Development and Validation of a Radiomics Nomogram for Preoperative Prediction of Lymph Node Metastasis in Colorectal Cancer

Artificial intelligence in thyroid ultrasound

Artificial intelligence (AI), particularly deep learning (DL) algorithms, has demonstrated remarkable progress in image-recognition tasks, enabling the automatic quantitative assessment of complex medical images with increased accuracy and efficiency. AI is widely used and is becoming increasingly popular in the field of ultrasound. The rising incidence of thyroid cancer and the workload of physicians have driven the need to utilize AI to efficiently process thyroid ultrasound images. Therefore, leveraging AI in thyroid cancer ultrasound screening and diagnosis cannot only help radiologists achieve more accurate and efficient imaging diagnosis but also reduce their workload. In this paper, we aim to present a comprehensive overview of the technical knowledge of AI with a focus on traditional machine learning (ML) algorithms and DL algorithms. We will also discuss their clinical applications in the ultrasound imaging of thyroid diseases, particularly in differentiating between benign and malignant nodules and predicting cervical lymph node metastasis in thyroid cancer. Finally, we will conclude that AI technology holds great promise for improving the accuracy of thyroid disease ultrasound diagnosis and discuss the potential prospects of AI in this field.

Impact of flipped classroom model in increasing the achievement for medical students

BACKGROUND

Over the past few years, there has been a parallel development in the use of the internet and technology for teaching purposes. The Flipped classroom model (FCM) used by the instructor aims at spending more time interacting with students rather than lecturing them. There are very few studies about the effectiveness of FCM on student performance and perception as compared to the traditional lecture in colleges of medicine. This study evaluates the effectiveness of the FCM on the academic achievement of students in terms of increased performance and perception as compared to the traditional lecture the medical students in Al-Neelain University-Sudan.

METHOD

This case-control study compares using (FCM) in the medical students at Al-Neelain University and the traditional lecture and its effect on students' academic achievement. The students were randomly assigned into two groups (A & B), flipped classroom group A (30 students as a test), and traditional classroom group B (33students as control). Major outcome indexes were pretest and posttest results used for students' academic achievement performance assessment and a questionnaire used for student perception evaluation about the FCM. Finally, statistical analysis was performed using SPSS programs.

RESULTS

Although the pretest and posttest scores showed highly statistically differences within each group (A&B) with P<.000, when comparing the pretest and posttest scores of the studied groups showed that, there were no statistically significant differences between the pretest and posttest scores between them with P=0.912 and 0.100 respectively. However, more than 80% of participants were satisfied with using a flipped classroom. While more than 90% of students were more motivated to learn in flipped classrooms meeting learning targets when they used FCM.

CONCLUSION

There was a positive student perception towards using the FCM, despite no significant effect of FCM on medical students' academic achievement.

ARMO: automated and reliable multi-objective model for lymph node metastasis prediction in head and neck cancer

Objective. Accurate diagnosis of lymph node metastasis (LNM) is critical in treatment management for patients with head and neck cancer. Positron emission tomography and computed tomography are routinely used for identifying LNM status. However, for small or less fluorodeoxyglucose (FDG) avid nodes, there are always uncertainties in LNM diagnosis. We are aiming to develop a reliable prediction model is for identifying LNM.Approach. In this study, a new automated and reliable multi-objective learning model (ARMO) is proposed. In ARMO, a multi-objective model is introduced to obtain balanced sensitivity and specificity. Meanwhile, confidence is calibrated by introducing individual reliability, whilst the model uncertainty is estimated by a newly defined overall reliability in ARMO. In the training stage, a Pareto-optimal model set is generated. Then all the Pareto-optimal models are used, and a reliable fusion strategy that introduces individual reliability is developed for calibrating the confidence of each output. The overall reliability is calculated to estimate the model uncertainty for each test sample.Main results. The experimental results demonstrated that ARMO obtained more promising results, which the area under the curve, accuracy, sensitivity and specificity can achieve 0.97, 0.93, 0.88 and 0.94, respectively. Meanwhile, based on calibrated confidence and overall reliability, clinicians could pay particular attention to highly uncertain predictions.Significance. In this study, we developed a unified model that can achieve balanced prediction, confidence calibration and uncertainty estimation simultaneously. The experimental results demonstrated that ARMO can obtain accurate and reliable prediction performance.

A novel nomogram integrated with PDL1 and CEA to predict the prognosis of patients with gastric cancer

INTRODUCTION

This study aimed to develop a prognostic nomogram for patients with gastric cancer (GC) based on the levels of programmed death 1 ligand 1 (PDL1) and carcinoembryonic antigen (CEA).

METHODS

The nomogram was developed using data from a primary cohort of 247 patients who had been clinicopathologically diagnosed with GC, as well as a validation cohort of 63 patients. Furthermore, the nomogram divided the patients into three different risk groups for overall survival (OS)-the low-risk, middle-risk, and high-risk groups. Univariate and multivariate Cox hazard analyses were used to determine all of the factors included in the model. Decision curve analysis and receiver operating characteristic (ROC) curves were used to assess the accuracy of the nomogram.

RESULTS

The Kaplan-Meier survival analysis revealed that metastasis stage, clinical stage, and CEA and PDL1 levels were predictors for progress-free survival (PFS) and OS of patients with GC. Metastasis stage, clinical stage, and CEA and PDL1 levels were found to be independent risk factors for the PFS and OS of patients with GC in a multivariate analysis, and the nomogram was based on these factors. The concordance index of the nomogram was 0.763 [95% confidence interval (CI) 0.740-0.787]. The area under the concentration-time curve of the nomogram model was 0.81 (95% CI 0.780-0.900). According to the decision curve analysis and ROC curves, the nomogram model had a higher overall net efficiency in forecasting OS than clinical stage, CEA and PDL1 levels.

CONCLUSION

In conclusion, we proposed a novel nomogram that integrated PDL1 and CEA, and the proposed nomogram provided more accurate and useful prognostic predictions for patients with GC.

Prediction models for overall and cancer-specific survival in patients with metastatic early-onset colorectal cancer: a population-based study

PURPOSE

Metastatic early-onset colorectal cancer (EO-CRC) is on the rise, yet there is a dearth of predictive models for this disease. Therefore, it is crucial to develop a nomogram to aid in the early detection and management of metastatic colorectal cancer in young patients.

METHODS

We retrieved data from the SEER database on patients with metastatic colorectal cancer aged 50 or younger between 2010 and 2017. The data were randomly allocated in a 7:3 ratio to training and validation cohorts, and univariate and multivariate Cox regression analyses were used to identify independent prognostic factors for overall survival (OS) and cancer-specific survival (CSS) at 1, 3, and 5 years. The nomograms were developed based on these factors, and their discriminatory and calibration capabilities were validated. Using the nomogram risk scores, patients were stratified into low-risk and high-risk groups.

RESULTS

The study included 2470 patients with metastatic EO-CRC. Univariate and multivariate Cox regression analysis identified 12 independent risk factors that were included in the nomogram. The training cohort had a consistency index (C-index) of 0.71, while the validation cohort had a C-index of 0.70, demonstrating good predictive accuracy. Calibration plots showed a high level of consistency between the observed and predicted values, with overlapping plots along the diagonal. The decision curve analysis (DCA) revealed that the nomogram had a high clinical application value.

CONCLUSIONS

The novel nomograms were created to predict the prognosis of patients with metastatic EO-CRC, which can aid clinicians in developing more effective treatment strategies and contribute to more accurate prognostic assessments.

Automated Breast Ultrasound (ABUS)-based radiomics nomogram: an individualized tool for predicting axillary lymph node tumor burden in patients with early breast cancer

OBJECTIVES

Preoperative evaluation of axillary lymph node (ALN) status is an essential part of deciding the appropriate treatment. According to ACOSOG Z0011 trials, the new goal of the ALN status evaluation is tumor burden (low burden, < 3 positive ALNs; high burden, ≥ 3 positive ALNs), instead of metastasis or non-metastasis. We aimed to develop a radiomics nomogram integrating clinicopathologic features, ABUS imaging features and radiomics features from ABUS for predicting ALN tumor burden in early breast cancer.

METHODS

A total of 310 patients with breast cancer were enrolled. Radiomics score was generated from the ABUS images. Multivariate logistic regression analysis was used to develop the predicting model, we incorporated the radiomics score, ABUS imaging features and clinicopathologic features, and this was presented with a radiomics nomogram. Besides, we separately constructed an ABUS model to analyze the performance of ABUS imaging features in predicting ALN tumor burden. The performance of the models was assessed through discrimination, calibration curve, and decision curve.

RESULTS

The radiomics score, which consisted of 13 selected features, showed moderate discriminative ability (AUC 0.794 and 0.789 in the training and test sets). The ABUS model, comprising diameter, hyperechoic halo, and retraction phenomenon, showed moderate predictive ability (AUC 0.772 and 0.736 in the training and test sets). The ABUS radiomics nomogram, integrating radiomics score with retraction phenomenon and US-reported ALN status, showed an accurate agreement between ALN tumor burden and pathological verification (AUC 0.876 and 0.851 in the training and test sets). The decision curves showed that ABUS radiomics nomogram was clinically useful and more excellent than US-reported ALN status by experienced radiologists.

CONCLUSIONS

The ABUS radiomics nomogram, with non-invasive, individualized and precise assessment, may assist clinicians to determine the optimal treatment strategy and avoid overtreatment.

A nomogram model based on pre-treatment and post-treatment MR imaging radiomics signatures: application to predict progression-free survival for nasopharyngeal carcinoma

BACKGROUND

To establish a novel model using radiomics analysis of pre-treatment and post-treatment magnetic resonance (MR) images for prediction of progression-free survival in the patients with stage II-IVA nasopharyngeal carcinoma (NPC) in South China.

METHODS

One hundred and twenty NPC patients who underwent chemoradiotherapy were enrolled (80 in the training cohort and 40 in the validation cohort). Acquiring data and screening features were performed successively. Totally 1133 radiomics features were extracted from the T2-weight images before and after treatment. Least absolute shrinkage and selection operator regression, recursive feature elimination algorithm, random forest, and minimum-redundancy maximum-relevancy (mRMR) method were used for feature selection. Nomogram discrimination and calibration were evaluated. Harrell's concordance index (C-index) and receiver operating characteristic (ROC) analyses were applied to appraise the prognostic performance of nomograms. Survival curves were plotted using Kaplan-Meier method.

