PLEK2 and IFI6, representing mesenchymal and immune-suppressive microenvironment, predicts resistance to neoadjuvant immunotherapy in esophageal squamous cell carcinoma.

BACKGROUND: Immunotherapy has largely improved clinical outcome of patients with esophageal squamous cell carcinoma (ESCC). However, a proportion of patients still fail to benefit. Thus, biomarkers predicting therapeutic resistance and underlying mechanism needs to be investigated. METHODS: Transcriptomic profiling was applied in FFPE tissues from 103 ESCC patients, including surgical samples from 66 treatment-naïve patients with long-term follow-up, and endoscopic biopsies from 37 local advanced ESCC cases receiving neoadjuvant immunotherapy plus chemotherapy. Unsupervised clustering indicated an aggressive phenotype with mesenchymal character in 66 treatment-naïve samples. Univariant logistic regression was applied to identify candidate biomarkers potentially predicted resistance to neoadjuvant immunotherapy within the range of mesenchymal phenotype enriched genes. These biomarkers were further validated by immunohistochemistry. Putative mechanisms mediating immunotherapy resistance, as indicated by microenvironment and immune cell infiltration, were evaluated by transcriptomic data, and validated by multiplex immunofluorescence. RESULTS: PLEK2 and IFI6, highly expressed in mesenchymal phenotype, were identified as novel biomarkers relating to non-MPR in neoadjuvant immunotherapy cohort [PLEK2high, OR (95% CI): 2.15 (1.07-4.33), P = 0.032; IFI6high, OR (95% CI): 2.21 (1.16-4.23), P = 0.016). PLEK2high and IFI6 high ESCC patients (versus low expressed patients) further exhibit higher chance of non-major pathological remissions (90%, P = 0.004) in neoadjuvant immunotherapy cohort and high mortality (78.9%, P = 0.05), poor prognosis in retrospective cohort. PLEK2high/IFI6high ESCC recapitulated mesenchymal phenotype, characterized by extracellular matrix composition and matrix remodeling. In addition, PLEK2high or IFI6high ESCC displayed an immune-unfavored microenvironment, represented by positive correlating with regulatory T cells, Helper 2 T cell as well as less infiltration of B cells, effector T cells and mast cells. CONCLUSIONS: PLEK2 and IFI6 was discovered of first time to identify a distinct ESCC subpopulation cannot be benefited from neoadjuvant immunotherapy and present a poor survival, which putatively associated with mesenchymal and immune-suppressive microenvironment.

Radiomics Nomogram with Added Nodal Features Improves Treatment Response Prediction in Locally Advanced Esophageal Squamous Cell Carcinoma: A Multicenter Study.

OBJECTIVE: We aimed to develop and validate a radiomics nomogram and determine the value of radiomic features from lymph nodes (LNs) for predicting pathological complete response (pCR) to neoadjuvant chemoradiotherapy (NCRT) in patients with locally advanced esophageal squamous cell carcinoma (ESCC). METHODS: In this multicenter retrospective study, eligible participants who had undergone NCRT followed by radical esophagectomy were consecutively recruited. Three radiomics models (modelT, modelLN, and modelTLN) based on tumor and LN features, alone and combined, were developed in the training cohort. The radiomics nomogram was developed by incorporating the prediction value of the radiomics model and clinicoradiological risk factors using multivariate logistic regression, and was evaluated using the receiver operating characteristic curve, validated in two external validation cohorts. RESULTS: Between October 2011 and December 2018, 116 patients were included in the training cohort. Between June 2015 and October 2020, 51 and 27 patients from two independent hospitals were included in validation cohorts 1 and 2, respectively. The radiomics modelTLN performed better than the radiomics modelT for predicting pCR. The radiomics nomogram incorporating the predictive value of the radiomics modelTLN and heterogeneous after NCRT outperformed the clinicoradiological model, with an area under the curve (95% confidence interval) of 0.833 (0.765-0.894) versus 0.764 (0.686-0.833) [p = 0.088, DeLong test], 0.824 (0.718-0.909) versus 0.692 (0.554-0.809) [p = 0.012], and 0.902 (0.794-0.984) versus 0.696 (0.526-0.857) [p = 0.024] in all three cohorts. CONCLUSIONS: Radiomic features from LNs could provide additional value for predicting pCR in ESCC patients, and the radiomics nomogram provided an accurate prediction of pCR, which might aid treatment decision.

