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

Application of machine learning based genome sequence analysis in pathogen identification

Infectious diseases caused by pathogenic microorganisms pose a serious threat to human health. Despite advances in molecular biology, genetics, computation, and medicinal chemistry, infectious diseases remain a significant public health concern. Addressing the challenges posed by pathogen outbreaks, pandemics, and antimicrobial resistance requires concerted interdisciplinary efforts. With the development of computer technology and the continuous exploration of artificial intelligence(AI)applications in the biomedical field, the automatic morphological recognition and image processing of microbial images under microscopes have advanced rapidly. The research team of Institute of Microbiology, Chinese Academy of Sciences has developed a single cell microbial identification technology combining Raman spectroscopy and artificial intelligence. Through laser Raman acquisition system and convolutional neural network analysis, the average accuracy rate of 95.64% has been achieved, and the identification can be completed in only 5 min. These technologies have shown substantial advantages in the visible morphological detection of pathogenic microorganisms, expanding anti-infective drug discovery, enhancing our understanding of infection biology, and accelerating the development of diagnostics. In this review, we discuss the application of AI-based machine learning in image analysis, genome sequencing data analysis, and natural language processing (NLP) for pathogen identification, highlighting the significant role of artificial intelligence in pathogen diagnosis. AI can improve the accuracy and efficiency of diagnosis, promote early detection and personalized treatment, and enhance public health safety.

Application of Innovative 3D Pathological Tactic for Diagnosis of Organizing Pneumonia

BACKGROUND/AIM

The pathological diagnosis of organizing pneumonia (OP) relies on conventional traditional histopathological analysis, which involves examining stained thin slices of tissue. However, this method often results in suboptimal diagnostic objectivity due to low tissue sampling rates. This study aimed to assess the efficacy of tissue-clearing and infiltration-enhanced 3D spatial imaging techniques for elucidating the tissue architecture of OP.

MATERIALS AND METHODS

H&E staining, 3D imaging technology, and AI-assisted analysis were employed to facilitate the construction of a multidimensional tissue architecture using six OP patient specimens procured from Taichung Veterans General Hospital, enabling a comprehensive morphological assessment.

RESULTS

Specimens underwent H&E staining and exhibited Masson bodies and varying degrees of interstitial fibrosis. Furthermore, we conducted a comprehensive study of 3D images of the pulmonary histology reconstructed through an in-depth pathology analysis, and uncovered heterogenous distributions of fibrosis and Masson bodies across different depths of the OP specimens.

CONCLUSION

Integrating 3D imaging for OP with AI-assisted analysis permits a substantially enhanced visualization and delineation of complex histological pulmonary disorders such as OP. The synergistic application of conventional histopathology with novel 3D imaging elucidated the sophisticated spatial configuration of OP, revealing the presence of Masson bodies and interstitial fibrosis. This methodology transcends conventional pathology constraints and paves the way for advanced algorithmic approaches to enhance precision in the detection, classification, and clinical management of lung pathologies.

Development of a machine learning-based risk model for postoperative complications of lung cancer surgery

PURPOSE

To develop a comorbidity risk score specifically for lung resection surgeries.

METHODS

We reviewed the medical records of patients who underwent lung resections for lung cancer, and developed a risk model using data from 2014 to 2017 (training dataset), validated using data from 2018 to 2019 (validation dataset). Forty variables were analyzed, including 35 factors related to the patient's overall condition and five factors related to surgical techniques and tumor-related factors. The risk model for postoperative complications was developed using an elastic net regularized generalized linear model. The performance of the risk model was evaluated using receiver operating characteristic curves and compared with the Charlson Comorbidity Index (CCI).

RESULTS

The rate of postoperative complications was 34.7% in the training dataset and 21.9% in the validation dataset. The final model consisted of 20 variables, including age, surgical-related factors, respiratory function tests, and comorbidities, such as chronic obstructive pulmonary disease, a history of ischemic heart disease, and 12 blood test results. The area under the curve (AUC) for the developed risk model was 0.734, whereas the AUC for the CCI was 0.521 in the validation dataset.

CONCLUSIONS

The new machine learning model could predict postoperative complications with acceptable accuracy.

CLINICAL REGISTRATION NUMBER

2020-0375.

Artificial intelligence in digital histopathology for predicting patient prognosis and treatment efficacy in breast cancer

INTRODUCTION

Histological images contain phenotypic information predictive of patient outcomes. Due to the heavy workload of pathologists, the time-consuming nature of quantitatively assessing histological features, and human eye limitations to recognize spatial patterns, manually extracting prognostic information in routine pathological workflows remains challenging. Digital pathology has facilitated the mining and quantification of these features utilizing whole-slide image (WSI) scanners and artificial intelligence (AI) algorithms. AI algorithms to identify image-based biomarkers from the tumor microenvironment (TME) have the potential to revolutionize the field of oncology, reducing delays between diagnosis and prognosis determination, allowing for rapid stratification of patients and prescription of optimal treatment regimes, thereby improving patient outcomes.

