Predicting gene mutation status via artificial intelligence technologies based on multimodal integration (MMI) to advance precision oncology

Troponin C gene mutations on cardiac muscle cell and skeletal Regulation: A comprehensive review

BACKGROUND

The troponin complex plays a crucial role in regulating skeletal and cardiac contraction. Congenital myopathies can occur due to several mutations in genes that encode skeletal troponin. Moreover, there is limited information regarding the composition of skeletal troponin. This review specifically examines a comprehensive review of the TNNC gene mutations on cardiac and skeletal regulations.

MAIN BODY

Troponin C (TNNC) has been linked to a newly discovered inherited muscle disorder. Genetic variations in genes that encode skeletal troponin can impair the function of sarcomeres. Various treatment approaches have been employed to mitigate the impact of variations, including the use of troponin activators, the injection of wild-type protein via AAV gene therapy, and myosin modification to enhance muscle contraction. The processes responsible for the pathophysiological implications of the variations in genes that encode skeletal troponin are not fully understood.

CONCLUSION

This comprehensive review will contribute to the understanding of the relationship between human cardiomyopathy and TNNC mutations and will guide the development of therapy approaches.

Radiomics in breast cancer: Current advances and future directions

Breast cancer is a common disease that causes great health concerns to women worldwide. During the diagnosis and treatment of breast cancer, medical imaging plays an essential role, but its interpretation relies on radiologists or clinical doctors. Radiomics can extract high-throughput quantitative imaging features from images of various modalities via traditional machine learning or deep learning methods following a series of standard processes. Hopefully, radiomic models may aid various processes in clinical practice. In this review, we summarize the current utilization of radiomics for predicting clinicopathological indices and clinical outcomes. We also focus on radio-multi-omics studies that bridge the gap between phenotypic and microscopic scale information. Acknowledging the deficiencies that currently hinder the clinical adoption of radiomic models, we discuss the underlying causes of this situation and propose future directions for advancing radiomics in breast cancer research.

Data-driven risk stratification and precision management of pulmonary nodules detected on chest computed tomography

The widespread implementation of low-dose computed tomography (LDCT) in lung cancer screening has led to the increasing detection of pulmonary nodules. However, precisely evaluating the malignancy risk of pulmonary nodules remains a formidable challenge. Here we propose a triage-driven Chinese Lung Nodules Reporting and Data System (C-Lung-RADS) utilizing a medical checkup cohort of 45,064 cases. The system was operated in a stepwise fashion, initially distinguishing low-, mid-, high- and extremely high-risk nodules based on their size and density. Subsequently, it progressively integrated imaging information, demographic characteristics and follow-up data to pinpoint suspicious malignant nodules and refine the risk scale. The multidimensional system achieved a state-of-the-art performance with an area under the curve (AUC) of 0.918 (95% confidence interval (CI) 0.918-0.919) on the internal testing dataset, outperforming the single-dimensional approach (AUC of 0.881, 95% CI 0.880-0.882). Moreover, C-Lung-RADS exhibited a superior sensitivity compared with Lung-RADS v2022 (87.1% versus 63.3%) in an independent cohort, which was screened using mobile computed tomography scanners to broaden screening accessibility in resource-constrained settings. With its foundation in precise risk stratification and tailored management, this system has minimized unnecessary invasive procedures for low-risk cases and recommended prompt intervention for extremely high-risk nodules to avert diagnostic delays. This approach has the potential to enhance the decision-making paradigm and facilitate a more efficient diagnosis of lung cancer during routine checkups as well as screening scenarios.

Risk Prediction Models for Gastric Cancer: A Scoping Review

Background

Gastric cancer is a significant contributor to the global cancer burden. Risk prediction models aim to estimate future risk based on current and past information, and can be utilized for risk stratification in population screening programs for gastric cancer. This review aims to explore the research design of existing models, as well as the methods, variables, and performance of model construction.

Methods

Six databases were searched through to November 4, 2023 to identify appropriate studies. PRISMA extension for scoping reviews and the Arksey and O'Malley framework were followed. Data sources included PubMed, Embase, Web of Science, CNKI, Wanfang, and VIP, focusing on gastric cancer risk prediction model studies.