Radiomics-Based Preoperative Prediction of Lymph Node Metastasis in Intrahepatic Cholangiocarcinoma Using Contrast-Enhanced Computed Tomography.

BACKGROUND: Lymph node (LN) metastasis is significantly associated with worse prognosis for patients with intrahepatic cholangiocarcinoma (ICC). Improvement in preoperative assessment on LN metastasis helps in treatment decision-making. We aimed to investigate the role of radiomics-based method in predicting LN metastasis for patients with ICC. METHODS: A total of 296 patients with ICC who underwent curative-intent hepatectomy and lymphadenectomy at two centers in China were analyzed. Radiomic features, including histogram- and wavelet-based features, shape and size features, and texture features were extracted from four-phase computerized tomography (CT) images. The clinical and conventional radiological variables which were independently associated with LN metastasis were also identified. A combined nomogram predicting LN metastasis was developed, and its performance was determined by discrimination, calibration, and stratification of long-term prognosis. The results were validated by the internal and external validation cohorts. RESULTS: Twenty-four radiomic features were selected into the nomogram. The established nomogram demonstrated good discrimination and calibration, with areas under the curve (AUCs) of 0.98 [95% confidence interval (CI) 0.96-0.99], 0.93 (0.88-0.98), and 0.89 (0.81-0.96) in the training and two validation cohorts, respectively. The 5-year overall survival (OS) and recurrence-free survival (RFS) rates of patients with high risk of LN metastasis as grouped by nomogram were poorer than those of patients with low risk in the training cohort (OS 28.8% versus 53.9%, p < 0.001; RFS 26.3% versus 44.2%, p = 0.001). Similar results were observed in the two validation cohorts. CONCLUSIONS: Radiomics-based method provided accurate prediction of LN metastasis and prognostic assessment for ICC patients, and might aid the preoperative surgical decision.

CT-based radiomics to predict development of macrovascular invasion in hepatocellular carcinoma: A multicenter study.

BACKGROUND: Macrovascular invasion (MaVI) occurs in nearly half of hepatocellular carcinoma (HCC) patients at diagnosis or during follow-up, which causes severe disease deterioration, and limits the possibility of surgical approaches. This study aimed to investigate whether computed tomography (CT)-based radiomics analysis could help predict development of MaVI in HCC. METHODS: A cohort of 226 patients diagnosed with HCC was enrolled from 5 hospitals with complete MaVI and prognosis follow-ups. CT-based radiomics signature was built via multi-strategy machine learning methods. Afterwards, MaVI-related clinical factors and radiomics signature were integrated to construct the final prediction model (CRIM, clinical-radiomics integrated model) via random forest modeling. Cox-regression analysis was used to select independent risk factors to predict the time of MaVI development. Kaplan-Meier analysis was conducted to stratify patients according to the time of MaVI development, progression-free survival (PFS), and overall survival (OS) based on the selected risk factors. RESULTS: The radiomics signature showed significant improvement for MaVI prediction compared with conventional clinical/radiological predictors (P < 0.001). CRIM could predict MaVI with satisfactory areas under the curve (AUC) of 0.986 and 0.979 in the training (n = 154) and external validation (n = 72) datasets, respectively. CRIM presented with excellent generalization with AUC of 0.956, 1.000, and 1.000 in each external cohort that accepted disparate CT scanning protocol/manufactory. Peel9_fos_InterquartileRange [hazard ratio (HR) = 1.98; P < 0.001] was selected as the independent risk factor. The cox-regression model successfully stratified patients into the high-risk and low-risk groups regarding the time of MaVI development (P < 0.001), PFS (P < 0.001) and OS (P = 0.002). CONCLUSIONS: The CT-based quantitative radiomics analysis could enable high accuracy prediction of subsequent MaVI development in HCC with prognostic implications.

Multiparametric MRI-Based Radiomics for Prostate Cancer Screening With PSA in 4-10 ng/mL to Reduce Unnecessary Biopsies.

