Calcineurin controls proximodistal blastema polarity in zebrafish fin regeneration.

Planarian flatworms regenerate their heads and tails from anterior or posterior wounds and this regenerative blastema polarity is controlled by Wnt/ß-catenin signaling. It is well known that a regeneration blastema of appendages of vertebrates such as fish and amphibians grows distally. However, it remains unclear whether a regeneration blastema in vertebrate appendages can grow proximally. Here, we show that a regeneration blastema in zebrafish fins can grow proximally along the proximodistal axis by calcineurin inhibition. We used fin excavation in adult zebrafish to observe unidirectional regeneration from the anterior cut edge (ACE) to the posterior cut edge (PCE) of the cavity and this unidirectional regeneration polarity occurs as the PCE fails to build blastemas. Furthermore, we found that calcineurin activities in the ACE were greater than in the PCE. Calcineurin inhibition induced PCE blastemas, and calcineurin hyperactivation suppressed fin regeneration. Collectively, these findings identify calcineurin as a molecular switch to specify the PCE blastema of the proximodistal axis and regeneration polarity in zebrafish fin.

Spectral CT-based nomogram for preoperative prediction of perineural invasion in locally advanced gastric cancer: a prospective study.

OBJECTIVES: This work focused on developing and validating the spectral CT-based nomogram to preoperatively predict perineural invasion (PNI) for locally advanced gastric cancer (LAGC). METHODS: This work prospectively included 196 surgically resected LAGC patients (139 males, 57 females, 59.55 ± 11.97 years) undergoing triple enhanced spectral CT scans. Patients were labeled as perineural invasion (PNI) positive and negative according to pathologic reports, then further split into primary (n = 130) and validation cohort (n = 66). We extracted clinicopathological information, follow-up data, iodine concentration (IC), and normalized IC values against to aorta (nICs) at arterial/venous/delayed phases (AP/VP/DP). Clinicopathological features and IC values between PNI positive and negative groups were compared. Multivariable logistic regression was performed to screen independent risk factors of PNI. Then, a nomogram was established, and its capability was determined by ROC curves. Its clinical use was evaluated by decision curve analysis. The correlations of PNI and the nomogram with patients' survival were explored by log-rank survival analysis. RESULTS: Borrmann classification, tumor thickness, and nICDP were independent predictors of PNI and used to build the nomogram. The nomogram yielded higher AUCs of 0.853 (0.744-0.928) and 0.782 (0.701-0.850) in primary and validation cohorts than any other parameters (p < 0.05). Both PNI and the nomogram were related to post-surgical treatment planning. Only PNI was associated with disease-free survival in the primary cohort (p < 0.05). CONCLUSION: This work prospectively established a spectral CT-based nomogram, which can effectively predict PNI preoperatively and potentially guide post-surgical treatment strategy in LAGC. KEY POINTS: • The present prospective study established a spectral CT-based nomogram for preoperative prediction of perineural invasion in LAGC. • The proposed nomogram, including morphological features and the quantitative iodine concentration values from spectral CT, had the potential to predict PNI for LAGC before surgery, along with guide post-surgical treatment planning. • Normalized iodine concentration at the delayed phase was the most valuable quantitative parameter, suggesting the importance of delayed enhancement in gastric CT.

Semi-supervised Long-tail Endoscopic Image Classification.