RESULTS

Integrating independent clinical predictors with pre-treatment and post-treatment radiomics signatures which were calculated in conformity with radiomics features, we established a clinical-and-radiomics nomogram by multivariable Cox regression. Nomogram consisting of 14 pre-treatment and 7 post-treatment selected features has been proved to yield a reliable predictive performance in both training and validation groups. The C-index of clinical-and-radiomics nomogram was 0.953 (all P < 0.05), which was higher than that of clinical (0.861) or radiomics nomograms alone (based on pre-treatment statistics: 0.942; based on post-treatment statistics: 0.944). Moreover, we received Rad-score of pre-treatment named RS1 and post-treatment named RS2 and all were used as independent predictors to divide patients into high-risk and low-risk groups. Kaplan-Meier analysis showed that lower RS1 (less than cutoff value, - 1.488) and RS2 (less than cutoff value, - 0.180) were easier to avoid disease progression (all P < 0.01). It showed clinical benefit with decision curve analysis.

CONCLUSIONS

MR-based radiomics measured the burden on primary tumor before treatment and the tumor regression after chemoradiotherapy, and was used to build a model to predict progression-free survival (PFS) in the stage II-IVA NPC patients. It can also help to distinguish high-risk patients from low-risk patients, thus guiding personalized treatment decisions effectively.

Evaluation of parametrial infiltration in patients with IB-IIB cervical cancer by a radiomics model integrating features from tumoral and peritumoral regions in 18 F-fluorodeoxy glucose positron emission tomography/MR images

Parametrial infiltration (PMI) is an essential factor in staging and planning treatment of cervical cancer. The purpose of this study was to develop a radiomics model for accessing PMI in patients with IB-IIB cervical cancer using features from ¹⁸ F-fluorodeoxy glucose (¹⁸ F-FDG) positron emission tomography (PET)/MR images. In this retrospective study, 66 patients with International Federation of Gynecology and Obstetrics stage IB-IIB cervical cancer (22 with PMI and 44 without PMI) who underwent ¹⁸ F-FDG PET/MRI were divided into a training dataset (n = 46) and a testing dataset (n = 20). Features were extracted from both the tumoral and peritumoral regions in ¹⁸ F-FDG PET/MR images. Single-modality and multimodality radiomics models were developed with random forest to predict PMI. The performance of the models was evaluated with F1 score, accuracy, and area under the curve (AUC). The Kappa test was used to observe the differences between PMI evaluated by radiomics-based models and pathological results. The intraclass correlation coefficient for features extracted from each region of interest (ROI) was measured. Three-fold crossvalidation was conducted to confirm the diagnostic ability of the features. The radiomics models developed by features from the tumoral region in T₂ -weighted images (F1 score = 0.400, accuracy = 0.700, AUC = 0.708, Kappa = 0.211, p = 0.329) and the peritumoral region in PET images (F1 score = 0.533, accuracy = 0.650, AUC = 0.714, Kappa = 0.271, p = 0.202) achieved the best performances in the testing dataset among the four single-ROI radiomics models. The combined model using features from the tumoral region in T₂ -weighted images and the peritumoral region in PET images achieved the best performance (F1 score = 0.727, accuracy = 0.850, AUC = 0.774, Kappa = 0.625, p < 0.05). The results suggest that ¹⁸ F-FDG PET/MRI can provide complementary information regarding cervical cancer. The radiomics-based method integrating features from the tumoral and peritumoral regions in ¹⁸ F-FDG PET/MR images gave a superior performance for evaluating PMI.

Dosiomics Risk Model for Predicting Radiation Induced Temporal Lobe Injury and Guiding Individual Intensity-Modulated Radiation Therapy

PURPOSE

We aimed to assess the value of dose distribution-based dosiomics and planning computed tomography-based radiomics to predict radiation-induced temporal lobe injury (TLI) and guide individualized intensity modulated radiation therapy.

METHODS AND MATERIALS

A total of 5599 nasopharyngeal carcinoma patients were enrolled, including 2503, 1072, 988, and 1036 patients in the training, validation, prospective test, and external test cohorts, respectively. The concordance index (C-index) was used to compare the performance of the radiomics and dosiomics models with that of the quantitative analyses of normal tissue effects in the clinic and Wen's models. The predicted TLI-free survival rates of redesigned simulated plans with the same dose-volume histogram but different dose distributions for same patient in a cohort of 30 randomly selected patients were compared by the Wilcoxon matched-pairs signed-rank test.

RESULTS

The radiomics and dosiomics signatures were constructed based on 30 selected computed tomography features and 10 selected dose distribution features, respectively, which were important predictors of TLI-free survival (all P <.001). However, the radiomics signature had a low C-index. The dosiomics risk model combining the dosiomics signature, D_1cc, and age had favorable performance, with C-index values of 0.776, 0.811, 0.805, and 0.794 in the training, validation, prospective test, and external test cohorts, respectively, which were better than those of the quantitative analyses of normal tissue effects in the clinic model and Wen's model (all P <.001). The dosiomics risk model can further distinguish patients in a same risk category divided by other models (all P <.05). Conversely, the other models were unable to separate populations classified by the dosiomics risk model (all P > .05). Two simulated plans with the same dose-volume histogram but different dose distributions had different TLI-free survival rates predicted by dosiomics risk model (all P ≤ .002).

CONCLUSIONS

The dosiomics risk model was superior to traditional models in predicting the risk of TLI. This is a promising approach to precisely predict radiation-induced toxicities and guide individualized intensity modulated radiation therapy.

Inconsistent Partitioning and Unproductive Feature Associations Yield Idealized Radiomic Models

Background Radiomics is the extraction of predefined mathematic features from medical images for the prediction of variables of clinical interest. While some studies report superlative accuracy of radiomic machine learning (ML) models, the published methodology is often incomplete, and the results are rarely validated in external testing data sets. Purpose To characterize the type, prevalence, and statistical impact of methodologic errors present in radiomic ML studies. Materials and Methods Radiomic ML publications were reviewed for the presence of performance-inflating methodologic flaws. Common flaws were subsequently reproduced with randomly generated features interpolated from publicly available radiomic data sets to demonstrate the precarious nature of reported findings. Results In an assessment of radiomic ML publications, the authors uncovered two general categories of data analysis errors: inconsistent partitioning and unproductive feature associations. In simulations, the authors demonstrated that inconsistent partitioning augments radiomic ML accuracy by 1.4 times from unbiased performance and that correcting for flawed methodologic results in areas under the receiver operating characteristic curve approaching a value of 0.5 (random chance). With use of randomly generated features, the authors illustrated that unproductive associations between radiomic features and gene sets can imply false causality for biologic phenomenon. Conclusion Radiomic machine learning studies may contain methodologic flaws that undermine their validity. This study provides a review template to avoid such flaws. © RSNA, 2022 Supplemental material is available for this article. See also the editorial by Jacobs in this issue.

A dual-transformation with contrastive learning framework for lymph node metastasis prediction in pancreatic cancer

Pancreatic cancer is a malignant tumor, and its high recurrence rate after surgery is related to the lymph node metastasis status. In clinical practice, a preoperative imaging prediction method is necessary for prognosis assessment and treatment decision; however, there are two major challenges: insufficient data and difficulty in discriminative feature extraction. This paper proposed a deep learning model to predict lymph node metastasis in pancreatic cancer using multiphase CT, where a dual-transformation with contrastive learning framework is developed to overcome the challenges in fine-grained prediction with small sample sizes. Specifically, we designed a novel dynamic surface projection method to transform 3D data into 2D images for effectively using the 3D information, preserving the spatial correlation of the original texture information and reducing computational resources. Then, this dynamic surface projection was combined with the spiral transformation to establish a dual-transformation method for enhancing the diversity and complementarity of the dataset. A dual-transformation-based data augmentation method was also developed to produce numerous 2D-transformed images to alleviate the effect of insufficient samples. Finally, the dual-transformation-guided contrastive learning scheme based on intra-space-transformation consistency and inter-class specificity was designed to mine additional supervised information, thereby extracting more discriminative features. Extensive experiments have shown the promising performance of the proposed model for predicting lymph node metastasis in pancreatic cancer. Our dual-transformation with contrastive learning scheme was further confirmed on an external public dataset, representing a potential paradigm for the fine-grained classification of oncological images with small sample sizes. The code will be released at https://github.com/SJTUBME-QianLab/Dual-transformation.

Nomogram to predict the prognosis of patients with AFP-negative hepatocellular carcinoma undergoing chemotherapy: A SEER based study

This study aimed to formulate reliable nomograms for predicting the outcomes of α-fetoprotein (AFP)-negative hepatocellular carcinoma (HCC) patients after chemotherapy. HCC patients with normal AFP expression who received chemotherapy were screened and evaluated from the surveillance, epidemiology, and end results database. The prognostic factors for predicting outcomes of HCC patients undergoing chemotherapy were chosen by analyzing the results of Cox analyses. Then, a nomogram integrating the prognostic factors was established. The discrimination ability of the nomogram was evaluated with computation of area under the curve (AUC) and calibration curve. A total of 2424 patients with AFP-negative HCC undergoing chemotherapy were identified. The median overall survival (OS) for HCC patients undergoing chemotherapy was 33 months. Age, race, pathologic grade, N stage, M stage, surgery, and lung metastases were significantly linked to OS. These relevant factors were incorporated into the nomogram. AUC values of the prognostic nomogram for 3- and 5-year OS were 0.696 and 0.706 in the training groups, which were superior to those of the tumor node metastasis (TNM) stage (0.641 and 0.671) in training groups. The calibration curves indicated a high consistency between the predicted probability of nomograms and the actual observation. The validation groups produced AUC values of 0.674 and 0.736 for 3- and 5-year OS, which were superior to those of the TNM stage (0.601 and 0.637) in validation groups. The results revealed significantly unfavorable OS in the high-risk group (P < .001). Nomograms to accurately predict the OS for AFP-negative HCC patients after chemotherapy were established and exhibited a more accurate predication than the conventional TNM staging system.

Predicting prostate cancer in men with PSA levels of 4-10 ng/mL: MRI-based radiomics can help junior radiologists improve the diagnostic performance

To develop MRI-based radiomics model for predicting prostate cancer (PCa) in men with prostate-specific antigen (PSA) levels of 4-10 ng/mL, to compare the performance of radiomics model and PI-RADS v2.1, and to further verify the predictive ability of radiomics model for lesions with different PI-RADS v2.1 score. 171 patients with PSA levels of 4-10 ng/mL were divided into training (n = 119) and testing (n = 52) groups. PI-RADS v2.1 score was assessed by two radiologists. All volumes of interest were segmented on T₂-weighted imaging, diffusion weighted imaging, and apparent diffusion coefficient sequences, from which quantitative radiomics features were extracted. Multivariate logistic regression analysis was performed to establish radiomics model for predicting PCa. The diagnostic performance was assessed using receiver operating characteristic curve analysis. The radiomics model exhibited the best performance in predicting PCa, which was better than the performance of PI-RADS v2.1 scoring by the junior radiologist in the training group [area under the curve (AUC): 0.932 vs 0.803], testing group (AUC: 0.922 vs 0.797), and the entire cohort (AUC: 0.927 vs 0.801) (P < 0.05). The radiomics model performed well for lesions with PI-RADS v2.1 score of 3 (AUC = 0.854, sensitivity = 84.62%, specificity = 84.34%) and PI-RADS v2.1 score of 4-5 (AUC = 0.967, sensitivity = 98.11%, specificity = 86.36%) assigned by junior radiologist. The radiomics model quantitatively outperformed PI-RADS v2.1 for noninvasive prediction of PCa in men with PSA levels of 4-10 ng/mL. The model can help improve the diagnostic performance of junior radiologists and facilitate better decision-making by urologists for management of lesions with different PI-RADS v2.1 score.