BACKGROUND: Whether men with a prostate-specific antigen (PSA) level of 4-10 ng/mL should be recommended for a biopsy is clinically challenging. PURPOSE: To develop and validate a radiomics model based on multiparametric MRI (mp-MRI) in patients with PSA levels of 4-10 ng/mL to predict prostate cancer (PCa) preoperatively and reduce unnecessary biopsies. STUDY TYPE: Retrospective. SUBJECTS: In all, 199 patients with PSA levels of 4-10 ng/mL. FIELD STRENGTH/SEQUENCE: 3T, T2 -weighted, diffusion-weighted, and dynamic contrast-enhanced MRI. ASSESSMENT: Lesion regions of interest (ROIs) from T2 -weighted, diffusion-weighted, and dynamic contrast-enhanced MRI were annotated by two radiologists. A total of 2104 radiomic features were extracted from the ROI of each patient. A random forest classifier was used to build the radiomics model for PCa in the primary cohort. A combined model was constructed using multivariate logistic regression by incorporating the radiomics signature and clinical-radiological risk factors. STATISTICAL TESTS: For continuous variables, variance equality was assessed by Levene's test and Student's t-test, and Welch's t-test was used to assess between-group differences. For categorical variables, Pearson's chi-square test, Fisher's exact test, or the approximate chi-square test was used to assess between-group differences. P < 0.05 was considered statistically significant. RESULTS: The combined model incorporating the multi-imaging fusion model, age, PSA density (PSAD), and the PI-RADS v2 score yielded area under the curve (AUC) values of 0.956 and 0.933 on the primary (n = 133) and validation (n = 66) cohorts, respectively. Compared with the clinical-radiological model, the combined model performed better on both the primary and validation cohorts (P < 0.05). Furthermore, the use of the combined model to predict PCa could identify more negative PCa patients than the use of the clinical-radiological model by 18.4%. DATA CONCLUSION: The combined model was developed and validated to provide potential preoperative prediction of PCa in men with PSA levels of 4-10 ng/mL and might aid in treatment decision-making and reduce unnecessary biopsies. LEVEL OF EVIDENCE: 3 Technical Efficacy Stage: 3 J. Magn. Reson. Imaging 2020;51:1890-1899.

A radiomics nomogram may improve the prediction of IDH genotype for astrocytoma before surgery.

OBJECTIVES: To develop and validate a radiomics nomogram to preoperative prediction of isocitrate dehydrogenase (IDH) genotype for astrocytomas, which might contribute to the pretreatment decision-making and prognosis evaluating. METHODS: One hundred five astrocytomas (Grades II-IV) with contrast-enhanced T1-weighted imaging (CE-T1WI), T2 fluid-attenuated inversion recovery (T2FLAIR), and apparent diffusion coefficient (ADC) map were enrolled in this study (training cohort: n = 74; validation cohort: n = 31). IDH1/2 genotypes were determined using Sanger sequencing. A total of 3882 radiomics features were extracted. Support vector machine algorithm was used to build the radiomics signature on the training cohort. Incorporating radiomics signature and clinico-radiological risk factors, the radiomics nomogram was developed. Receiver operating characteristic (ROC) curve and area under the curve (AUC) were used to assess these models. Kaplan-Meier survival analysis and log rank test were performed to assess the prognostic value of the radiomics nomogram. RESULTS: The radiomics signature was built by six selected radiomics features and yielded AUC values of 0.901 and 0.888 in the training and validation cohorts. The radiomics nomogram based on the radiomics signature and age performed better than the clinico-radiological model (training cohort, AUC = 0.913 and 0.817; validation cohort, AUC = 0.900 and 0.804). Additionally, the survival analysis showed that prognostic values of the radiomics nomogram and IDH genotype were similar (log rank test, p < 0.001; C-index = 0.762 and 0.687; z-score test, p = 0.062). CONCLUSIONS: The radiomics nomogram might be a useful supporting tool for the preoperative prediction of IDH genotype for astrocytoma, which could aid pretreatment decision-making. KEY POINTS: • The radiomics signature based on multiparametric and multiregional MRI images could predict IDH genotype of Grades II-IV astrocytomas. • The radiomics nomogram performed better than the clinico-radiological model, and it might be an easy-to-use supporting tool for IDH genotype prediction. • The prognostic value of the radiomics nomogram was similar with that of the IDH genotype, which might contribute to prognosis evaluating.

Preoperative prediction of cavernous sinus invasion by pituitary adenomas using a radiomics method based on magnetic resonance images.