AREAS COVERED

In this review, the authors discuss how AI algorithms and digital pathology can predict breast cancer patient prognosis and treatment outcomes using image-based biomarkers, along with the challenges of adopting this technology in clinical settings.

EXPERT OPINION

The integration of AI and digital pathology presents significant potential for analyzing the TME and its diagnostic, prognostic, and predictive value in breast cancer patients. Widespread clinical adoption of AI faces ethical, regulatory, and technical challenges, although prospective trials may offer reassurance and promote uptake, ultimately improving patient outcomes by reducing diagnosis-to-prognosis delivery delays.

Advanced 3D In Vitro Lung Fibrosis Models: Contemporary Status, Clinical Uptake, and Prospective Outlooks

Fibrosis has been characterized as a global health problem and ranks as one of the primary causes of organ dysfunction. Currently, there is no cure for pulmonary fibrosis, and limited therapeutic options are available due to an inadequate understanding of the disease pathogenesis. The absence of advanced in vitro models replicating dynamic temporal changes observed in the tissue with the progression of the disease is a significant impediment in the development of novel antifibrotic treatments, which has motivated research on tissue-mimetic three-dimensional (3D) models. In this review, we summarize emerging trends in preparing advanced lung models to recapitulate biochemical and biomechanical processes associated with lung fibrogenesis. We begin by describing the importance of in vivo studies and highlighting the often poor correlation between preclinical research and clinical outcomes and the limitations of conventional cell culture in accurately simulating the 3D tissue microenvironment. Rapid advancement in biomaterials, biofabrication, biomicrofluidics, and related bioengineering techniques are enabling the preparation of in vitro models to reproduce the epithelium structure and operate as reliable drug screening strategies for precise prediction. Improving and understanding these model systems is necessary to find the cross-talks between growing cells and the stage at which myofibroblasts differentiate. These advanced models allow us to utilize the knowledge and identify, characterize, and hand pick medicines beneficial to the human community. The challenges of the current approaches, along with the opportunities for further research with potential for translation in this field, are presented toward developing novel treatments for pulmonary fibrosis.

The Cross-Scale Association between Pathomics and Radiomics Features in Immunotherapy-Treated NSCLC Patients: A Preliminary Study

BACKGROUND

Recent advances in cancer biomarker development have led to a surge of distinct data modalities, such as medical imaging and histopathology. To develop predictive immunotherapy biomarkers, these modalities are leveraged independently, despite their orthogonality. This study aims to explore the cross-scale association between radiological scans and digitalized pathology images for immunotherapy-treated non-small cell lung cancer (NSCLC) patients.

METHODS

This study involves 36 NSCLC patients who were treated with immunotherapy and for whom both radiology and pathology images were available. A total of 851 and 260 features were extracted from CT scans and cell density maps of histology images at different resolutions. We investigated the radiopathomics relationship and their association with clinical and biological endpoints. We used the Kolmogorov-Smirnov (KS) method to test the differences between the distributions of correlation coefficients with the two imaging modality features. Unsupervised clustering was done to identify which imaging modality captures poor and good survival patients.

RESULTS

Our results demonstrated a significant correlation between cell density pathomics and radiomics features. Furthermore, we also found a varying distribution of correlation values between imaging-derived features and clinical endpoints. The KS test revealed that the two imaging feature distributions were different for PFS and CD8 counts, while similar for OS. In addition, clustering analysis resulted in significant differences in the two clusters generated from the radiomics and pathomics features with respect to patient survival and CD8 counts.

CONCLUSION

The results of this study suggest a cross-scale association between CT scans and pathology H&E slides among ICI-treated patients. These relationships can be further explored to develop multimodal immunotherapy biomarkers to advance personalized lung cancer care.

Why do errors arise in artificial intelligence diagnostic tools in histopathology and how can we minimize them?

Artificial intelligence (AI)-based diagnostic tools can offer numerous benefits to the field of histopathology, including improved diagnostic accuracy, efficiency and productivity. As a result, such tools are likely to have an increasing role in routine practice. However, all AI tools are prone to errors, and these AI-associated errors have been identified as a major risk in the introduction of AI into healthcare. The errors made by AI tools are different, in terms of both cause and nature, to the errors made by human pathologists. As highlighted by the National Institute for Health and Care Excellence, it is imperative that practising pathologists understand the potential limitations of AI tools, including the errors made. Pathologists are in a unique position to be gatekeepers of AI tool use, maximizing patient benefit while minimizing harm. Furthermore, their pathological knowledge is essential to understanding when, and why, errors have occurred and so to developing safer future algorithms. This paper summarises the literature on errors made by AI diagnostic tools in histopathology. These include erroneous errors, data concerns (data bias, hidden stratification, data imbalances, distributional shift, and lack of generalisability), reinforcement of outdated practices, unsafe failure mode, automation bias, and insensitivity to impact. Methods to reduce errors in both tool design and clinical use are discussed, and the practical roles for pathologists in error minimisation are highlighted. This aims to inform and empower pathologists to move safely through this seismic change in practice and help ensure that novel AI tools are adopted safely.