Delta computed tomography radiomics features-based nomogram predicts long-term efficacy after neoadjuvant chemotherapy in advanced gastric cancer

BACKGROUND AND OBJECTIVE

No effective preoperative tool is available for predicting the prognosis of advanced gastric cancer (AGC) treated by neoadjuvant chemotherapy (NAC). We aimed to explore the association between change values ("delta") in the radiomic signatures of computed tomography (CT) (delCT-RS) before and after NAC for AGC and overall survival(OS).

METHODS AND DESIGN

A total of 132 AGC patients with AGC were studied as a training cohort in our center, and 45 patients from another center were used as an external validation set. A radiomic signatures-clinical-nomogram(RS-CN) was established using delCT-RS and preoperative clinical variables. The prediction performance of RS-CN was evaluated using the area under the receiver operating characteristic (ROC)curve (AUC values), time-dependent ROC, decision curve analysis(DCA) and C-index.

RESULTS

Multivariable Cox regression analyses showed that delCT-RS, cT-stage, cN-stage, Lauren-type and the value of variation of carcinoma embryonic antigen (CEA) between NAC were independent risk factors for 3-year OS of AGC. In the training cohort, RS-CN had a good prediction performance for OS (C-Index 0.73) and AUC values were significantly better than those of delCT-RS, ypTNM-stage and tumor regression grade(TRG) (0.827 vs 0.704 vs 0.749 vs 0.571, p < 0.001). DCA and time-dependent ROC of RS-CN were better than those of ypTNM stage, TRG grade and delCT-RS. The prediction performance of the validation set was equivalent to that of the training set. The cut-off (177.2) of RS-CN score was obtained from X-Tile software, a score of > 177.2 was defined as high-risk group(HRG), and scores of ≤ 177.2 were defined as the low-risk group(LRG). The 3-year OS and disease free survival(DFS) of patients in the LRG were significantly better than those in the HRG. Adjuvant chemotherapy(AC) can only significantly improve the 3-year OS and DFS of the LRG. (p < 0.05).

CONCLUSIONS

Our nomogram based on delCT-RS has good prediction of prognosis before surgery and helps identify patients that are most likely to benefit from AC. It works well in precise and individualised NAC in AGC.

Dissecting the heterogeneities of the tumor microenvironment between metastatic and nonmetastatic primary colorectal cancer patients by single-cell RNA sequencing

AIMS

One of the main factors hampering the long-term prognosis of colorectal cancer (CRC) patients is distant metastasis. However, the driving factors of CRC metastasis have not been clarified at the single-cell level, which limits the in-depth study of accurate prediction and prevention of CRC metastasis to improve the prognosis.

MATERIALS AND METHODS

Heterogeneities in the tumor microenvironment (TME) between metastatic and nonmetastatic CRC were investigated by single-cell RNA (scRNA) sequencing data. In detail, 50,462 single cells from 20 primary CRC samples, including 40,910 cells from nonmetastatic CRC (M0 group) and 9552 cells from metastatic CRC (M1 group), were systematically analyzed in this study.

KEY FINDINGS

Based on the single-cell atlas, we revealed that cancer cells and fibroblasts accounted for relatively high proportions in metastatic CRC compared with nonmetastatic CRC. Moreover, two specific cancer cell subtypes (FGGY⁺SLC6A6⁺ and IGFBP3⁺KLK7⁺ cancer cells) and three specific fibroblast subtypes (ADAMTS6⁺CAPG⁺, PIM1⁺SGK1⁺ and CA9⁺UPP1⁺ fibroblasts) in metastatic CRC were identified. The functional and differentiation characteristics of these specific cell subclusters were elucidated by enrichment and trajectory analyses.

SIGNIFICANCE

These results provide fundamental knowledge for future in-depth research to screen effective methods and drugs to predict and prevent CRC metastasis to improve prognosis.

Identification of Tregs-Related Genes with Molecular Patterns in Patients with Systemic Sclerosis Related to ILD

Background: Systemic Sclerosis (SSc) is an autoimmune disease that is characterized by vasculopathy, digital ulcers, Raynaud’s phenomenon, renal failure, pulmonary arterial hypertension, and fibrosis. Regulatory T (Treg) cell subsets have recently been found to play crucial roles in SSc with interstitial lung disease (ILD) pathogenesis. This study investigates the molecular mechanism of Treg-related genes in SSc patients through bioinformatic analyses. Methods: The GSE181228 dataset of SSc was used in this study. CIBERSORT was used for assessing the category and proportions of immune cells in SSc. Random forest and least absolute shrinkage and selection operator (LASSO) regression analysis were used to select the hub Treg-related genes. Results: Through bioinformatic analyses, LIPN and CLEC4D were selected as hub Treg-regulated genes. The diagnostic power of the two genes separately for SSc was 0.824 and 0.826. LIPN was associated with the pathway of aminoacyl−tRNA biosynthesis, Primary immunodeficiency, DNA replication, etc. The expression of CLEC4D was associated with the pathway of Neutrophil extracellular trap formation, PPAR signaling pathway, Staphylococcus aureus infection, Systemic lupus erythematosus, TNF signaling pathway, and Toll−like receptor signaling pathway. Conclusion: Through bioinformatic analyses, we identified two Treg-related hub genes (LIPN, CLEC4D) that are mainly involved in the immune response and metabolism of Tregs in SSc with ILD. Moreover, our findings may provide the potential for studying the molecular mechanism of SSc with ILD.

Prediction of lymph node metastasis in stage T1-2 rectal cancers with MRI-based deep learning

OBJECTIVES

This study aimed to investigate whether a deep learning (DL) model based on preoperative MR images of primary tumors can predict lymph node metastasis (LNM) in patients with stage T1-2 rectal cancer.

METHODS

In this retrospective study, patients with stage T1-2 rectal cancer who underwent preoperative MRI between October 2013 and March 2021 were included and assigned to the training, validation, and test sets. Four two-dimensional and three-dimensional (3D) residual networks (ResNet18, ResNet50, ResNet101, and ResNet152) were trained and tested on T2-weighted images to identify patients with LNM. Three radiologists independently assessed LN status on MRI, and diagnostic outcomes were compared with the DL model. Predictive performance was assessed with AUC and compared using the Delong method.

RESULTS

In total, 611 patients were evaluated (444 training, 81 validation, and 86 test). The AUCs of the eight DL models ranged from 0.80 (95% confidence interval [CI]: 0.75, 0.85) to 0.89 (95% CI: 0.85, 0.92) in the training set and from 0.77 (95% CI: 0.62, 0.92) to 0.89 (95% CI: 0.76, 1.00) in the validation set. The ResNet101 model based on 3D network architecture achieved the best performance in predicting LNM in the test set, with an AUC of 0.79 (95% CI: 0.70, 0.89) that was significantly greater than that of the pooled readers (AUC, 0.54 [95% CI: 0.48, 0.60]; p < 0.001).

CONCLUSION

The DL model based on preoperative MR images of primary tumors outperformed radiologists in predicting LNM in patients with stage T1-2 rectal cancer.

KEY POINTS

• Deep learning (DL) models with different network frameworks showed different diagnostic performance for predicting lymph node metastasis (LNM) in patients with stage T1-2 rectal cancer. • The ResNet101 model based on 3D network architecture achieved the best performance in predicting LNM in the test set. • The DL model based on preoperative MR images outperformed radiologists in predicting LNM in patients with stage T1-2 rectal cancer.

Development and validation of a CECT-based radiomics model for predicting IL1B expression and prognosis of head and neck squamous cell carcinoma

Introduction

It is necessary to explore a noninvasive method to stratify head and neck squamous cell carcinoma (HNSCC)’s prognosis and to seek new indicators for individualized precision treatment. As a vital inflammatory cytokine, IL1B might drive a new tumor subtype that could be reflected in overall survival (OS) and predicted using the radiomics method.

Methods

A total of 139 patients with RNA-Seq data from The Cancer Genome Atlas (TCGA) and matched CECT data from The Cancer Image Archive (TCIA) were included in the analysis. The prognostic value of IL1B expression in patients with HNSCC was analyzed using Kaplan-Meier analysis, Cox regression analysis and subgroup analysis. Furthermore, the molecular function of IL1B on HNSCC was explored using function enrichment and immunocytes infiltration analyses. Radiomic features were extracted with PyRadiomics and processed using max-relevance minredundancy, recursive feature elimination, and gradient boosting machine algorithm to construct aradiomics model for predicting IL1B expression. The area under the receiver operating characteristic curve (AUC), calibration curve, precision recall (PR) curve, and decision curve analysis (DCA) curve were used to examine the performance of the model.

Results

Increased IL1B expression in patients with HNSCC indicated a poor prognosis (hazard ratio [HR] = 1.56, P = 0.003) and was harmful in patients who underwent radiotherapy (HR = 1.87, P = 0.007) or chemotherapy (HR = 2.514, P &lt; 0.001). Shape_Sphericity, glszm_SmallAreaEmphasis, and firstorder_Kurtosis were included in the radiomics model (AUC: training cohort, 0.861; validation cohort, 0.703). The calibration curves, PR curves and DCA showed good diagnostic effect of the model. The rad-score was close related to IL1B (P = 4.490*10-9), and shared the same corelated trend to EMT-related genes with IL1B. A higher rad-score was associated with worse overall survival (P = 0.041).

Discussion

The CECT-based radiomics model provides preoperative IL1B expression predictionand offers non-invasive instructions for the prognosis and individualized treatment of patients withHNSCC.

Predicting the Risk of Thymic Tumors Using Texture Analysis of Contrast-Enhanced Chest Computed Tomography

OBJECTIVE

This study aimed to explore the value of contrast-enhanced computed tomography texture features for predicting the risk of malignant thymic epithelial tumor.

METHODS

Data of 97 patients with pathologically confirmed thymic epithelial tumors treated at in our hospital from March 2015 to October 2021 were retrospectively analyzed. Based on the World Health Organization classification of thymic epithelial tumors, patients were divided into a high-risk group (types B2, B3, and C; n = 45) and a low-risk group (types A, AB, and B1; n = 52). Texture analysis was performed using a first-order, gray-level histogram method. Six features were evaluated: mean, variance, skewness, kurtosis, energy, and entropy. The association between contrast-enhanced computed tomography texture features and the risk of malignancy in thymic epithelial tumors was analyzed. The predictive thresholds of predictive texture features were determined by receiver operating characteristics analysis.