OBJECTIVES: To predict cavernous sinus (CS) invasion by pituitary adenomas (PAs) pre-operatively using a radiomics method based on contrast-enhanced T1 (CE-T1) and T2-weighted magnetic resonance (MR) imaging. METHODS: A total of 194 patients with Knosp grade two and three PAs (training set: n = 97; test set: n = 97) were enrolled in this retrospective study. From CE-T1 and T2 MR images, 2553 quantitative imaging features were extracted. To select the most informative features, least absolute shrinkage and selection operator (LASSO) was performed. Subsequently, a linear support vector machine (SVM) was used to fit the predictive model. Furthermore, a nomogram was constructed by incorporating clinico-radiological risk factors and radiomics signature, and the clinical usefulness of the nomogram was validated using decision curve analysis (DCA). RESULTS: Three imaging features were selected in the training set, based on which the radiomics model yielded area under the curve (AUC) values of 0.852 and 0.826 for the training and test sets. The nomogram based on the radiomics signature and the clinico-radiological risk factors yielded an AUC of 0.899 in the training set and 0.871 in the test set. CONCLUSIONS: The nomogram developed in this study might aid neurosurgeons in the pre-operative prediction of CS invasion by Knosp grade two and three PAs, which might contribute to creating surgical strategies. KEY POINTS: • Pre-operative diagnosis of CS invasion by PAs might affect creating surgical strategies • MRI might help for diagnosis of CS invasion by PAs before surgery • Radiomics might improve the CS invasion detection by MR images.

A multi-sequence and habitat-based MRI radiomics signature for preoperative prediction of MGMT promoter methylation in astrocytomas with prognostic implication.

OBJECTIVES: Oxygen 6-methylguanine-DNA methyltransferase (MGMT) promoter methylation is a significant prognostic biomarker in astrocytomas, especially for temozolomide (TMZ) chemotherapy. This study aimed to preoperatively predict MGMT methylation status based on magnetic resonance imaging (MRI) radiomics and validate its value for evaluation of TMZ chemotherapy effect. METHODS: We retrospectively reviewed a cohort of 105 patients with grade II-IV astrocytomas. Radiomic features were extracted from the tumour and peritumoral oedema habitats on contrast-enhanced T1-weighted images, T2-weighted fluid-attenuated inversion recovery images and apparent diffusion coefficient (ADC) maps. The following radiomics analysis was structured in three phases: feature reduction, signature construction and discrimination statistics. A fusion radiomics signature was finally developed using logistic regression modelling. Predictive performance was compared between the radiomics signature, previously reported clinical factors and ADC parameters. Validation was additionally performed on a time-independent cohort (n = 31). The prognostic value of the signature on overall survival for TMZ chemotherapy was explored using Kaplan Meier estimation. RESULTS: The fusion radiomics signature exhibited supreme power for predicting MGMT promoter methylation, with area under the curve values of 0.925 in the training cohort and 0.902 in the validation cohort. Performance of the radiomics signature surpassed that of clinical factors and ADC parameters. Moreover, the radiomics approach successfully divided patients into high-risk and low-risk groups for overall survival after TMZ chemotherapy (p = 0.03). CONCLUSIONS: The proposed radiomics signature accurately predicted MGMT promoter methylation in patients with astrocytomas, and achieved survival stratification for TMZ chemotherapy, thus providing a preoperative basis for individualised treatment planning. KEY POINTS: • Radiomics using magnetic resonance imaging can preoperatively perform satisfactory prediction of MGMT methylation in grade II-IV astrocytomas. • Habitat-based radiomics can improve efficacy in predicting MGMT methylation status. • Multi-sequence radiomics signature has the power to evaluate TMZ chemotherapy effect.

Non-invasive genotype prediction of chromosome 1p/19q co-deletion by development and validation of an MRI-based radiomics signature in lower-grade gliomas.

PURPOSE: To perform radiomics analysis for non-invasively predicting chromosome 1p/19q co-deletion in World Health Organization grade II and III (lower-grade) gliomas. METHODS: This retrospective study included 277 patients histopathologically diagnosed with lower-grade glioma. Clinical parameters were recorded for each patient. We performed a radiomics analysis by extracting 647 MRI-based features and applied the random forest algorithm to generate a radiomics signature for predicting 1p/19q co-deletion in the training cohort (n = 184). The clinical model consisted of pertinent clinical factors, and was built using a logistic regression algorithm. A combined model, incorporating both the radiomics signature and related clinical factors, was also constructed. The receiver operating characteristics curve was used to evaluate the predictive performance. We further validated the predictability of the three developed models using a time-independent validation cohort (n = 93). RESULTS: The radiomics signature was constructed as an independent predictor for differentiating 1p/19q co-deletion genotypes, which demonstrated superior performance on both the training and validation cohorts with areas under curve (AUCs) of 0.887 and 0.760, respectively. These results outperformed the clinical model (AUCs of 0.580 and 0.627 on training and validation cohorts). The AUCs of the combined model were 0.885 and 0.753 on training and validation cohorts, respectively, which indicated that clinical factors did not present additional improvement for the prediction. CONCLUSION: Our study highlighted that an MRI-based radiomics signature can effectively identify the 1p/19q co-deletion in histopathologically diagnosed lower-grade gliomas, thereby offering the potential to facilitate non-invasive molecular subtype prediction of gliomas.