Diagnosis of interstitial lung diseases: from Averill A. Liebow to artificial intelligence

Histopathologic criteria of usual interstitial pneumonia (UIP)/idiopathic pulmonary fibrosis (IPF) were defined over the years and endorsed by leading organizations decades after Dr. Averill A. Liebow first coined the term UIP in the 1960s as a distinct pathologic pattern of fibrotic interstitial lung disease. Novel technology and recent research on interstitial lung diseases with genetic component shed light on molecular pathogenesis of UIP/IPF. Two antifibrotic agents introduced in the mid-2010s opened a new era of therapeutic approaches to UIP/IPF, albeit contentious issues regarding their efficacy, side effects, and costs. Recently, the concept of progressive pulmonary fibrosis was introduced to acknowledge additional types of progressive fibrosing interstitial lung diseases with the clinical and pathologic phenotypes comparable to those of UIP/IPF. Likewise, some authors have proposed a paradigm shift by considering UIP as a stand-alone diagnostic entity to encompass other fibrosing interstitial lung diseases that manifest a relentless progression as in IPF. These trends signal a pendulum moving toward the tendency of lumping diagnoses, which poses a risk of obscuring potentially important information crucial to both clinical and research purposes. Recent advances in whole slide imaging for digital pathology and artificial intelligence technology could offer an unprecedented opportunity to enhance histopathologic evaluation of interstitial lung diseases. However, current clinical practice trends of moving away from surgical lung biopsies in interstitial lung disease patients may become a limiting factor in this endeavor as it would be difficult to build a large histopathologic database with correlative clinical data required for artificial intelligence models.

A Review of AI-Based Radiomics and Computational Pathology Approaches in Triple-Negative Breast Cancer: Current Applications and Perspectives

Breast cancer is one of the most common and deadly cancers worldwide. Approximately, 20% of all breast cancers are characterized as triple negative (TNBC). TNBC typically is associated with a poorer prognosis relative to other breast cancer subtypes. Due to its aggressiveness and lack of response to hormonal therapy, conventional cytotoxic chemotherapy is the usual treatment; however, this treatment is not always effective, and an important percentage of patients develop recurrence. More recently, immunotherapy has started to be used on some populations with TNBC showing promising results. Unfortunately, immunotherapy is only applicable to a minority of patients and responses in metastatic TNBC have overall been modest in comparison to other cancer types. This situation evidences the need for developing effective biomarkers that help to stratify and personalize patient management. Thanks to recent advances in artificial intelligence (AI), there has been an increasing interest in its use for medical applications aiming at supporting clinical decision making. Several works have used AI in combination with diagnostic medical imaging, more specifically radiology and digitized histopathological tissue samples, aiming to extract disease-specific information that is difficult to quantify by the human eye. These works have demonstrated that analysis of such images in the context of TNBC has great potential for (1) risk-stratifying patients to identify those patients who are more likely to experience disease recurrence or die from the disease and (2) predicting pathologic complete response. In this manuscript, we present an overview on AI and its integration with radiology and histopathological images for developing prognostic and predictive approaches for TNBC. We present state of the art approaches in the literature and discuss the opportunities and challenges with developing AI algorithms regarding further development and clinical deployment, including identifying those patients who may benefit from certain treatments (e.g., adjuvant chemotherapy) from those who may not and thereby should be directed toward other therapies, discovering potential differences between populations, and identifying disease subtypes.

Pathomic Features Reveal Immune and Molecular Evolution From Lung Preneoplasia to Invasive Adenocarcinoma