Objective To explore the semi-supervised learning (SSL) algorithm for long-tail endoscopic image classification with limited annotations. Method We explored semi-supervised long-tail endoscopic image classification in HyperKvasir, the largest gastrointestinal public dataset with 23 diverse classes. Semi-supervised learning algorithm FixMatch was applied based on consistency regularization and pseudo-labeling. After splitting the training dataset and the test dataset at a ratio of 4:1, we sampled 20%, 50%, and 100% labeled training data to test the classification with limited annotations. Results The classification performance was evaluated by micro-average and macro-average evaluation metrics, with the Mathews correlation coefficient (MCC) as the overall evaluation. SSL algorithm improved the classification performance, with MCC increasing from 0.8761 to 0.8850, from 0.8983 to 0.8994, and from 0.9075 to 0.9095 with 20%, 50%, and 100% ratio of labeled training data, respectively. With a 20% ratio of labeled training data, SSL improved both the micro-average and macro-average classification performance; while for the ratio of 50% and 100%, SSL improved the micro-average performance but hurt macro-average performance. Through analyzing the confusion matrix and labeling bias in each class, we found that the pseudo-based SSL algorithm exacerbated the classifier's preference for the head class, resulting in improved performance in the head class and degenerated performance in the tail class. Conclusion SSL can improve the classification performance for semi-supervised long-tail endoscopic image classification, especially when the labeled data is extremely limited, which may benefit the building of assisted diagnosis systems for low-volume hospitals. However, the pseudo-labeling strategy may amplify the effect of class imbalance, which hurts the classification performance for the tail class.

Identifying early gastric cancer under magnifying narrow-band images with deep learning: a multicenter study.

BACKGROUND AND AIMS: Narrow-band imaging with magnifying endoscopy (ME-NBI) has shown advantages in the diagnosis of early gastric cancer (EGC). However, proficiency in diagnostic algorithms requires substantial expertise and experience. In this study, we aimed to develop a computer-aided diagnostic model for EGM (EGCM) to analyze and assist in the diagnosis of EGC under ME-NBI. METHODS: A total of 1777 ME-NBI images from 295 cases were collected from 3 centers. These cases were randomly divided into a training cohort (n = 170), an internal test cohort (ITC, n = 73), and an external test cohort (ETC, n = 52). EGCM based on VGG-19 architecture (Visual Geometry Group [VGG], Oxford University, Oxford, UK) with a single fully connected 2-classification layer was developed through fine-tuning and validated on all cohorts. Furthermore, we compared the model with 8 endoscopists with varying experience. Primary comparison measures included accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV). RESULTS: EGCM acquired AUCs of .808 in the ITC and .813 in the ETC. Moreover, EGCM achieved similar predictive performance as the senior endoscopists (accuracy: .770 vs .755, P = .355; sensitivity: .792 vs .767, P = .183; specificity: .745 vs .742, P = .931) but better than the junior endoscopists (accuracy: .770 vs .728, P < .05). After referring to the results of EGCM, the average diagnostic ability of the endoscopists was significantly improved in terms of accuracy, sensitivity, PPV, and NPV (P < .05). CONCLUSIONS: EGCM exhibited comparable performance with senior endoscopists in the diagnosis of EGC and showed the potential value in aiding and improving the diagnosis of EGC by endoscopists.

Integrating No.3 lymph nodes and primary tumor radiomics to predict lymph node metastasis in T1-2 gastric cancer.

BACKGROUND: This study aimed to develope and validate a radiomics nomogram by integrating the quantitative radiomics characteristics of No.3 lymph nodes (LNs) and primary tumors to better predict preoperative lymph node metastasis (LNM) in T1-2 gastric cancer (GC) patients. METHODS: A total of 159 T1-2 GC patients who had undergone surgery with lymphadenectomy between March 2012 and November 2017 were retrospectively collected and divided into a training cohort (n = 80) and a testing cohort (n = 79). Radiomic features were extracted from both tumor region and No. 3 station LNs based on computed tomography (CT) images per patient. Then, key features were selected using minimum redundancy maximum relevance algorithm and fed into two radiomic signatures, respectively. Meanwhile, the predictive performance of clinical risk factors was studied. Finally, a nomogram was built by merging radiomic signatures and clinical risk factors and evaluated by the area under the receiver operator characteristic curve (AUC) as well as decision curve. RESULTS: Two radiomic signatures, reflecting phenotypes of the tumor and LNs respectively, were significantly associated with LN metastasis. A nomogram incorporating two radiomic signatures and CT-reported LN metastasis status showed good discrimination of LN metastasis in both the training cohort (AUC 0.915; 95% confidence interval [CI] 0.832-0.998) and testing cohort (AUC 0.908; 95% CI 0.814-1.000). The decision curve also indicated its potential clinical usefulness. CONCLUSIONS: The nomogram received favorable predictive accuracy in predicting No.3 LNM in T1-2 GC, and the nomogram showed positive role in predicting LNM in No.4 LNs. The nomogram may be used to predict LNM in T1-2 GC and could assist the choice of therapy.