Non-invasive radiomics approach potentially predicts non-functioning pituitary adenomas subtypes before surgery.

PURPOSE: To make individualised preoperative prediction of non-functioning pituitary adenoma (NFPAs) subtypes between null cell adenomas (NCAs) and other subtypes using a radiomics approach. METHODS: We enrolled 112 patients (training set: n = 75; test set: n = 37) with complete T1-weighted magnetic resonance imaging (MRI) and contrast-enhanced T1-weighted MRI (CE-T1). A total of 1482 quantitative imaging features were extracted from T1 and CE-T1 images. Support vector machine trained a predictive model that was validated using a receiver operating characteristics (ROC) analysis on an independent test set. Moreover, a nomogram was constructed incorporating clinical characteristics and the radiomics signature for individual prediction. RESULTS: T1 image features yielded area under the curve (AUC) values of 0.8314 and 0.8042 for the training and test sets, respectively, while CE-T1 image features provided no additional contribution to the predictive model. The nomogram incorporating sex and the T1 radiomics signature yielded good calibration in the training and test sets (concordance index (CI) = 0.854 and 0.857, respectively). CONCLUSION: This study focused on the preoperative prediction of NFPA subtypes between NCAs and others using a radiomics approach. The developed model yielded good performance, indicating that radiomics had good potential for the preoperative diagnosis of NFPAs. KEY POINTS: • MRI may help in the pre-operative diagnosis of NFPAs subtypes • Retrospective study showed T1-weighted MRI more useful than CE-T1 in NCAs diagnosis • Treatment decision making becomes more individualised • Radiomics approach had potential for classification of NFPAs.

Systematic Investigation of High-Sensitivity Luminescent Sensing for Polyoxometalates and Iron(III) by MOFs Assembled with a New Resorcin[4]arene-Functionalized Tetracarboxylate.

A new family of resorcin[4]arene-based metal-organic frameworks (MOFs), namely, [Eu(HL)(DMF)(H2 O)2 ]⋅3 H2 O (1), [Tb(HL)(DMF)(H2 O)2 ] 3 H2 O (2), [Cd4 (L)2 (DMF)4 (H2 O)2 ] 3 H2 O (3) and [Zn3 (HL)2 (H2 O)2 ] 2 DMF⋅7 H2 O (4), have been constructed from a new resorcin[4]arene-functionalized tetracarboxylic acid (H4 L=2,8,14,20-tetra-ethyl-6,12,18,24-tetra-methoxy-4,10,16,22-tetra-carboxy-methoxy-calix[4]arene). Isostructural 1 and 2 exhibit charming 1D motifs built with the cup-like HL(3-) anions and rare earth cations. Compounds 3 and 4 show a unique sandwich-based 2D layer and a fascinating 3D framework, respectively. Remarkably, compounds 1 and 2 display intensive red and green emissions triggered by the efficient antenna effect of organic ligands under UV light. More importantly, systematic luminescence studies demonstrate that Ln-MOFs 1 and 2, as efficient multifunctional fluorescent materials, show highly selective and sensitive sensing of Fe(3+) , polyoxometalates (POMs), and acetone, which represents a rare example of a sensor for quantitatively detecting three different types of analytes. This is also an exceedingly rare example of Fe(3+) and POMs detection in aqueous solutions employing resorcin[4]arene-based luminescent Ln-MOFs. Furthermore, the possible mechanism of the sensing properties is deduced.

Biologically interpretable multi-task deep learning pipeline predicts molecular alterations, grade, and prognosis in glioma patients.