Recent statistics on lung cancer, including the steady decline of advanced diseases and the dramatically increasing detection of early-stage diseases and indeterminate pulmonary nodules, mark the significance of a comprehensive understanding of early lung carcinogenesis. Lung adenocarcinoma (ADC) is the most common histologic subtype of lung cancer, and atypical adenomatous hyperplasia is the only recognized preneoplasia to ADC, which may progress to adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) and eventually to invasive ADC. Although molecular evolution during early lung carcinogenesis has been explored in recent years, the progress has been significantly hindered, largely due to insufficient materials from ADC precursors. Here, we employed state-of-the-art deep learning and artificial intelligence techniques to robustly segment and recognize cells on routinely used hematoxylin and eosin histopathology images and extracted 9 biology-relevant pathomic features to decode lung preneoplasia evolution. We analyzed 3 distinct cohorts (Japan, China, and United States) covering 98 patients, 162 slides, and 669 regions of interest, including 143 normal, 129 atypical adenomatous hyperplasia, 94 AIS, 98 MIA, and 205 ADC. Extracted pathomic features revealed progressive increase of atypical epithelial cells and progressive decrease of lymphocytic cells from normal to AAH, AIS, MIA, and ADC, consistent with the results from tissue-consuming and expensive molecular/immune profiling. Furthermore, pathomics analysis manifested progressively increasing cellular intratumor heterogeneity along with the evolution from normal lung to invasive ADC. These findings demonstrated the feasibility and substantial potential of pathomics in studying lung cancer carcinogenesis directly from the low-cost routine hematoxylin and eosin staining.

Deep Learning for Predicting Effect of Neoadjuvant Therapies in Non-Small Cell Lung Carcinomas With Histologic Images

Neoadjuvant therapies are used for locally advanced non-small cell lung carcinomas, whereby pathologists histologically evaluate the effect using resected specimens. Major pathological response (MPR) has recently been used for treatment evaluation and as an economical survival surrogate; however, interobserver variability and poor reproducibility are often noted. The aim of this study was to develop a deep learning (DL) model to predict MPR from hematoxylin and eosin-stained tissue images and to validate its utility for clinical use. We collected data on 125 primary non-small cell lung carcinoma cases that were resected after neoadjuvant therapy. The cases were randomly divided into 55 for training/validation and 70 for testing. A total of 261 hematoxylin and eosin-stained slides were obtained from the maximum tumor beds, and whole slide images were prepared. We used a multiscale patch model that can adaptively weight multiple convolutional neural networks trained with different field-of-view images. We performed 3-fold cross-validation to evaluate the model. During testing, we compared the percentages of viable tumor evaluated by annotator pathologists (reviewed data), those evaluated by nonannotator pathologists (primary data), and those predicted by the DL-based model using 2-class confusion matrices and receiver operating characteristic curves and performed a survival analysis between MPR-achieved and non-MPR cases. In cross-validation, accuracy and mean F1 score were 0.859 and 0.805, respectively. During testing, accuracy and mean F1 score with reviewed data and those with primary data were 0.986, 0.985, 0.943, and 0.943, respectively. The areas under the receiver operating characteristic curve with reviewed and primary data were 0.999 and 0.978, respectively. The disease-free survival of MPR-achieved cases with reviewed and primary data was significantly better than that of the non-MPR cases (P<.001 and P=.001), and that predicted by the DL-based model was almost identical (P=.005). The DL model may support pathologist evaluations and can offer accurate determinations of MPR in patients.

Application of InceptionV3, SqueezeNet, and VGG16 Convoluted Neural Networks in the Image Classification of Oral Squamous Cell Carcinoma: A Cross-Sectional Study

Background Artificial intelligence (AI) is a rapidly emerging field in medicine and has applications in diagnostics, therapeutics, and prognostication in various malignancies. The present study was conducted to analyze and compare the accuracy of three deep learning neural networks for oral squamous cell carcinoma (OSCC) images. Materials and methods Three hundred and twenty-five cases of OSCC were included and graded histologically by two grading systems. The images were then analyzed using the Orange data mining tool. Three neural networks, viz., InceptionV3, SqueezeNet, and VGG16, were used for further analysis and classification. Positive predictive value, negative predictive value, specificity, sensitivity, area under curve (AUC), and accuracy were estimated for each neural network. Results Histological grading by Bryne's yielded significantly stronger inter-observer agreement. The highest accuracy was found for the classification of poorly differentiated squamous cell carcinoma images irrespective of the network used. Other values were variegated. Conclusion AI could serve as an adjunct for improvement in theragnostics. Further research is required to achieve the modification of mining tools for greater predictive values, sensitivity, specificity, AUC, accuracy, and security. Bryne's grading system is warranted for the better application of AI in OSCC image analytics.