RESULTS

The mean, skewness, and entropy were significantly greater in the high-risk group than in the low-risk group (P < 0.05); however, variance, kurtosis, and energy were comparable in the two groups (P > 0.05). The area under curve of mean, skewness, and entropy was 0.670, 0.760, and 0.880, respectively. The optimal cutoff value of entropy for predicting risk of malignancy was 7.74, with sensitivity, specificity, and accuracy of 80.0%, 80.0%, and 75%, respectively.

CONCLUSIONS

Contrast-enhanced computed tomography texture features, especially entropy, may be a useful tool to predict the risk of malignancy in thymic epithelial tumors.

The gut microbiota metabolite capsiate regulate SLC2A1 expression by targeting HIF-1α to inhibit knee osteoarthritis-induced ferroptosis

Ferroptosis is an iron-dependent cell death that has been found to aggravate the progression of osteoarthritis (OA) and gut microbiota- OA axis refers to the bidirectional information network between the gut microbiota and OA, which may provide a new way to protect the OA. However, the role of gut microbiota-derived metabolites in ferroptosis-relative osteoarthritis remains unclear. The objective of this study was to analyze the protective effect of gut microbiota and its metabolite capsiate (CAT) on ferroptosis-relative osteoarthritis in vivo and in vitro experiments. From June 2021 to February 2022, 78 patients were evaluated retrospectively and divided into two groups: The health group (n = 39) and the OA group (n = 40). Iron and oxidative stress indicators were determined in peripheral blood samples. And then in vivo and in vitro experiments, a surgically destabilized medial meniscus (DMM) mice model was established and treated with CAT or Ferric Inhibitor-1 (Fer-1). Solute Carrier Family 2 Member 1 (SLC2A1) short hairpin RNA (shRNA) was utilized to inhibit SLC2A1 expression. Serum iron was increased significantly but total iron binding capacity was decreased significantly in OA patients than healthy people (p < 0.0001). The least absolute shrinkage and selection operator clinical prediction model suggested that serum iron, total iron binding capacity, transferrin, and superoxide dismutase were all independent predictors of OA (p < 0.001). Bioinformatics results suggested that SLC2A1, Metastasis-Associated Lung Adenocarcinoma Transcript 1 (MALAT1), and HIF-1α (Hypoxia Inducible Factor 1 Alpha)-related oxidative stress signaling pathways play an important role in iron homeostasis and OA. In addition, gut microbiota 16s RNA sequencing and untargeted metabolomics were used to find that gut microbiota metabolites CAT in mice with osteoarthritis were negatively correlated with Osteoarthritis Research Society International (OARSI) scores for chondrogenic degeneration (p = 0.0017). Moreover, CAT reduced ferroptosis-dependent osteoarthritis in vivo and in vitro. However, the protective effect of CAT against ferroptosis-dependent osteoarthritis could be eliminated by silencing SLC2A1. SLC2A1 was upregulated but reduced the SLC2A1 and HIF-1α levels in the DMM group. HIF-1α, MALAT1, and apoptosis levels were increased after SLC2A1 knockout in chondrocyte cells (p = 0.0017). Finally, downregulation of SLC2A1 expression by Adeno-associated Virus (AAV) -SLC2A1 shRNA improves osteoarthritis in vivo. Our findings indicated that CAT inhibited HIF-1a expression and reduced ferroptosis-relative osteoarthritis progression by activating SLC2A1.

Predicting and Analyzing Restenosis Risk after Endovascular Treatment in Lower Extremity Arterial Disease: Development and Assessment of a Predictive Nomogram

PURPOSE

This study aimed to develop and internally validate nomograms for predicting restenosis after endovascular treatment of lower extremity arterial diseases.

MATERIALS AND METHODS

A total of 181 hospitalized patients with lower extremity arterial disease diagnosed for the first time between 2018 and 2019 were retrospectively collected. Patients were randomly divided into a primary cohort (n=127) and a validation cohort (n=54) at a ratio of 7:3. The least absolute shrinkage and selection operator (LASSO) regression was used to optimize the feature selection of the prediction model. Combined with the best characteristics of LASSO regression, the prediction model was established by multivariate Cox regression analysis. The predictive models' identification, calibration, and clinical practicability were evaluated by the C index, calibration curve, and decision curve. The prognosis of patients with different grades was compared by survival analysis. Internal validation of the model used data from the validation cohort.

RESULTS

The predictive factors included in the nomogram were lesion site, use of antiplatelet drugs, application of drug coating technology, calibration, coronary heart disease, and international normalized ratio (INR). The prediction model demonstrated good calibration ability, and the C index was 0.762 (95% confidence interval: 0.691-0.823). The C index of the validation cohort was 0.864 (95% confidence interval: 0.801-0.927), which also showed good calibration ability. The decision curve shows that when the threshold probability of the prediction model is more significant than 2.5%, the patients benefit significantly from our prediction model, and the maximum net benefit rate is 30.9%. Patients were graded according to the nomogram. Survival analysis found that there was a significant difference in the postoperative primary patency rate between patients of different classifications (log-rank p<0.001) in both the primary cohort and the validation cohort.

CONCLUSION

We developed a nomogram to predict the risk of target vessel restenosis after endovascular treatment by considering information on lesion site, postoperative antiplatelet drugs, calcification, coronary heart disease, drug coating technology, and INR.

CLINICAL IMPACT

Clinicians can grade patients after endovascular procedure according to the scores of the nomograms and apply intervention measures of different intensities for people at different risk levels. During the follow-up process, an individualized follow-up plan can be further formulated according to the risk classification. Identifying and analyzing risk factors is essential for making appropriate clinical decisions to prevent restenosis.

LncRNA-LA16c-313D11.11,A Signature to Predict Endometrial Carcinoma Patients with a Better Survival

The aim of this study was to develop and internally validate a nomogram of the probability EC patients surviving longer than 5 years. Quantitative real-time PCR (qRT-PCR) was implemented to analyze the expression of lncRNA-LA16c-313D11.11 in 60 EC tissues. The clinicopathological characteristics and follow-up data were retrospectively gathered and analyzed. To establish the prediction model, multivariate logistic regression analysis was applied, and the discrimination, calibration, and clinical practicability of the prediction model were assessed with a concordance index (C-index), calibration chart, and decision curve analysis. Bootstrap validation was performed for internal validation. The prediction factors included the age of patients, myometrial invasion, lymphovascular space invasion, histological subtype, and the expression of lncRNA-LA16C-313D11.11. The model demonstrated good calibration and modest discrimination (C-index = 0.860, 95% confidence interval: 0.724-0.946). Moreover, the interval validation achieved a high C-index value of 0.778. This study revealed the predictive value of lncRNA-LA16C-313D11.11 and successfully developed a nomogram for predicting EC patients survival longer than 5 years, which may facilitate the institution of personalized treatment algorithms, surveillance strategies, and lifestyle interventions.

A nomogram for preoperative risk stratification based on MR morphological parameters in patients with endometrioid adenocarcinoma

PURPOSE

To develop and validate a nomogram based on MRI morphological parameters to preoperatively discriminate between low-risk and non-low-risk patients with endometrioid endometrial carcinoma (EEC).

METHODS

Two hundred eighty-one women with histologically confirmed EEC were divided into training (1.5-T MRI, n = 182) and validation cohorts (3.0-T MRI, n = 99). According to the European Society of Medical Oncology guidelines, the patients were divided into four risk groups: low, intermediate, high-intermediate, and high. Binary classification models were developed (low-risk vs. non-low-risk). Univariate logistic regression (LR) analyses were used to determine which variables to select to build the predictive models. Five classification models were constructed, and the best model was selected. The area under the receiver operating characteristic curve (AUC) was calculated to evaluate the performance of the prediction model and nomogram. P < 0.05 indicated a statistically significant difference.

RESULTS

Age and four morphological parameters (tumor size, tumor volume, maximum anteroposterior tumor diameter on sagittal T2-weighted images (APsag), and tumor area ratio (TAR)) were selected, and the LR model was used to construct an MRI morphological nomogram. The AUCs for the nomogram in predicting a non-low-risk of EEC among patients in the training and validation cohorts were 0.856 (sensitivity = 75.0%, specificity = 83.1%) and 0.849 (sensitivity = 74.6%, specificity = 85.0%), respectively.

CONCLUSION

An MRI morphological nomogram was developed and achieved high diagnostic performance for classifying low-risk and non-low-risk EEC preoperatively, which could provide support for therapeutic decision-making. Furthermore, our findings indicate that this nomogram is robust in the clinical application of various field strength data.

A radiomics model development via the associations with genomics features in predicting axillary lymph node metastasis of breast cancer: a study based on a public database and single-centre verification

AIM

To evaluate the predictive performance of the radiomics model in predicting axillary lymph node (ALN) metastasis through the associations between radiomics features and genomic features in patients with breast cancer.

MATERIALS AND METHODS

Patients with breast cancer were enrolled retrospectively from a public database (111 patients as training group) and one hospital (15 patients as external validation group). The genomics features from transcriptome data and radiomics features from dynamic contrast-enhanced magnetic resonance imaging (MRI) were collected. Firstly, overlapping genes were identified using the Kyoto Encyclopedia of Genes and Genomes and differentially expressed gene analysis, while radiomics features were reduced using a data-driven method. Then, the associations between overlapping genes and retained radiomics features were assessed to obtain key pairs of radiomics-genomics features. Furthermore, the least absolute shrinkage and selection operator (LASSO) algorithm was used to detect the key-pairs features. Finally, radiomics and genomics models were constructed to predict ALN metastasis.

RESULTS

After using the hybrid data- and gene-driven selection method, key pairs of features were detected, which consisted of six radiomic features associated with four genomic features. The radiomics model exhibited comparable performance to the genomics model in predicting ALN metastasis (radiomic model: area under the curve [AUC] = 0.71, sensitivity = 77%, specificity = 56%; genomic model: AUC = 0.72, sensitivity = 85%, specificity = 74%). The four genomic features were enriched in six pathways and related to metabolism and human diseases.

CONCLUSION

The radiomics model established using the gene-driven hybrid selection method could predict ALN metastasis in breast cancer, which showed comparable performance to the genomics model.

Pancancer analysis of SKA1 mutation and its association with the diagnosis and prognosis of human cancers

This study aims to explore the role of SKA1 in cancer diagnosis and prognosis and to investigate the mechanism by which SKA1 affects the malignant behaviors of ovarian cancer. Herein, we analyzed the oncogenic role of SKA1 at pan-cancer level by multiple informatics databases and verified the analysis by in vitro experiments. As a result, SKA1 was upregulated across cancers and was related to poor clinical outcome and immune infiltration. Specifically, the constructed nomogram showed superior performance in predicting the prognosis of epithelial ovarian cancer patients. Furthermore, the in vitro experiments revealed that silencing SKA1 significantly inhibited the proliferation, migratory ability and enhanced the cisplatin sensitivity of ovarian cancer cells. Therefore, we explored the oncogenic and potential therapeutic role of SKA1 across cancers through multiple bioinformatic analysis and revealed that SKA1 may promote ovarian cancer progression and chemoresistance to cisplatin by activating the AKT-FOXO3a signaling pathway.