Results

A total of 29 articles met the inclusion criteria, from which 28 original risk prediction models were identified that met the analysis criteria. The risk prediction model is screened, and the data extracted includes research characteristics, prediction variables selection, model construction methods and evaluation indicators. The area under the curve (AUC) of the models ranged from 0.560 to 0.989, while the C-statistics varied between 0.684 and 0.940. The number of predictor variables is mainly concentrated between 5 to 11. The top 5 most frequently included variables were age, helicobacter pylori (Hp), precancerous lesion, pepsinogen (PG), sex, and smoking. Age and Hp were the most consistently included variables.

Conclusion

This review enhances understanding of current gastric cancer risk prediction research and its future directions. The findings provide a strong scientific basis and technical support for developing more accurate gastric cancer risk models. We expect that these conclusions will point the way for future research and clinical practice in this area to assist in the early prevention and treatment of gastric cancer.

Artificial Intelligence and Lung Pathology

This manuscript provides a comprehensive overview of the application of artificial intelligence (AI) in lung pathology, particularly in the diagnosis of lung cancer. It discusses various AI models designed to support pathologists and clinicians. AI models supporting pathologists are to standardize diagnosis, score PD-L1 status, supporting tumor cellularity count, and indicating explainability for pathologic judgements. Several models predict outcomes beyond pathologic diagnosis and predict clinical outcomes like patients' survival and molecular alterations. The manuscript emphasizes the potential of AI to enhance accuracy and efficiency in pathology, while also addressing the challenges and future directions for integrating AI into clinical practice.

Deep learning-based multimodal spatial transcriptomics analysis for cancer

The advent of deep learning (DL) and multimodal spatial transcriptomics (ST) has revolutionized cancer research, offering unprecedented insights into tumor biology. This book chapter explores the integration of DL with ST to advance cancer diagnostics, treatment planning, and precision medicine. DL, a subset of artificial intelligence, employs neural networks to model complex patterns in vast datasets, significantly enhancing diagnostic and treatment applications. In oncology, convolutional neural networks excel in image classification, segmentation, and tumor volume analysis, essential for identifying tumors and optimizing radiotherapy. The chapter also delves into multimodal data analysis, which integrates genomic, proteomic, imaging, and clinical data to offer a holistic understanding of cancer biology. Leveraging diverse data sources, researchers can uncover intricate details of tumor heterogeneity, microenvironment interactions, and treatment responses. Examples include integrating MRI data with genomic profiles for accurate glioma grading and combining proteomic and clinical data to uncover drug resistance mechanisms. DL's integration with multimodal data enables comprehensive and actionable insights for cancer diagnosis and treatment. The synergy between DL models and multimodal data analysis enhances diagnostic accuracy, personalized treatment planning, and prognostic modeling. Notable applications include ST, which maps gene expression patterns within tissue contexts, providing critical insights into tumor heterogeneity and potential therapeutic targets. In summary, the integration of DL and multimodal ST represents a paradigm shift towards more precise and personalized oncology. This chapter elucidates the methodologies and applications of these advanced technologies, highlighting their transformative potential in cancer research and clinical practice.

Toward Realizing the Promise of AI in Precision Health Across the Spectrum of Care

Significant progress has been made in augmenting clinical decision-making using artificial intelligence (AI) in the context of secondary and tertiary care at large academic medical centers. For such innovations to have an impact across the spectrum of care, additional challenges must be addressed, including inconsistent use of preventative care and gaps in chronic care management. The integration of additional data, including genomics and data from wearables, could prove critical in addressing these gaps, but technical, legal, and ethical challenges arise. On the technical side, approaches for integrating complex and messy data are needed. Data and design imperfections like selection bias, missing data, and confounding must be addressed. In terms of legal and ethical challenges, while AI has the potential to aid in leveraging patient data to make clinical care decisions, we also risk exacerbating existing disparities. Organizations implementing AI solutions must carefully consider how they can improve care for all and reduce inequities.