Phenotyping the virulence of SARS-CoV-2 variants in hamsters by digital pathology and machine learning

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has continued to evolve throughout the coronavirus disease-19 (COVID-19) pandemic, giving rise to multiple variants of concern (VOCs) with different biological properties. As the pandemic progresses, it will be essential to test in near real time the potential of any new emerging variant to cause severe disease. BA.1 (Omicron) was shown to be attenuated compared to the previous VOCs like Delta, but it is possible that newly emerging variants may regain a virulent phenotype. Hamsters have been proven to be an exceedingly good model for SARS-CoV-2 pathogenesis. Here, we aimed to develop robust quantitative pipelines to assess the virulence of SARS-CoV-2 variants in hamsters. We used various approaches including RNAseq, RNA in situ hybridization, immunohistochemistry, and digital pathology, including software assisted whole section imaging and downstream automatic analyses enhanced by machine learning, to develop methods to assess and quantify virus-induced pulmonary lesions in an unbiased manner. Initially, we used Delta and Omicron to develop our experimental pipelines. We then assessed the virulence of recent Omicron sub-lineages including BA.5, XBB, BQ.1.18, BA.2, BA.2.75 and EG.5.1. We show that in experimentally infected hamsters, accurate quantification of alveolar epithelial hyperplasia and macrophage infiltrates represent robust markers for assessing the extent of virus-induced pulmonary pathology, and hence virus virulence. In addition, using these pipelines, we could reveal how some Omicron sub-lineages (e.g., BA.2.75 and EG.5.1) have regained virulence compared to the original BA.1. Finally, to maximise the utility of the digital pathology pipelines reported in our study, we developed an online repository containing representative whole organ histopathology sections that can be visualised at variable magnifications (https://covid-atlas.cvr.gla.ac.uk). Overall, this pipeline can provide unbiased and invaluable data for rapidly assessing newly emerging variants and their potential to cause severe disease.

Unraveling the potential of 3D bioprinted immunomodulatory materials for regulating macrophage polarization: State-of-the-art in bone and associated tissue regeneration

Macrophage-assisted immunomodulation is an alternative strategy in tissue engineering, wherein the interplay between pro-inflammatory and anti-inflammatory macrophage cells and body cells determines the fate of healing or inflammation. Although several reports have demonstrated that tissue regeneration depends on spatial and temporal regulation of the biophysical or biochemical microenvironment of the biomaterial, the underlying molecular mechanism behind immunomodulation is still under consideration for developing immunomodulatory scaffolds. Currently, most fabricated immunomodulatory platforms reported in the literature show regenerative capabilities of a particular tissue, for example, endogenous tissue (e.g., bone, muscle, heart, kidney, and lungs) or exogenous tissue (e.g., skin and eye). In this review, we briefly introduced the necessity of the 3D immunomodulatory scaffolds and nanomaterials, focusing on material properties and their interaction with macrophages for general readers. This review also provides a comprehensive summary of macrophage origin and taxonomy, their diverse functions, and various signal transduction pathways during biomaterial-macrophage interaction, which is particularly helpful for material scientists and clinicians for developing next-generation immunomodulatory scaffolds. From a clinical standpoint, we briefly discussed the role of 3D biomaterial scaffolds and/or nanomaterial composites for macrophage-assisted tissue engineering with a special focus on bone and associated tissues. Finally, a summary with expert opinion is presented to address the challenges and future necessity of 3D bioprinted immunomodulatory materials for tissue engineering.

Clinical severity classes in COVID-19 pneumonia have distinct immunological profiles, facilitating risk stratification by machine learning

Objective

Clinical triage in coronavirus disease 2019 (COVID-19) places a heavy burden on senior clinicians during a pandemic situation. However, risk stratification based on serum biomarker bioprofiling could be implemented by a larger, nonspecialist workforce.

Method

Measures of Complement Activation and inflammation in patientS with CoronAvirus DisEase 2019 (CASCADE) patients (n = 72), (clinicaltrials.gov: NCT04453527), classified as mild, moderate, or severe (by support needed to maintain SpO2 > 93%), and healthy controls (HC, n = 20), were bioprofiled using 76 immunological biomarkers and compared using ANOVA. Spearman correlation analysis on biomarker pairs was visualised via heatmaps. Linear Discriminant Analysis (LDA) models were generated to identify patients likely to deteriorate. An X-Gradient-boost (XGB) model trained on CASCADE data to triage patients as mild, moderate, and severe was retrospectively employed to classify COROnavirus Nomacopan Emergency Treatment for covid 19 infected patients with early signs of respiratory distress (CORONET) patients (n = 7) treated with nomacopan.

Results

The LDA models distinctly discriminated between deteriorators, nondeteriorators, and HC, with IL-27, IP-10, MDC, ferritin, C5, and sC5b-9 among the key predictor variables during deterioration. C3a and C5 were elevated in all severity classes vs. HC (p < 0.05). sC5b-9 was elevated in the "moderate" and "severe" categories vs. HC (p < 0.001). Heatmap analysis shows a pairwise increase of negatively correlated pairs with IL-27. The XGB model indicated sC5b-9, IL-8, MCP1, and prothrombin F1 and F2 were key discriminators in nomacopan-treated patients (CORONET study).