Development and Validation of a Nomogram for Predicting Radial Artery Spasm During Coronary Angiography

This study describes an attempt to develop a user-friendly nomogram incorporating psychological factors to individually predict the risk of radial artery spasm. Patients consecutively recruited between June 2020 and June 2021 constituted the development cohort for retrospective analysis of the development of a prediction model. Least absolute shrinkage and selection operator regression combined with clinical significance was employed to screen out appropriate independent variables. The model's discrimination and calibration were subsequently evaluated and calibrated by using the C-index, receiver operating characteristic (ROC) curve, and calibration plot. Decision curve analysis was also performed to evaluate the net benefit with the nomogram, and internal validation was assessed using bootstrapping validation. The predictors included in the risk nomogram included "body mass index ," "anxiety score," "duration of interventional surgery," "latency time (time spent waiting in the catheterization laboratory)," "vascular circuity (substantial changes in the curvature of vessels)," and "puncture number." The derived model showed good discrimination with an area under the ROC curve of .77, a C-index of .771 (95% CI: .72-.822) and good calibration. Decision curve analysis indicated that the nomogram provided a better net benefit than the alternatives.

A novel nomogram for the preoperative prediction of sentinel lymph node metastasis in breast cancer

BACKGROUND OR PURPOSE

A practical noninvasive method to identify sentinel lymph node (SLN) status in breast cancer patients, who had a suspicious axillary lymph node (ALN) at ultrasound (US), but a negative clinical physical examination is needed. To predict SLN metastasis using a nomogram based on US and biopsy-based pathological features, this retrospective study investigated associations between clinicopathological features and SLN status.

METHODS

Patients treated with SLN dissection at four centers were apportioned to training, internal, or external validation sets (n = 472, 175, and 81). Lymph node ultrasound and pathological characteristics were compared using chi-squared and t-tests. A nomogram predicting SLN metastasis was constructed using multivariate logistic regression models.

RESULTS

In the training set, statistically significant factors associated with SLN+ were as follows: histology type (p < 0.001); progesterone receptor (PR: p = 0.003); Her-2 status (p = 0.049); and ALN-US shape (p = 0.034), corticomedullary demarcation (CMD: p < 0.001), and blood flow (p = 0.001). With multivariate analysis, five independent variables (histological type, PR status, ALN-US shape, CMD, and blood flow) were integrated into the nomogram (C-statistic 0.714 [95% CI: 0.688-0.740]) and validated internally (0.816 [95% CI: 0.784-0.849]) and externally (0.942 [95% CI: 0.918-0.966]), with good predictive accuracy and clinical applicability.

CONCLUSION

This nomogram could be a direct and reliable tool for individual preoperative evaluation of SLN status, and therefore aids decisions concerning ALN dissection and adjuvant treatment.

Amplitude of low-frequency fluctuation-based regional radiomics similarity network: Biomarker for Parkinson's disease

Parkinson's disease (PD) is a highly heterogeneous disorder that is difficult to diagnose. Therefore, reliable biomarkers are needed. We implemented a method constructing a regional radiomics similarity network (R2SN) based on the amplitude of low-frequency fluctuation (ALFF). We classified patients with PD and healthy individuals by using a machine learning approach in accordance with the R2SN connectome. The ALFF-based R2SN exhibited great reproducibility with different brain atlases and datasets. Great classification performances were achieved both in primary (AUC = 0.85 ± 0.02 and accuracy = 0.81 ± 0.03) and independent external validation (AUC = 0.77 and accuracy = 0.70) datasets. The discriminative R2SN edges correlated with the clinical evaluations of patients with PD. The nodes of discriminative R2SN edges were primarily located in the default mode, sensorimotor, executive control, visual and frontoparietal network, cerebellum and striatum. These findings demonstrate that ALFF-based R2SN is a robust potential neuroimaging biomarker for PD and could provide new insights into connectome reorganization in PD.

A radiomics model for predicting the response to methylprednisolone in brain necrosis after radiotherapy for nasopharyngeal carcinoma

BACKGROUND

Methylprednisolone is recommended as the front-line therapy for radiation-induced brain necrosis (RN) after radiotherapy for nasopharyngeal carcinoma. However, some patients fail to benefit from methylprednisolone or even progress. This study aimed to develop and validate a radiomic model to predict the response to methylprednisolone in RN.

METHODS

Sixty-six patients receiving methylprednisolone were enrolled. In total, 961 radiomic features were extracted from the pre-treatment magnetic resonance imagings of the brain. Least absolute shrinkage and selection operator regression was then applied to construct the radiomics signature. Combined with independent clinical predictors, a radiomics model was built with multivariate logistic regression analysis. Discrimination, calibration and clinical usefulness of the model were assessed. The model was internally validated using 10-fold cross-validation.

RESULTS

The radiomics signature consisted of 16 selected features and achieved favorable discrimination performance. The radiomics model incorporating the radiomics signature and the duration between radiotherapy and RN diagnosis, yielded an AUC of 0.966 and an optimism-corrected AUC of 0.967 via 10-fold cross-validation, which also revealed good discrimination. Calibration curves showed good agreement. Decision curve analysis confirmed the clinical utility of the model.

CONCLUSIONS

The presented radiomics model can be conveniently used to facilitate individualized prediction of the response to methylprednisolone in patients with RN.

A multi-objective based radiomics feature selection method for response prediction following radiotherapy

Objective.Radiomics contains a large amount of mineable information extracted from medical images, which has important significance in treatment response prediction for personalized treatment. Radiomics analyses generally involve high dimensions and redundant features, feature selection is essential for construction of prediction models.Approach.We proposed a novel multi-objective based radiomics feature selection method (MRMOPSO), where the number of features, sensitivity, and specificity are jointly considered as optimization objectives in feature selection. The MRMOPSO innovated in the following three aspects: (1) Fisher score to initialize the population to speed up the convergence; (2) Min-redundancy particle generation operations to reduce the redundancy between radiomics features, a truncation strategy was introduced to further reduce the number of features effectively; (3) Particle selection operations guided by elitism strategies to improve local search ability of the algorithm. We evaluated the effectiveness of the MRMOPSO by using a multi-institution oropharyngeal cancer dataset from The Cancer Imaging Archive. 357 patients were used for model training and cross validation, an additional 64 patients were used for evaluation.Main results.The area under the curve (AUC) of our method achieved AUCs of 0.82 and 0.84 for cross validation and independent dataset, respectively. Compared with classical feature selection methods, the AUC of MRMOPSO is significantly higher than the Lasso (AUC = 0.74,p-value = 0.02), minimal-redundancy-maximal-relevance criterion (mRMR) (AUC = 0.73,p-value = 0.05), F-score (AUC = 0.48,p-value < 0.01), and mutual information (AUC = 0.69,p-value < 0.01) methods. Compared to single-objective methods, the AUC of MRMOPSO is 12% higher than those of the genetic algorithm (GA) (AUC = 0.68,p-value = 0.02) and particle swarm optimization algorithm (AUC = 0.72,p-value = 0.05) methods. Compared to other multi-objective feature selection methods, the AUC of MRMOPSO is 14% higher than those of multiple objective particle swarm optimization (MOPSO) (AUC = 0.68,p-value = 0.02) and nondominated sorting genetic algorithm II (NSGA2) (AUC = 0.70,p-value = 0.03).Significance.We proposed a multi-objective based radiomics feature selection method. Compared to conventional feature reduction algorithms, the proposed algorithm effectively reduced feature dimension, and achieved superior performance, with improved sensitivity and specificity, for response prediction in radiotherapy.

Deep Learning Provides a New Magnetic Resonance Imaging-Based Prognostic Biomarker for Recurrence Prediction in High-Grade Serous Ovarian Cancer

This study aims to use a deep learning method to develop a signature extract from preoperative magnetic resonance imaging (MRI) and to evaluate its ability as a non-invasive recurrence risk prognostic marker in patients with advanced high-grade serous ovarian cancer (HGSOC). Our study comprises a total of 185 patients with pathologically confirmed HGSOC. A total of 185 patients were randomly assigned in a 5:3:2 ratio to a training cohort (n = 92), validation cohort 1 (n = 56), and validation cohort 2 (n = 37). We built a new deep learning network from 3839 preoperative MRI images (T2-weighted images and diffusion-weighted images) to extract HGSOC prognostic indicators. Following that, a fusion model including clinical and deep learning features is developed to predict patients' individual recurrence risk and 3-year recurrence likelihood. In the two validation cohorts, the consistency index of the fusion model was higher than both the deep learning model and the clinical feature model (0.752, 0.813 vs. 0.625, 0.600 vs. 0.505, 0.501). Among the three models, the fusion model had a higher AUC than either the deep learning model or the clinical model in validation cohorts 1 or 2 (AUC = was 0.986, 0.961 vs. 0.706, 0.676/0.506, 0.506). Using the DeLong method, the difference between them was statistically significant (p < 0.05). The Kaplan-Meier analysis distinguished two patient groups with high and low recurrence risk (p = 0.0008 and 0.0035, respectively). Deep learning may be a low-cost, non-invasive method for predicting risk for advanced HGSOC recurrence. Deep learning based on multi-sequence MRI serves as a prognostic biomarker for advanced HGSOC, which provides a preoperative model for predicting recurrence in HGSOC. Additionally, using the fusion model as a new prognostic analysis means that can use MRI data can be used without the need to follow-up the prognostic biomarker.

Value of pre-treatment 18F-FDG PET/CT radiomics in predicting the prognosis of stage III-IV colorectal cancer

Background and purpose

To investigate the value of radiomics features extracted from pre-treatment ¹⁸F-FDG PET/CT in predicting the outcomes of stage III-IV colorectal cancer (CRC), which may assist in clinical management strategies and precise treatment of stage III-IV CRC.

Materials and methods

124 patients with pathologically confirmed stage III-IV CRC who underwent pre-treatment ¹⁸F-FDG PET/CT scans were enrolled in this study. The least absolute shrinkage and selection operator Cox regression (LASSO-Cox) was used to select radiomics features, and the radiomics scores (Rad-scores) were calculated to build radiomics models. The performance of radiomics models was represented by the concordance index (C-index) and compared with clinical models and complex model. The bootstrap resampling method was used to create validation sets. Additionally, nomograms were developed based on complex models.