Deep learning models have been developed for various predictions in glioma; yet, they were constrained by manual segmentation, task-specific design, or a lack of biological interpretation. Herein, we aimed to develop an end-to-end multi-task deep learning (MDL) pipeline that can simultaneously predict molecular alterations and histological grade (auxiliary tasks), as well as prognosis (primary task) in gliomas. Further, we aimed to provide the biological mechanisms underlying the model's predictions. We collected multiscale data including baseline MRI images from 2776 glioma patients across two private (FAHZU and HPPH, n = 1931) and three public datasets (TCGA, n = 213; UCSF, n = 410; and EGD, n = 222). We trained and internally validated the MDL model using our private datasets, and externally validated it using the three public datasets. We used the model-predicted deep prognosis score (DPS) to stratify patients into low-DPS and high-DPS subtypes. Additionally, a radio-multiomics analysis was conducted to elucidate the biological basis of the DPS. In the external validation cohorts, the MDL model achieved average areas under the curve of 0.892-0.903, 0.710-0.894, and 0.850-0.879 for predicting IDH mutation status, 1p/19q co-deletion status, and tumor grade, respectively. Moreover, the MDL model yielded a C-index of 0.723 in the TCGA and 0.671 in the UCSF for the prediction of overall survival. The DPS exhibits significant correlations with activated oncogenic pathways, immune infiltration patterns, specific protein expression, DNA methylation, tumor mutation burden, and tumor-stroma ratio. Accordingly, our work presents an accurate and biologically meaningful tool for predicting molecular subtypes, tumor grade, and survival outcomes in gliomas, which provides personalized clinical decision-making in a global and non-invasive manner.

Deep Learning for Automatic Gross Tumor Volumes Contouring in Esophageal Cancer Based on Contrast-Enhanced Computed Tomography Images: A Multi-Institutional Study.

PURPOSE: To develop and externally validate an automatic artificial intelligence (AI) tool for delineating gross tumor volume (GTV) in patients with esophageal squamous cell carcinoma (ESCC), which can assist in neo-adjuvant or radical radiation therapy treatment planning. METHODS AND MATERIALS: In this multi-institutional study, contrast-enhanced CT images from 580 eligible ESCC patients were retrospectively collected. The GTV contours delineated by 2 experts via consensus were used as ground truth. A 3-dimensional deep learning model was developed for GTV contouring in the training cohort and internally and externally validated in 3 validation cohorts. The AI tool was compared against 12 board-certified experts in 25 patients randomly selected from the external validation cohort to evaluate its assistance in improving contouring performance and reducing variation. Contouring performance was measured using dice similarity coefficient (DSC) and average surface distance. Additionally, our previously established radiomics model for predicting pathologic complete response was used to compare AI-generated and ground truth contours, to assess the potential of the AI contouring tool in radiomics analysis. RESULTS: The AI tool demonstrated good GTV contouring performance in multicenter validation cohorts, with median DSC values of 0.865, 0.876, and 0.866 and median average surface distance values of 0.939, 0.789, and 0.875 mm, respectively. Furthermore, the AI tool significantly improved contouring performance for half of 12 board-certified experts (DSC values, 0.794-0.835 vs 0.856-0.881, P = .003-0.048), reduced the intra- and interobserver variations by 37.4% and 55.2%, respectively, and saved contouring time by 77.6%. In the radiomics analysis, 88.7% of radiomic features from ground truth and AI-generated contours demonstrated stable reproducibility, and similar pathologic complete response prediction performance for these contours (P = .430) was observed. CONCLUSIONS: Our AI contouring tool can improve GTV contouring performance and facilitate radiomics analysis in ESCC patients, which indicates its potential for GTV contouring during radiation therapy treatment planning and radiomics studies.

Development and validation of an integrated system for lung cancer screening and post-screening pulmonary nodules management: a proof-of-concept study (ASCEND-LUNG).