Conclusion

Distinct immunological fingerprints from serum biomarkers exist within different severity classes of COVID-19, and harnessing them using machine learning enabled the development of clinically useful triage and prognostic tools. Complement-mediated lung injury plays a key role in COVID-19 pneumonia, and preliminary results hint at the usefulness of a C5 inhibitor in COVID-19 recovery.

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.

Unleashing the potential of AI for pathology: challenges and recommendations

Computational pathology is currently witnessing a surge in the development of AI techniques, offering promise for achieving breakthroughs and significantly impacting the practices of pathology and oncology. These AI methods bring with them the potential to revolutionize diagnostic pipelines as well as treatment planning and overall patient care. Numerous peer-reviewed studies reporting remarkable performance across diverse tasks serve as a testimony to the potential of AI in the field. However, widespread adoption of these methods in clinical and pre-clinical settings still remains a challenge. In this review article, we present a detailed analysis of the major obstacles encountered during the development of effective models and their deployment in practice. We aim to provide readers with an overview of the latest developments, assist them with insights into identifying some specific challenges that may require resolution, and suggest recommendations and potential future research directions. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Leveraging the Academic Artificial Intelligence Silecosystem to Advance the Community Oncology Enterprise

Over the last 75 years, artificial intelligence has evolved from a theoretical concept and novel paradigm describing the role that computers might play in our society to a tool with which we daily engage. In this review, we describe AI in terms of its constituent elements, the synthesis of which we refer to as the AI Silecosystem. Herein, we provide an historical perspective of the evolution of the AI Silecosystem, conceptualized and summarized as a Kuhnian paradigm. This manuscript focuses on the role that the AI Silecosystem plays in oncology and its emerging importance in the care of the community oncology patient. We observe that this important role arises out of a unique alliance between the academic oncology enterprise and community oncology practices. We provide evidence of this alliance by illustrating the practical establishment of the AI Silecosystem at the City of Hope Comprehensive Cancer Center and its team utilization by community oncology providers.

A Deep Learning-Based System Trained for Gastrointestinal Stromal Tumor Screening Can Identify Multiple Types of Soft Tissue Tumors

The accuracy and timeliness of the pathologic diagnosis of soft tissue tumors (STTs) critically affect treatment decision and patient prognosis. Thus, it is crucial to make a preliminary judgement on whether the tumor is benign or malignant with hematoxylin and eosin-stained images. A deep learning-based system, Soft Tissue Tumor Box (STT-BOX), is presented herein, with only hematoxylin and eosin images for malignant STT identification from benign STTs with histopathologic similarity. STT-BOX assumed gastrointestinal stromal tumor as a baseline for malignant STT evaluation, and distinguished gastrointestinal stromal tumor from leiomyoma and schwannoma with 100% area under the curve in patients from three hospitals, which achieved higher accuracy than the interpretation of experienced pathologists. Particularly, this system performed well on six common types of malignant STTs from The Cancer Genome Atlas data set, accurately highlighting the malignant mass lesion. STT-BOX was able to distinguish ovarian malignant sex-cord stromal tumors without any fine-tuning. This study included mesenchymal tumors that originated from the digestive system, bone and soft tissues, and reproductive system, where the high accuracy of migration verification may reveal the morphologic similarity of the nine types of malignant tumors. Further evaluation in a pan-STT setting would be potential and prospective, obviating the overuse of immunohistochemistry and molecular tests, and providing a practical basis for clinical treatment selection in a timely manner.

ERS International Congress 2022: highlights from the Thoracic Oncology Assembly

Thoracic malignancies are associated with a substantial public health burden. Lung cancer is the leading cause of cancer-related mortality worldwide, with significant impact on patients' quality of life. Following 2 years of virtual European Respiratory Society (ERS) Congresses due to the COVID-19 pandemic, the 2022 hybrid ERS Congress in Barcelona, Spain allowed peers from all over the world to meet again and present their work. Thoracic oncology experts presented best practices and latest developments in lung cancer screening, lung cancer diagnosis and management. Early lung cancer diagnosis, subsequent pros and cons of aggressive management, identification and management of systemic treatments' side-effects, and the application of artificial intelligence and biomarkers across all aspects of the thoracic oncology pathway were among the areas that triggered specific interest and will be summarised here.

An online survival predictor in glioma patients using machine learning based on WHO CNS5 data

Background

The World Health Organization (WHO) CNS5 classification system highlights the significance of molecular biomarkers in providing meaningful prognostic and therapeutic information for gliomas. However, predicting individual patient survival remains challenging due to the lack of integrated quantitative assessment tools. In this study, we aimed to design a WHO CNS5-related risk signature to predict the overall survival (OS) rate of glioma patients using machine learning algorithms.