Results

The C-indices of the radiomics model for predicting PFS and OS were 0.712 (95%CI: 0.680-0.744) and 0.758 (0.728-0.789), respectively. In the clinical model, these values were 0.690 (0.664-0.0.717) and 0.738 (0.709-0.767), respectively. However, in the complex model were 0.734 (0.705-0.762) and 0.780 (0.754-0.807), respectively. The Kaplan-Meier curves demonstrated that the radiomics model could effectively separate patients with stage III-IV stage CRC into high- and low-risk groups (p < 0.001). Multivariate Cox regression analysis confirmed the independent prognostic value of Rad-scores.

Conclusion

Pre-treatment ¹⁸F-FDG PET/CT radiomics features can stratify the risk of patients with stage III-IV CRC and accurately predict their outcomes. These findings could be clinically valuable for precision treatment and management decisions in stage III-IV CRC.

A bibliometric and visual analysis of publications on artificial intelligence in colorectal cancer (2002-2022)

Background

Colorectal cancer (CRC) has the third-highest incidence and second-highest mortality rate of all cancers worldwide. Early diagnosis and screening of CRC have been the focus of research in this field. With the continuous development of artificial intelligence (AI) technology, AI has advantages in many aspects of CRC, such as adenoma screening, genetic testing, and prediction of tumor metastasis.

Objective

This study uses bibliometrics to analyze research in AI in CRC, summarize the field's history and current status of research, and predict future research directions.

Method

We searched the SCIE database for all literature on CRC and AI. The documents span the period 2002-2022. we used bibliometrics to analyze the data of these papers, such as authors, countries, institutions, and references. Co-authorship, co-citation, and co-occurrence analysis were the main methods of analysis. Citespace, VOSviewer, and SCImago Graphica were used to visualize the results.

Result

This study selected 1,531 articles on AI in CRC. China has published a maximum number of 580 such articles in this field. The U.S. had the most quality publications, boasting an average citation per article of 46.13. Mori Y and Ding K were the two authors with the highest number of articles. Scientific Reports, Cancers, and Frontiers in Oncology are this field's most widely published journals. Institutions from China occupy the top 9 positions among the most published institutions. We found that research on AI in this field mainly focuses on colonoscopy-assisted diagnosis, imaging histology, and pathology examination.

Conclusion

AI in CRC is currently in the development stage with good prospects. AI is currently widely used in colonoscopy, imageomics, and pathology. However, the scope of AI applications is still limited, and there is a lack of inter-institutional collaboration. The pervasiveness of AI technology is the main direction of future housing development in this field.

Dynamic radiomics for predicting the efficacy of antiangiogenic therapy in colorectal liver metastases

Background and objective

For patients with advanced colorectal liver metastases (CRLMs) receiving first-line anti-angiogenic therapy, an accurate, rapid and noninvasive indicator is urgently needed to predict its efficacy. In previous studies, dynamic radiomics predicted more accurately than conventional radiomics. Therefore, it is necessary to establish a dynamic radiomics efficacy prediction model for antiangiogenic therapy to provide more accurate guidance for clinical diagnosis and treatment decisions.

Methods

In this study, we use dynamic radiomics feature extraction method that extracts static features using tomographic images of different sequences of the same patient and then quantifies them into new dynamic features for the prediction of treatmentefficacy. In this retrospective study, we collected 76 patients who were diagnosed with unresectable CRLM between June 2016 and June 2021 in the First Hospital of China Medical University. All patients received standard treatment regimen of bevacizumab combined with chemotherapy in the first-line treatment, and contrast-enhanced abdominal CT (CECT) scans were performed before treatment. Patients with multiple primary lesions as well as missing clinical or imaging information were excluded. Area Under Curve (AUC) and accuracy were used to evaluate model performance. Regions of interest (ROIs) were independently delineated by two radiologists to extract radiomics features. Three machine learning algorithms were used to construct two scores based on the best response and progression-free survival (PFS).

Results

For the task that predict the best response patients will achieve after treatment, by using ROC curve analysis, it can be seen that the relative change rate (RCR) feature performed best among all features and best in linear discriminantanalysis (AUC: 0.945 and accuracy: 0.855). In terms of predicting PFS, the Kaplan-Meier plots suggested that the score constructed using the RCR features could significantly distinguish patients with good response from those with poor response (Two-sided P<0.0001 for survival analysis).

Conclusions

This study demonstrates that the application of dynamic radiomics features can better predict the efficacy of CRLM patients receiving antiangiogenic therapy compared with conventional radiomics features. It allows patients to have a more accurate assessment of the effect of medical treatment before receiving treatment, and this assessment method is noninvasive, rapid, and less expensive. Dynamic radiomics model provides stronger guidance for the selection of treatment options and precision medicine.

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.

Development and validation of a novel radiomics-clinical model for predicting post-stroke epilepsy after first-ever intracerebral haemorrhage

OBJECTIVES

Post-stroke epilepsy (PSE) is associated with increased morbidity and mortality. This study aimed to develop and validate a novel prediction model combining clinical factors and radiomics features to accurately identify patients at high risk of developing PSE after intracerebral haemorrhage (ICH).

METHODS

Researchers performed a retrospective medical chart review to extract derivation and validation cohorts of patients with first-ever ICH that attended two tertiary hospitals in China between 2010 and 2020. Clinical data were extracted from electronic medical records and supplemented by tele-interview. Predictive clinical variables were selected by multivariable logistic regression to build the clinical model. Predictive radiomics features were identified, and a Rad-score was calculated according to the coefficient of the selected feature. Both clinical variables and radiomic features were combined to build the radiomics-clinical model. Performances of the clinical, Rad-score, and combined models were compared.

RESULTS

A total of 1571 patients were included in the analysis. Cortical involvement, early seizures within 7 days of ICH, NIHSS score, and ICH volume were included in the clinical model. Rad-score, instead of ICH volume, was included in the combined model. The combined model exhibited better discrimination ability and achieved an overall better benefit against threshold probability than the clinical model in the decision curve analysis (DCA).

CONCLUSIONS

The combined radiomics-clinical model was better able to predict ICH-associated PSE compared to the clinical model. This can help clinicians better predict an individual patient's risk of PSE following a first-ever ICH and facilitate earlier PSE diagnosis and treatment.

KEY POINTS

• Radiomics has not been used in predicting the risk of developing PSE. • Higher Rad-scores were associated with higher risk of developing PSE. • The combined model showed better performance of PSE prediction ability.

Establishment and verification of a nomogram model for predicting the risk of post-stroke depression

Objective

The purpose of this study was to establish a nomogram predictive model of clinical risk factors for post-stroke depression (PSD).

Patients and Methods

We used the data of 202 stroke patients collected from Xuanwu Hospital from October 2018 to September 2020 as training data to develop a predictive model. Nineteen clinical factors were selected to evaluate their risk. Minimum absolute contraction and selection operator (LASSO, least absolute shrinkage and selection operator) regression were used to select the best patient attributes, and seven predictive factors with predictive ability were selected, and then multi-factor logistic regression analysis was carried out to determine six predictive factors and establish a nomogram prediction model. The C-index, calibration chart, and decision curve analyses were used to evaluate the predictive ability, accuracy, and clinical practicability of the prediction model. We then used the data of 156 stroke patients collected by Xiangya Hospital from June 2019 to September 2020 for external verification.

Results

The selected predictors including work style, number of children, time from onset to hospitalization, history of hyperlipidemia, stroke area, and the National Institutes of Health Stroke Scale (NIHSS) score. The model showed good prediction ability and a C index of 0.773 (95% confidence interval: [0.696-0.850]). It reached a high C-index value of 0.71 in bootstrap verification, and its C index was observed to be as high as 0.702 (95% confidence interval: [0.616-0.788]) in external verification. Decision curve analyses further showed that the nomogram of post-stroke depression has high clinical usefulness when the threshold probability was 6%.

Conclusion

This novel nomogram, which combines patients' work style, number of children, time from onset to hospitalization, history of hyperlipidemia, stroke area, and NIHSS score, can help clinicians to assess the risk of depression in patients with acute stroke much earlier in the timeline of the disease, and to implement early intervention treatment so as to reduce the incidence of PSD.

The role of serum inflammatory in mycoplasma pneumonia infection with respiratory asthma

OBJECTIVE

With the growing frequency of Mycoplasma pneumoniae infections linked to respiratory asthma (MP-RA), particularly in children, the quest for novel diagnostic molecular markers has become critical. We examined the link between serum immunoglobulin, inflammatory variables, vitamin A, and vitamin D levels in MP-RA patients and then found markedly diagnostic indicators.

METHODS

From January 2015 to March 2020, our hospital screened 55 cases of healthy control children (HC), 53 instances of mycoplasma pneumonia infection complicated with respiratory asthma (MP-RA), and 58 cases of non-respiratory asthma children for pneumonia mycoplasma infection (MP). Serum immunoglobulins, inflammatory markers, vitamin D, and vitamin A levels were analyzed, and a predictive model including the feature chosen in the least absolute shrinkage and selection operator regression model was developed.

RESULTS

Serum TNF- and IL-1b levels were greater in MP-RA children than in MP children, but 25(OH)D, IgG, and IgA levels were lower. Our findings verified the link between IgA, TNF-a, 25(OH)D, and vitamin A with MP-RA. In addition, TNF-a, IL-1b, 25(OH)D (Vit-D), IgG, and IgA were the predictors in the prediction nomogram, showing the combined influence of serum inflammation in MP-RA. C-index of 0.985 (95% CI: -1.25 to 1.68) shows high scaling ability and the model exhibits good discriminative capacity. With range validation, the high C-index value of 0.96 is still possible.

CONCLUSION

TNF-a, IL-1b, 25(OH)D (Vit-D), IgG, and IgA were considered as predictors in children with MP-RA was investigated in this research.

Development and validation of a clinical-radiomics model to predict recurrence for patients with hepatocellular carcinoma after curative resection

PURPOSE

Recurrence is the leading cause of death in hepatocellular carcinoma (HCC) patients with curative resection. In this study, we aimed to develop a preoperative predictive model based on high-throughput radiomics features and clinical factors for prediction of long- and short-term recurrence for these patients.

METHODS

A total of 270 patients with HCC who were followed up for at least 5 years after curative hepatectomy between June 2014 and December 2017 were enrolled in this retrospective study. Regions of interest were manually delineated in preoperative T2-weighted images using ITK-SNAP software on each HCC tumor slice. A total of 1197 radiomics features were extracted, and the recursive feature elimination method based on logistic regression was used for radiomics signature building. Tenfold cross-validation was applied for model development. Nomograms were constructed and assessed by calibration plot, which compares nomogram-predicated probability with observed outcome. Receiver-operating characteristic was then generated to evaluate the predictive performance of the model in the development and test cohorts.