Development and External Validation of Radiomics Approach for Nuclear Grading in Clear Cell Renal Cell Carcinoma.

BACKGROUND AND PURPOSE: Nuclear grades of clear cell renal cell carcinoma (ccRCC) are usually confirmed by invasive methods. Radiomics is a quantitative tool that uses non-invasive medical imaging for tumor diagnosis and prognosis. In this study, a radiomics approach was proposed to analyze the association between preoperative computed tomography (CT) images and nuclear grades of ccRCC. METHODS: Our dataset included 320 ccRCC patients from two centers and was divided into a training set (n = 124), an internal test set (n = 123), and an external test set (n = 73). A radiomic feature set was extracted from unenhanced, corticomedullary phase, and nephrographic phase CT images. The maximizing independent classification information criteria function and recursive feature elimination with cross-validation were used to select effective features. Random forests were used to build a final model for predicting nuclear grades, and area under the receiver operating characteristic curve (AUC) was used to evaluate the performance of radiomic features and models. RESULTS: The radiomic features from the three CT phases could effectively distinguished the four nuclear grades. A combined model, merging radiomic features and clinical characteristics, obtained good predictive performances in the internal test set (AUC 0.77, 0.75, 0.79, and 0.85 for the four grades, respectively), and performance was further confirmed in the external test set, with AUCs of 0.75, 0.68, and 0.73 (no fourth-level data). CONCLUSION: The combination of CT radiomic features and clinical characteristics could discriminate the nuclear grades in ccRCC, which may help in assisting treatment decision making.

Noninvasive Prediction of High-Grade Prostate Cancer via Biparametric MRI Radiomics.

BACKGROUND: Gleason score (GS) is a histologic prognostic factor and the basis of treatment decision-making for prostate cancer (PCa). Treatment regimens between lower-grade (GS ≤7) and high-grade (GS >7) PCa differ largely and have great effects on cancer progression. PURPOSE: To investigate the use of different sequences in biparametric MRI (bpMRI) of the prostate gland for noninvasively distinguishing high-grade PCa. STUDY TYPE: Retrospective. POPULATION: In all, 489 patients (training cohort: N = 326; test cohort: N = 163) with PCa between June 2008 and January 2018. FIELD STRENGTH/SEQUENCE: 3.0T, pelvic phased-array coils, bpMRI including T2 -weighted imaging (T2 WI) and diffusion-weighted imaging (DWI); apparent diffusion coefficient map extracted from DWI. ASSESSMENT: The whole prostate gland was delineated. Radiomic features were extracted and selected using the Kruskal-Wallis test, the minimum redundancy-maximum relevance, and the sequential backward elimination algorithm. Two single-sequence radiomic (T2 WI, DWI) and two combined (T2 WI-DWI, T2 WI-DWI-Clinic) models were respectively constructed and validated via logistic regression. STATISTICAL TESTS: The Kruskal-Wallis test and chi-squared test were utilized to evaluate the differences among variable groups. P < 0.05 determined statistical significance. The area under the receiver operating characteristic curve (AUC), specificity, sensitivity, and accuracy were used to evaluate model performance. The Delong test was conducted to compare the differences between the AUCs of all models. RESULT: All radiomic models showed significant (P < 0.001) predictive performances. Between the single-sequence radiomic models, the DWI model achieved the most encouraging results, with AUCs of 0.801 and 0.787 in the training and test cohorts, respectively. For the combined models, the T2 WI-DWI models acquired an AUC of 0.788, which was almost the same with DWI in the test cohort, and no significant difference was found between them (training cohort: P = 0.199; test cohort: P = 0.924). DATA CONCLUSION: Radiomics based on bpMRI can noninvasively identify high-grade PCa before the operation, which is helpful for individualized diagnosis of PCa. LEVEL OF EVIDENCE: 4 TECHNICAL EFFICACY STAGE: 2 J. Magn. Reson. Imaging 2020;52:1102-1109.