Background: In order to address the low compliance and dissatisfied specificity of low-dose computed tomography (LDCT), efficient and non-invasive approaches are needed to complement its limitations for lung cancer screening and management. The ASCEND-LUNG study is a prospective two-stage case-control study designed to evaluate the performance of a liquid biopsy-based comprehensive lung cancer screening and post-screening pulmonary nodules management system. Methods: We aimed to develop a comprehensive lung cancer system called Peking University Lung Cancer Screening and Management System (PKU-LCSMS) which comprises a lung cancer screening model to identify specific populations requiring LDCT and an artificial intelligence-aided (AI-aided) pulmonary nodules diagnostic model to classify pulmonary nodules following LDCT. A dataset of 465 participants (216 cancer, 47 benign, 202 non-cancer control) were used for the two models' development phase. For the lung cancer screening model development, cancer participants were randomly split at a ratio of 1:1 into the train and validation cohorts, and then non-cancer controls were age-matched to the cancer cases in a 1:1 ratio. Similarly, for the AI-aided pulmonary nodules model, cancer and benign participants were also randomly divided at a ratio of 2:1 into the train and validation cohorts. Subsequently, during the model validation phase, sensitivity and specificity were validated using an independent validation cohort consisting of 291 participants (140 cancer, 25 benign, 126 non-cancer control). Prospectively collected blood samples were analyzed for multi-omics including cell-free DNA (cfDNA) methylation, mutation, and serum protein. Computerized tomography (CT) images data was also obtained. Paired tissue samples were additionally analyzed for DNA methylation, DNA mutation, and messenger RNA (mRNA) expression to further explore the potential biological mechanisms. This study is registered with ClinicalTrials.gov, NCT04817046. Findings: Baseline blood samples were evaluated for the whole screening and diagnostic process. The cfDNA methylation-based lung cancer screening model exhibited the highest area under the curve (AUC) of 0.910 (95% CI, 0.869-0.950), followed by the protein model (0.891 [95% CI, 0.845-0.938]) and lastly the mutation model (0.577 [95% CI, 0.482-0.672]). Further, the final screening model, which incorporated cfDNA methylation and protein features, achieved an AUC of 0.963 (95% CI, 0.942-0.984). In the independent validation cohort, the multi-omics screening model showed a sensitivity of 99.2% (95% CI, 0.957-1.000) at a specificity of 56.3% (95% CI, 0.472-0.652). For the AI-aided pulmonary nodules diagnostic model, which incorporated cfDNA methylation and CT images features, it yielded a sensitivity of 81.1% (95% CI, 0.732-0.875), a specificity of 76.0% (95% CI, 0.549-0.906) in the independent validation cohort. Furthermore, four differentially methylated regions (DMRs) were shared in the lung cancer screening model and the AI-aided pulmonary nodules diagnostic model. Interpretation: We developed and validated a liquid biopsy-based comprehensive lung cancer screening and management system called PKU-LCSMS which combined a blood multi-omics based lung cancer screening model incorporating cfDNA methylation and protein features and an AI-aided pulmonary nodules diagnostic model integrating CT images and cfDNA methylation features in sequence to streamline the entire process of lung cancer screening and post-screening pulmonary nodules management. It might provide a promising applicable solution for lung cancer screening and management. Funding: This work was supported by Science, Science, Technology & Innovation Project of Xiongan New Area, Beijing Natural Science Foundation, CAMS Innovation Fund for Medical Sciences (CIFMS), Clinical Medicine Plus X-Young Scholars Project of Peking University, the Fundamental Research Funds for the Central Universities, Research Unit of Intelligence Diagnosis and Treatment in Early Non-small Cell Lung Cancer, Chinese Academy of Medical Sciences, National Natural Science Foundation of China, Peking University People's Hospital Research and Development Funds, National Key Research and Development Program of China, and the fundamental research funds for the central universities.

The Applications of Artificial Intelligence in Digestive System Neoplasms: A Review.

Importance: Digestive system neoplasms (DSNs) are the leading cause of cancer-related mortality with a 5-year survival rate of less than 20%. Subjective evaluation of medical images including endoscopic images, whole slide images, computed tomography images, and magnetic resonance images plays a vital role in the clinical practice of DSNs, but with limited performance and increased workload of radiologists or pathologists. The application of artificial intelligence (AI) in medical image analysis holds promise to augment the visual interpretation of medical images, which could not only automate the complicated evaluation process but also convert medical images into quantitative imaging features that associated with tumor heterogeneity. Highlights: We briefly introduce the methodology of AI for medical image analysis and then review its clinical applications including clinical auxiliary diagnosis, assessment of treatment response, and prognosis prediction on 4 typical DSNs including esophageal cancer, gastric cancer, colorectal cancer, and hepatocellular carcinoma. Conclusion: AI technology has great potential in supporting the clinical diagnosis and treatment decision-making of DSNs. Several technical issues should be overcome before its application into clinical practice of DSNs.