RESULTS

The 10 most recurrence-free survival-related radiomics features were selected for the radiomics signatures. A multiparametric clinical-radiomics model combining albumin and radiomics score for recurrence prediction was further established. The integrated model demonstrated good calibration and satisfactory discrimination, with the area under the curve (AUC) of 0.864, 95% CI 0.842-0.903, sensitivity of 0.889, and specificity of 0.644 in the test set. Calibration curve showed good agreement concerning 5-year recurrence risk predicted by the nomogram. In addition, the AUC of 1-, 2-, 3-, and 4-year recurrence was 0.935 (95% CI 0.836-1.000), 0.861 (95% CI 0.723-0.999), 0.878 (95% CI 0.762-0.994), and 0.878 (95% CI 0.762-0.994) in the test set, respectively.

CONCLUSIONS

The clinical-radiomics model integrating radiomics features and clinical factors can improve recurrence predictions beyond predictions made using clinical factors or radiomics features alone. Our clinical-radiomics model is a valid method to predict recurrence that should improve preoperative prognostic performance and allow more individualized treatment decisions.

CT radiomics for differentiating oncocytoma from renal cell carcinomas: Systematic review and meta-analysis

BACKGROUND

Radiomics is a type of quantitative analysis that provides a more objective approach to detecting tumor subtypes using medical imaging. The goal of this paper is to conduct a comprehensive assessment of the literature on computed tomography (CT) radiomics for distinguishing renal cell carcinomas (RCCs) from oncocytoma.

METHODS

From February 15th 2012 to 2022, we conducted a broad search of the current literature using the PubMed/MEDLINE, Google scholar, Cochrane Library, Embase, and Web of Science. A meta-analysis of radiomics studies concentrating on discriminating between oncocytoma and RCCs was performed, and the risk of bias was assessed using the Quality Assessment of Diagnostic Accuracy Studies method. The pooled sensitivity, specificity, and diagnostic odds ratio were evaluated via a random-effects model, which was applied for the meta-analysis. This study is registered with PROSPERO (CRD42022311575).

RESULTS

After screening the search results, we identified 6 studies that utilized radiomics to distinguish oncocytoma from other renal tumors; there were a total of 1064 lesions in 1049 patients (288 oncocytoma lesions vs 776 RCCs lesions). The meta-analysis found substantial heterogeneity among the included studies, with pooled sensitivity and specificity of 0.818 [0.619-0.926] and 0.808 [0.537-0.938], for detecting different subtypes of RCCs (clear cell RCC, chromophobe RCC, and papillary RCC) from oncocytoma. Also, a pooled sensitivity and specificity of 0.83 [0.498-0.960] and 0.92 [0.825-0.965], respectively, was found in detecting oncocytoma from chromophobe RCC specifically.

CONCLUSIONS

According to this study, CT radiomics has a high degree of accuracy in distinguishing RCCs from RO, including chromophobe RCCs from RO. Radiomics algorithms have the potential to improve diagnosis in scenarios that have traditionally been ambiguous. However, in order for this modality to be implemented in the clinical setting, standardization of image acquisition and segmentation protocols as well as inter-institutional sharing of software is warranted.

Radiomics analysis of CT imaging improves preoperative prediction of cervical lymph node metastasis in laryngeal squamous cell carcinoma

OBJECTIVES

To investigate the role of CT radiomics for preoperative prediction of lymph node metastasis (LNM) in laryngeal squamous cell carcinoma (LSCC).

METHODS

LSCC patients who received open surgery and lymphadenectomy were enrolled and randomized into primary and validation cohorts at a ratio of 7:3 (325 vs. 139). In the primary cohort, we extracted radiomics features from whole intratumoral regions on venous-phase CT images and constructed a radiomics signature by least absolute shrinkage and selection operator (LASSO) regression. A radiomics model incorporating the radiomic signature and independent clinical factors was established via multivariable logistic regression and presented as a nomogram. Nomogram performance was compared with a clinical model and traditional CT report with respect to its discrimination and clinical usefulness. The radiomics nomogram was internally tested in an independent validation cohort.

RESULTS

The radiomics signature, composed of 9 stable features, was associated with LNM in both the primary and validation cohorts (both p < .001). A radiomics model incorporating independent predictors of LNM (the radiomics signature, tumor subsite, and CT report) showed significantly better discrimination of nodal status than either the clinical model or the CT report in the primary cohort (AUC 0.91 vs. 0.84 vs. 0.68) and validation cohort (AUC 0.89 vs. 0.83 vs. 0.70). Decision curve analysis confirmed that the radiomics nomogram was superior to the clinical model and traditional CT report.

CONCLUSIONS

The CT-based radiomics nomogram may improve preoperative identification of nodal status and help in clinical decision-making in LSCC.

KEY POINTS

• The radiomics model showed favorable performance for predicting LN metastasis in LSCC patients. • The radiomics model may help in clinical decision-making and define patient subsets benefiting most from neck treatment.

A CT-based radiomics nomogram for differentiation of benign and malignant small renal masses (≤4 cm)

RATIONALE AND OBJECTIVES

Based on radiomics signature and clinical data, to develop and verify a radiomics nomogram for preoperative distinguish between benign and malignant of small renal masses (SRM).

MATERIALS AND METHODS

One hundred and fifty-six patients with malignant (n = 92) and benign (n = 64) SRM were divided into the following three categories: category A, typical angiomyolipoma (AML) with visible fat; category B, benign SRM without visible fat, including fat-poor angiomyolipoma (fp-AML), and other rare benign renal tumors; category C, malignant renal tumors. At the same time, one hundred and fifty-six patients included in the study were divided into the training set (n = 108) and test set (n = 48). Respectively from corticomedullary phase (CP), nephrogram phase (NP) and excretory phase (EP) CT images to extract the radiomics features, and the optimal features were screened to establish the logistic regression model and decision tree model, and computed the radiomics score (Rad-score). Demographics and CT findings were evaluated and statistically significant factors were selected to construct a clinical factors model. The radiomics nomogram was established by merging Rad-score and selected clinical factors. The Akaike information criterion (AIC) values and the area under the curve (AUC) were used to compare model discriminant performance, and decision curve analysis (DCA) was used to assess clinical usefulness.

RESULTS

Seven, fifteen, nineteen, and seventeen distinguishing features were obtained in the CP, NP, EP, and three-phase joint, respectively, and the logistic regression and decision tree models were built based on this features. In the training set, the logistic regression model works better than the decision tree model for distinguishing categories A and B from category C, with the AUC of CP, NP, EP and three-phase joint were 0.868, 0.906, 0.937 and 0.975, respectively. The radiomics nomogram constructed based on the three-phase joint Rad-score and selected clinical factor performed well on the training set (AUC, 0.988; 95% CI, 0.974-1.000) for differentiation of categories A and B from category C. In the test set, the AUC of clinical factors model, radiomics signature and radiomics nomogram for discriminating categories A and B from category C were 0.814, 0.954 and 0.968, respectively; for the identification of category A from category C, the AUC of the three models were 0.789, 0.979, 0.985, respectively; for discriminating category B from category C, the AUC of the three models were 0.853, 0.915, 0.946, respectively. The radiomics nomogram had better discriminative than the clinical factors model in both training and test sets (P < 0.05). The radiomics nomogram (AIC = 40.222) with the lowest AIC value was considered the best model compared with that of the clinical factors model (AIC = 106.814) and the radiomics signature (AIC = 44.224). The DCA showed that the radiomics nomogram have better clinical utility than the clinical factors model and radiomics signature.

CONCLUSIONS

The logistic regression model has better discriminative performance than the decision tree model, and the radiomics nomogram based on Rad-score of three-phase joint and clinical factors has a good predictive effect in differentiating benign from malignant of SRM, which may help clinicians develop accurate and individualized treatment strategies.

A CT-based radiomics approach to predict immediate response of radiofrequency ablation in colorectal cancer lung metastases

Objectives

To objectively and accurately assess the immediate efficacy of radiofrequency ablation (RFA) on colorectal cancer (CRC) lung metastases, the novel multimodal data fusion model based on radiomics features and clinical variables was developed.

Methods

This case-control single-center retrospective study included 479 lung metastases treated with RFA in 198 CRC patients. Clinical and radiological data before and intraoperative computed tomography (CT) scans were retrieved. The relative radiomics features were extracted from pre- and immediate post-RFA CT scans by maximum relevance and minimum redundancy algorithm (MRMRA). The Gaussian mixture model (GMM) was used to divide the data of the training dataset and testing dataset. In the process of modeling in the training set, radiomics model, clinical model and fusion model were built based on a random forest classifier. Finally, verification was carried out on an independent test dataset. The receiver operating characteristic curves (ROC) were drawn based on the obtained predicted scores, and the corresponding area under ROC curve (AUC), accuracy, sensitivity, and specificity were calculated and compared.

Results

Among the 479 pulmonary metastases, 379 had complete response (CR) ablation and 100 had incomplete response ablation. Three hundred eighty-six lesions were selected to construct a training dataset and 93 lesions to construct a testing dataset. The multivariate logistic regression analysis revealed cancer antigen 19-9 (CA19-9, p<0.001) and the location of the metastases (p< 0.05) as independent risk factors. Significant correlations were observed between complete ablation and 9 radiomics features. The best prediction performance was achieved with the proposed multimodal data fusion model integrating radiomic features and clinical variables with the highest accuracy (82.6%), AUC value (0.921), sensitivity (80.3%), and specificity (81.4%).

Conclusion

This novel multimodal data fusion model was demonstrated efficient for immediate efficacy evaluation after RFA for CRC lung metastases, which could benefit necessary complementary treatment.

Leveraging a cuproptosis-based signature to predict the prognosis and drug sensitivity of cutaneous melanoma

Immunotherapy is a vital treatment for patients with cutaneous melanoma (CM), but effective predictors to guide clinical immunotherapy are lacking. Cuproptosis is a newly discovered mode of cell death related to tumorigenesis. Exploring the relationship between the mode of cuproptosis and the effect of immunotherapy on CM could better guide clinical management. We clustered all patients with CM in the Cancer Genome Atlas (TCGA) database based on cuproptosis-related genes (CRGs). Prognosis, immunotherapeutic effect, tumor microenvironment score, expression of CD274, CTLA4, and PDCD1, and abundance of CD8 + T infiltration in group A were higher than in group B. Using a combination of LASSO and COX regression analysis, we identified 10 molecules significant to prognosis from differentially expressed genes between the two groups and constructed a cuproptosis-related scoring system (CRSS). Compared with the American Joint Committee on Cancer (AJCC) staging system, CRSS more accurately stratified CM patient risk and guided immunotherapy. CRSS successfully stratified risk and predicted the effect of immunotherapy in 869 patients with eight CM immunotherapy datasets and multiple other tumor immunotherapy cohorts. The nomogram model, which combined AJCC stage and CRSS, greatly improved the ability and accuracy of prognosis prediction. In general, our cuproptosis-related scoring system and nomogram model accurately stratified risk in CM patients and effectively predicted prognosis and the effect of immunotherapy in CM patients.