A multimodal integration pipeline for accurate diagnosis, pathogen identification, and prognosis prediction of pulmonary infections

Pulmonary infections pose formidable challenges in clinical settings with high mortality rates across all age groups worldwide. Accurate diagnosis and early intervention are crucial to improve patient outcomes. Artificial intelligence (AI) has the capability to mine imaging features specific to different pathogens and fuse multimodal features to reach a synergistic diagnosis, enabling more precise investigation and individualized clinical management. In this study, we successfully developed a multimodal integration (MMI) pipeline to differentiate among bacterial, fungal, and viral pneumonia and pulmonary tuberculosis based on a real-world dataset of 24,107 patients. The area under the curve (AUC) of the MMI system comprising clinical text and computed tomography (CT) image scans yielded 0.910 (95% confidence interval [CI]: 0.904-0.916) and 0.887 (95% CI: 0.867-0.909) in the internal and external testing datasets respectively, which were comparable to those of experienced physicians. Furthermore, the MMI system was utilized to rapidly differentiate between viral subtypes with a mean AUC of 0.822 (95% CI: 0.805-0.837) and bacterial subtypes with a mean AUC of 0.803 (95% CI: 0.775-0.830). Here, the MMI system harbors the potential to guide tailored medication recommendations, thus mitigating the risk of antibiotic misuse. Additionally, the integration of multimodal factors in the AI-driven system also provided an evident advantage in predicting risks of developing critical illness, contributing to more informed clinical decision-making. To revolutionize medical care, embracing multimodal AI tools in pulmonary infections will pave the way to further facilitate early intervention and precise management in the foreseeable future.

A PET/CT radiomics model for predicting distant metastasis in early-stage non-small cell lung cancer patients treated with stereotactic body radiotherapy: a multicentric study

OBJECTIVES

Stereotactic body radiotherapy (SBRT) is a treatment option for patients with early-stage non-small cell lung cancer (NSCLC) who are unfit for surgery. Some patients may experience distant metastasis. This study aimed to develop and validate a radiomics model for predicting distant metastasis in patients with early-stage NSCLC treated with SBRT.

METHODS

Patients at five institutions were enrolled in this study. Radiomics features were extracted based on the PET/CT images. After feature selection in the training set (from Tianjin), CT-based and PET-based radiomics signatures were built. Models based on CT and PET signatures were built and validated using external datasets (from Zhejiang, Zhengzhou, Shandong, and Shanghai). An integrated model that included CT and PET radiomic signatures was developed. The performance of the proposed model was evaluated in terms of its discrimination, calibration, and clinical utility. Multivariate logistic regression was used to calculate the probability of distant metastases. The cutoff value was obtained using the receiver operator characteristic curve (ROC), and the patients were divided into high- and low-risk groups. Kaplan-Meier analysis was used to evaluate the distant metastasis-free survival (DMFS) of different risk groups.

RESULTS

In total, 228 patients were enrolled. The median follow-up time was 31.4 (2.0-111.4) months. The model based on CT radiomics signatures had an area under the curve (AUC) of 0.819 in the training set (n = 139) and 0.786 in the external dataset (n = 89). The PET radiomics model had an AUC of 0.763 for the training set and 0.804 for the external dataset. The model combining CT and PET radiomics had an AUC of 0.835 for the training set and 0.819 for the external dataset. The combined model showed a moderate calibration and a positive net benefit. When the probability of distant metastasis was greater than 0.19, the patient was considered to be at high risk. The DMFS of patients with high- and low-risk was significantly stratified (P < 0.001).

CONCLUSIONS

The proposed PET/CT radiomics model can be used to predict distant metastasis in patients with early-stage NSCLC treated with SBRT and provide a reference for clinical decision-making. In this study, the model was established by combining CT and PET radiomics signatures in a moderate-quantity training cohort of early-stage NSCLC patients treated with SBRT and was successfully validated in independent cohorts. Physicians could use this easy-to-use model to assess the risk of distant metastasis after SBRT. Identifying subgroups of patients with different risk factors for distant metastasis is useful for guiding personalized treatment approaches.