Methods

We extracted data from patients who underwent an operation for histopathologically confirmed glioma from our hospital database (2011-2022) and split them into a training and hold-out test set in a 7/3 ratio. We used biological markers related to WHO CNS5, clinical data (age, sex, and WHO grade), and prognosis follow-up information to identify prognostic factors and construct a predictive dynamic nomograph to predict the survival rate of glioma patients using 4 kinds machine learning algorithms (RF, SVM, XGB, and GLM).

Results

A total of 198 patients with complete WHO5 molecular data and follow-up information were included in the study. The median OS time of all patients was 29.77 [95% confidence interval (CI): 21.19-38.34] months. Age, FGFR2, IDH1, CDK4, CDK6, KIT, and CDKN2A were considered vital indicators related to the prognosis and OS time of glioma. To better predict the prognosis of glioma patients, we constructed a WHO5-related risk signature and nomogram. The AUC values of the ROC curves of the nomogram for predicting the 1, 3, and 5-year OS were 0.849, 0.835, and 0.821 in training set, and, 0.844, 0.943, and 0.959 in validation set. The calibration plot confirmed the reliability of the nomogram, and the c-index was 0.742 in training set and 0.775 in validation set. Additionally, our nomogram showed a superior net benefit across a broader scale of threshold probabilities in decision curve analysis. Therefore, we selected it as the backend for the online survival prediction tool (Glioma Survival Calculator, https://who5pumch.shinyapps.io/DynNomapp/), which can calculate the survival probability for a specific time of the patients.

Conclusion

An online prognosis predictor based on WHO5-related biomarkers was constructed. This therapeutically promising tool may increase the precision of forecast therapy outcomes and assess prognosis.

Quantifying Intratumoral Heterogeneity and Immunoarchitecture Generated In-Silico by a Spatial Quantitative Systems Pharmacology Model

Spatial heterogeneity is a hallmark of cancer. Tumor heterogeneity can vary with time and location. The tumor microenvironment (TME) encompasses various cell types and their interactions that impart response to therapies. Therefore, a quantitative evaluation of tumor heterogeneity is crucial for the development of effective treatments. Different approaches, such as multiregional sequencing, spatial transcriptomics, analysis of autopsy samples, and longitudinal analysis of biopsy samples, can be used to analyze the intratumoral heterogeneity (ITH) and temporal evolution and to reveal the mechanisms of therapeutic response. However, because of the limitations of these data and the uncertainty associated with the time points of sample collection, having a complete understanding of intratumoral heterogeneity role is challenging. Here, we used a hybrid model that integrates a whole-patient compartmental quantitative-systems-pharmacology (QSP) model with a spatial agent-based model (ABM) describing the TME; we applied four spatial metrics to quantify model-simulated intratumoral heterogeneity and classified the TME immunoarchitecture for representative cases of effective and ineffective anti-PD-1 therapy. The four metrics, adopted from computational digital pathology, included mixing score, average neighbor frequency, Shannon's entropy and area under the curve (AUC) of the G-cross function. A fifth non-spatial metric was used to supplement the analysis, which was the ratio of the number of cancer cells to immune cells. These metrics were utilized to classify the TME as "cold", "compartmentalized" and "mixed", which were related to treatment efficacy. The trends in these metrics for effective and ineffective treatments are in qualitative agreement with the clinical literature, indicating that compartmentalized immunoarchitecture is likely to result in more efficacious treatment outcomes.

The Artificial Intelligence in Teledermatology: A Narrative Review on Opportunities, Perspectives, and Bottlenecks

Artificial intelligence (AI) is recently seeing significant advances in teledermatology (TD), also thanks to the developments that have taken place during the COVID-19 pandemic. In the last two years, there was an important development of studies that focused on opportunities, perspectives, and problems in this field. The topic is very important because the telemedicine and AI applied to dermatology have the opportunity to improve both the quality of healthcare for citizens and the workflow of healthcare professionals. This study conducted an overview on the opportunities, the perspectives, and the problems related to the integration of TD with AI. The methodology of this review, following a standardized checklist, was based on: (I) a search of PubMed and Scopus and (II) an eligibility assessment, using parameters with five levels of score. The outcome highlighted that applications of this integration have been identified in various skin pathologies and in quality control, both in eHealth and mHealth. Many of these applications are based on Apps used by citizens in mHealth for self-care with new opportunities but also open questions. A generalized enthusiasm has been registered regarding the opportunities and general perspectives on improving the quality of care, optimizing the healthcare processes, minimizing costs, reducing the stress in the healthcare facilities, and in making citizens, now at the center, more satisfied. However, critical issues have emerged related to: (a) the need to improve the process of diffusion of the Apps in the hands of citizens, with better design, validation, standardization, and cybersecurity; (b) the need for better attention paid to medico-legal and ethical issues; and (c) the need for the stabilization of international and national regulations. Targeted agreement initiatives, such as position statements, guidelines, and/or consensus initiatives, are needed to ensure a better result for all, along with the design of both specific plans and shared workflows.