Development and validation of a nomogram for survival prediction in hepatocellular carcinoma after partial hepatectomy

BACKGROUND

The prognosis for hepatocellular carcinoma (HCC) is complex due to its high level of heterogeneity, even after radical resection. This study was designed to develop and validate a prognostic nomogram for predicting the postoperative prognosis for HCC patients following partial hepatectomy.

PATIENTS AND METHODS

We extracted data on HCC patients and randomly divided them into two groups (primary and validation cohorts), using the Surveillance, Epidemiology and End Results (SEER) database. We developed the prediction model based on the data of the primary cohort and prognostic factors were evaluated using univariate and multivariate Cox regression analysis. A nomogram was constructed for predicting the 1-, 3-, and 5-year survival probability of HCC patients after surgery based on the results of the multivariate Cox regression analysis. The performance of the nomogram was evaluated in terms of its discrimination and calibration. To validated the model, discrimination and calibration were also evaluated in the validation cohort. Decision curve analysis (DCA) was performed to assess the clinical utility of the nomogram.

RESULTS

A total of 890 patients who underwent partial hepatectomy for HCC were included in the study. The primary cohort enrolled 628 patients with a median follow-up time of 39 months, the 1-, 3-, and 5-year survival rate were 95.4%, 52.7% and 25.8% during follow-up. Multivariate Cox regression analysis showed that differentiation, tumor size, AFP and fibrosis were independently association with the prognosis of HCC patients after partial hepatectomy. The nomogram showed a moderate discrimination ith a C-index of 0.705 (95% CI 0.669 to 0.742), and good calibration. Similar discrimination with a C-index of 0.681 (95% CI 0.625 to 0.737), and calibration were also observed in the validation cohort. Decision curve analysis showed that the nomogram could be useful to predicting the prognosis in HCC patients following partial hepatectomy.

CONCLUSIONS

The proposed nomogram is highly predictive and has moderate calibration and discrimination, potentially contributing to the process of managing HCC patients after partial hepatectomy in an individualized way.

Development and validation of a dynamic online nomogram for predicting acute kidney injury in cirrhotic patients upon ICU admission

Background

Acute kidney injury (AKI) is one of the most common and deadly complications among cirrhotic patients at intensive care unit (ICU) admission. We aimed to develop and validate a simple and clinically useful dynamic nomogram for predicting AKI in cirrhotic patients upon ICU admission.

Methods

We analyzed the admission data of 4,375 patients with liver cirrhosis in ICU from 2008 to 2019 in the intensive care unit IV (MIMIC-IV) database. The eligible cirrhotic patients were non-randomly divided into derivation (n = 2,188) and validation (n = 2,187) cohorts at a ratio of 1:1, according to the order of admission. The least absolute shrinkage and selection operator regression model was used to identify independent predictors of AKI in the derivation cohort. A dynamic online nomogram was built using multivariate logistic regression analysis in the derivation cohort and then validated in the validation cohort. The C-index, calibration curve, and decision curve analysis were used to assess the nomogram's discrimination, calibration, and clinical usefulness, respectively.

Results

The incidence of AKI in 4,375 patients was 71.3%. Ascites, chronic kidney disease, shock, sepsis, diuretic drugs, hepatic encephalopathy, bacterial infections, vasoactive drugs, admission age, total bilirubin, and blood urea nitrogen were identified using the multivariate logistic regression analysis as significant predictors of AKI upon ICU admission. In the derivation cohort, the model showed good discrimination (C-index, 0.786; 95% CI, 0.765-0.806) and good calibration. The model in the validation cohort yielded good discrimination (C-index, 0.774; 95% CI, 0.753-0.795) and good calibration. Decision curve analysis demonstrated that the dynamic online nomogram was clinically useful.

Conclusion

Our study presents a dynamic online nomogram that incorporates clinical predictors and can be conveniently used to facilitate the individualized prediction of AKI in cirrhotic patients upon ICU admission.

PET-based radiomics visualizes tumor-infiltrating CD8 T cell exhaustion to optimize radiotherapy/immunotherapy combination in mouse models of lung cancer

BACKGROUND

Cumulative preclinical and clinical evidences showed radiotherapy might augment systemic antitumoral responses to immunotherapy for metastatic non-small cell lung cancer, but the optimal timing of combination is still unclear. The overall infiltration and exhausted subpopulations of tumor-infiltrating CD8⁺ T cells might be a potential biomarker indicating the response to immune checkpoint inhibitors (ICI), the alteration of which is previously uncharacterized during peri-irradiation period, while dynamic monitoring is unavailable via repeated biopsies in clinical practice.

METHODS

Basing on tumor-bearing mice model, we investigated the dynamics of overall infiltration and exhausted subpopulations of CD8⁺ T cells after ablative irradiation. With the understanding of distinct metabolic characteristics accompanied with T cell exhaustion, we developed a PET radiomics approach to identify and visualize T cell exhaustion status.

RESULTS

CD8⁺ T cell infiltration increased from 3 to 14 days after ablative irradiation while terminally exhausted populations significantly predominated CD8⁺ T cells during late course of this infiltrating period, indicating that 3-7 days post-irradiation might be a potential appropriate window for delivering ICI treatment. A PET radiomics approach was established to differentiate T cell exhaustion status, which fitted well in both ICI and irradiation settings. We also visualized the underlying association of more heterogeneous texture on PET images with progressed T cell exhaustion.

CONCLUSIONS

We proposed a non-invasive imaging predictor which accurately assessed heterogeneous T cell exhaustion status relevant to ICI treatment and irradiation, and might serve as a promising solution to timely estimate immune-responsiveness of tumor microenvironment and the optimal timing of combined therapy.

The Effect of Magnetic Resonance Imaging Based Radiomics Models in Discriminating stage I-II and III-IVa Nasopharyngeal Carcinoma

BACKGROUND

Nasopharyngeal carcinoma (NPC) is a common tumor in China. Accurate stages of NPC are crucial for treatment. We therefore aim to develop radiomics models for discriminating early-stage (I-II) and advanced-stage (III-IVa) NPC based on MR images.

METHODS

329 NPC patients were enrolled and randomly divided into a training cohort (n = 229) and a validation cohort (n = 100). Features were extracted based on axial contrast-enhanced T1-weighted images (CE-T1WI), T1WI, and T2-weighted images (T2WI). Least absolute shrinkage and selection operator (LASSO) was used to build radiomics signatures. Seven radiomics models were constructed with logistic regression. The AUC value was used to assess classification performance. The DeLong test was used to compare the AUCs of different radiomics models and visual assessment.

RESULTS

Models A, B, C, D, E, F, and G were constructed with 13, 9, 7, 9, 10, 7, and 6 features, respectively. All radiomics models showed better classification performance than that of visual assessment. Model A (CE-T1WI + T1WI + T2WI) showed the best classification performance (AUC: 0.847) in the training cohort. CE-T1WI showed the greatest significance for staging NPC.

CONCLUSION

Radiomics models can effectively distinguish early-stage from advanced-stage NPC patients, and Model A (CE-T1WI + T1WI + T2WI) showed the best classification performance.

A meta-analysis of based radiomics for predicting lymph node metastasis in patients with biliary tract cancers

Background

To assess the predictive value of radiomics for preoperative lymph node metastasis (LMN) in patients with biliary tract cancers (BTCs).

Methods

PubMed, Embase, Web of Science, Cochrane Library databases, and four Chinese databases [VIP, CNKI, Wanfang, and China Biomedical Literature Database (CBM)] were searched to identify relevant studies published up to February 10, 2022. Two authors independently screened all publications for eligibility. We included studies that used histopathology as a gold standard and radiomics to evaluate the diagnostic efficacy of LNM in BTCs patients. The quality of the literature was evaluated using the Radiomics Quality Score (RQS) and the Quality Assessment of Diagnostic Accuracy Studies 2 (QUADAS-2). The diagnostic odds ratio (DOR), sensitivity, specificity, positive likelihood ratio (PLR), negative likelihood ratio (NLR), and area under the receiver operating characteristic curve (AUC) were calculated to assess the predictive validity of radiomics for lymph node status in patients with BTCs. Spearman correlation coefficients were calculated, and Meta-regression and subgroup analyses were performed to assess the causes of heterogeneity.

Results

Seven studies were included, with 977 patients. The pooled sensitivity, specificity and AUC were 83% [95% confidence interval (CI): 77%, 88%], 78% (95% CI: 71, 84) and 0.88 (95% CI: 0.85, 0.90), respectively. The substantive heterogeneity was observed among the included studies (I ² = 80%, 95%CI: 58,100). There was no threshold effect seen. Meta-regression showed that tumor site contributed to the heterogeneity of specificity analysis (P < 0.05). Imaging methods, number of patients, combined clinical factors, tumor site, model, population, and published year all played a role in the heterogeneity of the sensitivity analysis (P < 0.05). Subgroup analysis revealed that magnetic resonance imaging (MRI) based radiomics had a higher pooled sensitivity than contrast-computed tomography (CT), whereas the result for pooled specificity was the opposite.

Conclusion

Our meta-analysis showed that radiomics provided a high level of prognostic value for preoperative LMN in BTCs patients.

Radiomics based of deep medullary veins on susceptibility-weighted imaging in infants: predicting the severity of brain injury of neonates with perinatal asphyxia

OBJECTIVE

This study aimed to apply radiomics analysis of the change of deep medullary veins (DMV) on susceptibility-weighted imaging (SWI), and to distinguish mild hypoxic-ischemic encephalopathy (HIE) from moderate-to-severe HIE in neonates.

METHODS

A total of 190 neonates with HIE (24 mild HIE and 166 moderate-to-severe HIE) were included in this study. All of them were born at 37 gestational weeks or later. The DMVs were manually included in the regions of interest (ROI). For the purpose of identifying optimal radiomics features and to construct Rad-scores, 1316 features were extracted. LASSO regression was used to identify the optimal radiomics features. Using the Red-score and the clinical independent factor, a nomogram was constructed. In order to evaluate the performance of the different models, receiver operating characteristic (ROC) curve analysis was applied. Decision curve analysis (DCA) was implemented to evaluate the clinical utility.

RESULTS

A total of 15 potential predictors were selected and contributed to Red-score construction. Compared with the radiomics model, the nomogram combined model incorporating Red-score and urea nitrogen did not better distinguish between the mild HIE and moderate-to-severe HIE group. For the training cohort, the AUC of the radiomics model and the combined nomogram model was 0.84 and 0.84. For the validation cohort, the AUC of the radiomics model and the combined nomogram model was 0.80 and 0.79, respectively. The addition of clinical characteristics to the nomogram failed to distinguish mild HIE from moderate-to-severe HIE group.

CONCLUSION

We developed a radiomics model and combined nomogram model as an indicator to distinguish mild HIE from moderate-to-severe HIE group.