Potential of CDC25 phosphatases in cancer research and treatment: key to precision medicine

The global burden of cancer continues to rise, underscoring the urgency of developing more effective and precisely targeted therapies. This comprehensive review explores the confluence of precision medicine and CDC25 phosphatases in the context of cancer research. Precision medicine, alternatively referred to as customized medicine, aims to customize medical interventions by taking into account the genetic, genomic, and epigenetic characteristics of individual patients. The identification of particular genetic and molecular drivers driving cancer helps both diagnostic accuracy and treatment selection. Precision medicine utilizes sophisticated technology such as genome sequencing and bioinformatics to elucidate genetic differences that underlie the proliferation of cancer cells, hence facilitating the development of customized therapeutic interventions. CDC25 phosphatases, which play a crucial role in governing the progression of the cell cycle, have garnered significant attention as potential targets for cancer treatment. The dysregulation of CDC25 is a characteristic feature observed in various types of malignancies, hence classifying them as proto-oncogenes. The proteins in question, which operate as phosphatases, play a role in the activation of Cyclin-dependent kinases (CDKs), so promoting the advancement of the cell cycle. CDC25 inhibitors demonstrate potential as therapeutic drugs for cancer treatment by specifically blocking the activity of CDKs and modulating the cell cycle in malignant cells. In brief, precision medicine presents a potentially fruitful option for augmenting cancer research, diagnosis, and treatment, with an emphasis on individualized care predicated upon patients' genetic and molecular profiles. The review highlights the significance of CDC25 phosphatases in the advancement of cancer and identifies them as promising candidates for therapeutic intervention. This statement underscores the significance of doing thorough molecular profiling in order to uncover the complex molecular characteristics of cancer cells.

The long-term health outcomes, pathophysiological mechanisms and multidisciplinary management of long COVID

There have been hundreds of millions of cases of coronavirus disease 2019 (COVID-19), which is caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). With the growing population of recovered patients, it is crucial to understand the long-term consequences of the disease and management strategies. Although COVID-19 was initially considered an acute respiratory illness, recent evidence suggests that manifestations including but not limited to those of the cardiovascular, respiratory, neuropsychiatric, gastrointestinal, reproductive, and musculoskeletal systems may persist long after the acute phase. These persistent manifestations, also referred to as long COVID, could impact all patients with COVID-19 across the full spectrum of illness severity. Herein, we comprehensively review the current literature on long COVID, highlighting its epidemiological understanding, the impact of vaccinations, organ-specific sequelae, pathophysiological mechanisms, and multidisciplinary management strategies. In addition, the impact of psychological and psychosomatic factors is also underscored. Despite these crucial findings on long COVID, the current diagnostic and therapeutic strategies based on previous experience and pilot studies remain inadequate, and well-designed clinical trials should be prioritized to validate existing hypotheses. Thus, we propose the primary challenges concerning biological knowledge gaps and efficient remedies as well as discuss the corresponding recommendations.

CT radiomics for noninvasively predicting NQO1 expression levels in hepatocellular carcinoma

Using noninvasive radiomics to predict pathological biomarkers is an innovative work worthy of exploration. This retrospective cohort study aimed to analyze the correlation between NAD(P)H quinone oxidoreductase 1 (NQO1) expression levels and the prognosis of patients with hepatocellular carcinoma (HCC) and to construct radiomic models to predict the expression levels of NQO1 prior to surgery. Data of patients with HCC from The Cancer Genome Atlas (TCGA) and the corresponding arterial phase-enhanced CT images from The Cancer Imaging Archive were obtained for prognosis analysis, radiomic feature extraction, and model development. In total, 286 patients with HCC from TCGA were included. According to the cut-off value calculated using R, patients were divided into high-expression (n = 143) and low-expression groups (n = 143). Kaplan-Meier survival analysis showed that higher NQO1 expression levels were significantly associated with worse prognosis in patients with HCC (p = 0.017). Further multivariate analysis confirmed that high NQO1 expression was an independent risk factor for poor prognosis (HR = 1.761, 95% CI: 1.136-2.73, p = 0.011). Based on the arterial phase-enhanced CT images, six radiomic features were extracted, and a new bi-regional radiomics model was established, which could noninvasively predict higher NQO1 expression with good performance. The area under the curve (AUC) was 0.9079 (95% CI 0.8127-1.0000). The accuracy, sensitivity, and specificity were 0.86, 0.88, and 0.84, respectively, with a threshold value of 0.404. The data verification of our center showed that this model has good predictive efficiency, with an AUC of 0.8791 (95% CI 0.6979-1.0000). In conclusion, there existed a significant correlation between the CT image features and the expression level of NQO1, which could indirectly reflect the prognosis of patients with HCC. The predictive model based on arterial phase CT imaging features has good stability and diagnostic efficiency and is a potential means of identifying the expression level of NQO1 in HCC tissues before surgery.