The Long Run towards Personalized Therapy in Non-Small-Cell Lung Cancer: Current State and Future Directions

Non-small-cell lung cancer (NSCLC) is the major cause of cancer-related deaths worldwide, due to its high incidence and mortality [...].

Explainability and causability in digital pathology

The current move towards digital pathology enables pathologists to use artificial intelligence (AI)-based computer programmes for the advanced analysis of whole slide images. However, currently, the best-performing AI algorithms for image analysis are deemed black boxes since it remains - even to their developers - often unclear why the algorithm delivered a particular result. Especially in medicine, a better understanding of algorithmic decisions is essential to avoid mistakes and adverse effects on patients. This review article aims to provide medical experts with insights on the issue of explainability in digital pathology. A short introduction to the relevant underlying core concepts of machine learning shall nurture the reader's understanding of why explainability is a specific issue in this field. Addressing this issue of explainability, the rapidly evolving research field of explainable AI (XAI) has developed many techniques and methods to make black-box machine-learning systems more transparent. These XAI methods are a first step towards making black-box AI systems understandable by humans. However, we argue that an explanation interface must complement these explainable models to make their results useful to human stakeholders and achieve a high level of causability, i.e. a high level of causal understanding by the user. This is especially relevant in the medical field since explainability and causability play a crucial role also for compliance with regulatory requirements. We conclude by promoting the need for novel user interfaces for AI applications in pathology, which enable contextual understanding and allow the medical expert to ask interactive 'what-if'-questions. In pathology, such user interfaces will not only be important to achieve a high level of causability. They will also be crucial for keeping the human-in-the-loop and bringing medical experts' experience and conceptual knowledge to AI processes.

Predicting peritoneal recurrence in gastric cancer with serosal invasion using a pathomics nomogram

Peritoneal recurrence is the most frequent and lethal recurrence pattern in gastric cancer (GC) with serosal invasion after radical surgery. However, current evaluation methods are not adequate for predicting peritoneal recurrence in GC with serosal invasion. Emerging evidence shows that pathomics analyses could be advantageous for risk stratification and outcome prediction. Herein, we propose a pathomics signature composed of multiple pathomics features extracted from digital hematoxylin and eosin-stained images. We found that the pathomics signature was significantly associated with peritoneal recurrence. A competing-risk pathomics nomogram including carbohydrate antigen 19-9 level, depth of invasion, lymph node metastasis, and pathomics signature was developed for predicting peritoneal recurrence. The pathomics nomogram had favorable discrimination and calibration. Thus, the pathomics signature is a predictive indicator of peritoneal recurrence, and the pathomics nomogram may provide a helpful reference for predicting an individual's risk in peritoneal recurrence of GC with serosal invasion.

See Lung Cancer with an AI

A lot has happened in the field of lung cancer screening in recent months. The ongoing discussion and documentation published by the scientific community and policymakers are of great importance to the entire European community and perhaps beyond. Lung cancer is the main worldwide killer. Low-dose computed tomography-based screening, together with smoking cessation, is the only tool to fight lung cancer, as it has already been proven in the United States of America but also European randomized controlled trials. Screening requires a lot of well-organized specialized work, but it can be supported by artificial intelligence (AI). Here we discuss whether and how to use AI for patients, radiologists, pulmonologists, thoracic surgeons, and all hospital staff supporting screening process benefits.

Study on the Innovative Development of Digital Media Art in the Context of Artificial Intelligence

With the rapid development of modern science and technology, the speed of digital media to disseminate information is also accelerating, and the forms of communication become more and more diversified. In order to make digital media art better innovate and develop, designers should actively explore, so that art under digital media has the characteristics of intelligence, networking, and content diversification, so that it can be better applied in modern digital media communication and let visual art reach new development heights in the context of digital media. The current rapid development of artificial intelligence technology is being gradually applied to all fields of society. From the current point of view, China's digital media art will inevitably remain in the early development stage for a long time to face many difficulties and problems. In the context of the era of artificial intelligence, there will also be a large number of art and design talents for continuous research and exploration, and the emergence of these talents will certainly prompt the further development and enhancement of digital media art and artificial intelligence technology. In this process, it is especially worth noting that China's excellent cultural connotations should not be abandoned. Committed to digging more beneficial digital media art elements from traditional culture to make digital media art always develop in a more meaningful way. This paper takes the artificial intelligence era as the background, discusses the core of the development of the mutual integration of artificial intelligence technology and digital media art, analyzes the current development status of digital media art and technology, as well as the innovative development direction and future trends, proposes a digital media art design algorithm based on the convolutional neural network, and finally proves the effectiveness of the method in the relevant data set.