using an “end-to-end” training strategy that efficiently uses mammography images for computer-aided breast cancer recognition in the early stages. First, the proposed approach implements the modified contrast enhancement method in order to refine the detail of edges from the source mammogram images. Next, the transferable texture convolutional neural network (TTCNN) is presented to enhance the performance of classification and the energy layer is integrated in this work to extract the texture features from the convolutional layer. The proposed approach consists of only three layers of convolution and one energy layer, rather than the pooling layer. In the third stage, we analyzed the performance of TTCNN based on deep features of convolutional neural network models (InceptionResNet-V2, Inception-V3, VGG-16, VGG-19, GoogLeNet, ResNet-18, ResNet-50, and ResNet-101). The deep features are extracted by determining the best layers which enhance the classification accuracy. In the fourth stage, by using the convolutional sparse image decomposition approach, all the extracted feature vectors are fused and, finally, the best features are selected by using the entropy controlled firefly method. The proposed approach employed on DDSM, INbreast, and MIAS datasets and attained the average accuracy of 97.49%. Our proposed transferable texture CNN-based method for classifying screening mammograms has outperformed prior methods. These findings demonstrate that automatic deep learning algorithms can be easily trained to achieve high accuracy in diverse mammography images, and can offer great potential to improve clinical tools to minimize false positive and false negative screening mammography results.

Tumor-Infiltrating Lymphocytes in Head and Neck Cancer: Ready for Prime Time?

The evaluation of tumor-infiltrating lymphocytes (TILs) has received global attention as a promising prognostic cancer biomarker that can aid in clinical decision making. Proof of their significance was first shown in breast cancer, where TILs are now recommended in the classification of breast tumors. Emerging evidence indicates that the significance of TILs extends to other cancer types, including head and neck cancer. In the era of immunotherapy as a treatment choice for head and neck cancer, assessment of TILs and immune checkpoints is of high clinical relevance. The availability of the standardized method from the International Immuno-oncology Biomarker Working Group (IIBWG) is an important cornerstone toward standardized assessment. The aim of the current article is to summarize the accumulated evidence and to establish a clear premise for future research toward the implementation of TILs in the personalized management of head and neck squamous cell carcinoma patients.