Novel tools for early diagnosis and precision treatment based on artificial intelligence

Lung cancer has the highest mortality rate among all cancers in the world. Hence, early diagnosis and personalized treatment plans are crucial to improving its 5-year survival rate. Chest computed tomography (CT) serves as an essential tool for lung cancer screening, and pathology images are the gold standard for lung cancer diagnosis. However, medical image evaluation relies on manual labor and suffers from missed diagnosis or misdiagnosis, and physician heterogeneity. The rapid development of artificial intelligence (AI) has brought a whole novel opportunity for medical task processing, demonstrating the potential for clinical application in lung cancer diagnosis and treatment. AI technologies, including machine learning and deep learning, have been deployed extensively for lung nodule detection, benign and malignant classification, and subtype identification based on CT images. Furthermore, AI plays a role in the non-invasive prediction of genetic mutations and molecular status to provide the optimal treatment regimen, and applies to the assessment of therapeutic efficacy and prognosis of lung cancer patients, enabling precision medicine to become a reality. Meanwhile, histology-based AI models assist pathologists in typing, molecular characterization, and prognosis prediction to enhance the efficiency of diagnosis and treatment. However, the leap to extensive clinical application still faces various challenges, such as data sharing, standardized label acquisition, clinical application regulation, and multimodal integration. Nevertheless, AI holds promising potential in the field of lung cancer to improve cancer care.

Persistent increase and improved survival of stage I lung cancer based on a large-scale real-world sample of 26,226 cases

BACKGROUND

Lung cancer prevails and induces high mortality around the world. This study provided real-world information on the evolution of clinicopathological profiles and survival outcomes of lung cancer, and provided survival information within stage I subtypes.

METHODS

Patients pathologically confirmed with lung cancer between January 2009 and December 2018 were identified with complete clinicopathological information, molecular testing results, and follow-up data. Shifts in clinical characteristics were evaluated using χ2 tests. Overall survival (OS) was calculated through the Kaplan-Meier method.

RESULTS

A total of 26,226 eligible lung cancer patients were included, among whom 62.55% were male and 52.89% were smokers. Non-smokers and elderly patients took increasingly larger proportions in the whole patient population. The proportion of adenocarcinoma increased from 51.63% to 71.80%, while that of squamous carcinoma decreased from 28.43% to 17.60%. Gene mutations including EGFR (52.14%), KRAS (12.14%), and ALK (8.12%) were observed. Female, younger, non-smoking, adenocarcinoma patients and those with mutated EGFR had better survival prognoses. Importantly, this study validated that early detection of early-stage lung cancer patients had contributed to pronounced survival benefits during the decade. Patients with stage I lung cancer, accounted for an increasingly considerable proportion, increasing from 15.28% to 40.25%, coinciding with the surgery rate increasing from 38.14% to 54.25%. Overall, period survival analyses found that 42.69% of patients survived 5 years, and stage I patients had a 5-year OS of 84.20%. Compared with that in 2009-2013, the prognosis of stage I patients in 2014-2018 was dramatically better, with 5-year OS increasing from 73.26% to 87.68%. Regarding the specific survival benefits among stage I patients, the 5-year survival rates were 95.28%, 93.25%, 82.08%, and 74.50% for stage IA1, IA2, IA3, and IB, respectively, far more promising than previous reports.

CONCLUSIONS

Crucial clinical and pathological changes have been observed in the past decade. Notably, the increased incidence of stage I lung cancer coincided with an improved prognosis, indicating actual benefits of early detection and management of lung cancer.

Developing a weakly supervised deep learning framework for breast cancer diagnosis with HR status based on mammography images

The status of hormone receptors (HR) at the molecular level is crucial for accurate diagnosis and effective treatment of breast cancer. Meanwhile, mammography is an effective screening method for detecting breast cancer, which significantly improve survival. However, diagnosing the molecular status of breast cancer involves a pathological biopsy, which can affect the accuracy of the diagnosis. To non-invasively diagnose the hormone receptor (HR) status of breast cancer and reduced manual annotation, we proposed a weakly supervised deep learning framework BSNet which detected breast cancer with HR status and benign tumors. BSNet was trained on 2321 multi-view mammography cases from female undergoing digital mammography for the general population at Harbin Medical University Cancer Hospital in Heilongjiang Province during the period 2017-2018 and was validated on the external cohort. The average AUCs of BSNet on the test set and the external validation set were 0.89 and 0.92, respectively. BSNet demonstrated excellent performance in non-invasive breast cancer diagnosis with HR status, using multiple mammography views without pixel annotation. Furthermore, we developed a web server (http://bsnet.edbc.org) for easy use. BSNet described high-dimensional mammography of breast cancer subtypes, which helped inform early management options.

Identification and validation of m6A-GPI signatures as a novel prognostic model for colorectal cancer

In order to develop an N6-methyladenosine-related gene prognostic index (m6A-GPI) that can predict the prognosis in colorectal cancer (CRC), we obtained m6A-related differentially expressed genes (DEGs) based on The Cancer Genome Atlas (TCGA) and m6Avar database, seven genes were screened by weighted gene co-expression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) analysis. Then, m6A-GPI was constructed based on the risk score. Survival analysis indicated that patients in the lower m6A-GPI group have more prolonged disease-free survival (DFS), and different clinical characteristic groups (tumor site and stage) also showed differential risk scores. In the analysis of the molecular characteristics, the risk score is positively associated with homologous recombination defects (HRD), copy number alterations (CNA), and the mRNA expression-based stemness index (mRNAsi). In addition, m6A-GPI also plays an essential role in tumor immune cell infiltration. The immune cell infiltration in the low m6A-GPI group is significantly higher in CRC. Moreover, we found that CIITA, one of the genes in m6A-GPI was up-regulated in CRC tissues based on real-time RT-PCR and Western blot. m6A-GPI is a promising prognostic biomarker that can be used to distinguish the prognosis of CRC patients in CRC.

Hepatocellular Carcinomas with Concomitant Mutations of TERT, TP53, and CTNNB1: Is There a Role for Artificial Intelligence?

TP53, CTNNB1, and TERT-promoter mutations are the most common driver mutations in hepatocellular carcinoma (HCC). The morphological and genetical HCC heterogeneities are difficult to discriminate by the eye of the pathologist. Here, we describe two rare cases of HCC with simultaneous co-mutation of all three of genes, which represent a poorly described occurrence in the literature. In these two cases, areas with different tumor grade and different β-catenin and Glutamine Synthetase expression (performed by automated immunohistochemistry) were observed. NGS analysis was performed in these different areas, to check for potential diversity of mutation burden on the different regions, but no differences were found: all micro-areas analyzed showed the co-presence of mutations in TP53, CTNNB1, and TERT. The evidence that all mutations were found in all the different areas analyzed by NGS leads to hypothesize that the tumor is not composed of different clones harboring different mutations. All the variants are harbored by the same neoplastic clone, albeit leading to different phenotypes. Mutation prediction Artificial Intelligence models could help the morpho-genetic classification of HCC in the future, since they can find variabilities not obvious to the human eye, with increased sensitivity, specificity and reproducibility.