Dual-energy CT-based deep learning radiomics can improve lymph node metastasis risk prediction for gastric cancer.

OBJECTIVES: To build a dual-energy CT (DECT)-based deep learning radiomics nomogram for lymph node metastasis (LNM) prediction in gastric cancer. MATERIALS AND METHODS: Preoperative DECT images were retrospectively collected from 204 pathologically confirmed cases of gastric adenocarcinoma (mean age, 58 years; range, 28-81 years; 157 men [mean age, 60 years; range, 28-81 years] and 47 women [mean age, 54 years; range, 28-79 years]) between November 2011 and October 2018, They were divided into training (n = 136) and test (n = 68) sets. Radiomics features were extracted from monochromatic images at arterial phase (AP) and venous phase (VP). Clinical information, CT parameters, and follow-up data were collected. A radiomics nomogram for LNM prediction was built using deep learning approach and evaluated in test set using ROC analysis. Its prognostic performance was determined with Harrell's concordance index (C-index) based on patients' outcomes. RESULTS: The dual-energy CT radiomics signature was associated with LNM in two sets (Mann-Whitney U test, p < 0.001) and an achieved area under the ROC curve (AUC) of 0.71 for AP and 0.76 for VP in test set. The nomogram incorporated the two radiomics signatures and CT-reported lymph node status exhibited AUCs of 0.84 in the training set and 0.82 in the test set. The C-indices of the nomogram for progression-free survival and overall survival prediction were 0.64 (p = 0.004) and 0.67 (p = 0.002). CONCLUSION: The DECT-based deep learning radiomics nomogram showed good performance in predicting LNM in gastric cancer. Furthermore, it was significantly associated with patients' prognosis. KEY POINTS: • This study investigated the value of deep learning dual-energy CT-based radiomics in predicting lymph node metastasis in gastric cancer. • The dual-energy CT-based radiomics nomogram outweighed the single-energy model and the clinical model. • The nomogram also exhibited a significant prognostic ability for patient survival and enriched radiomics studies.

Development and validation of a novel MR imaging predictor of response to induction chemotherapy in locoregionally advanced nasopharyngeal cancer: a randomized controlled trial substudy (NCT01245959).

BACKGROUND: In locoregionally advanced nasopharyngeal carcinoma (LANPC) patients, variance of tumor response to induction chemotherapy (ICT) was observed. We developed and validated a novel imaging biomarker to predict which patients will benefit most from additional ICT compared with chemoradiotherapy (CCRT) alone. METHODS: All patients, including retrospective training (n = 254) and prospective randomized controlled validation cohorts (a substudy of NCT01245959, n = 248), received ICT+CCRT or CCRT alone. Primary endpoint was failure-free survival (FFS). From the multi-parameter magnetic resonance images of the primary tumor at baseline, 819 quantitative 2D imaging features were extracted. Selected key features (according to their interaction effect between the two treatments) were combined into an Induction Chemotherapy Outcome Score (ICTOS) with a multivariable Cox proportional hazards model using modified covariate method. Kaplan-Meier curves and significance test for treatment interaction were used to evaluate ICTOS, in both cohorts. RESULTS: Three imaging features were selected and combined into ICTOS to predict treatment outcome for additional ICT. In the matched training cohort, patients with a high ICTOS had higher 3-year and 5-year FFS in ICT+CCRT than CCRT subgroup (69.3% vs. 45.6% for 3-year FFS, and 64.0% vs. 36.5% for 5-year FFS; HR = 0.43, 95% CI = 0.25-0.74, p = 0.002), whereas patients with a low ICTOS had no significant difference in FFS between the subgroups (p = 0.063), with a significant treatment interaction (pinteraction <  0.001). This trend was also found in the validation cohort with high (n = 73, ICT+CCRT 89.7% and 89.7% vs. CCRT 61.8% and 52.8% at 3-year and 5-year; HR = 0.17, 95% CI = 0.06-0.51, p <  0.001) and low ICTOS (n = 175, p = 0.31), with a significant treatment interaction (pinteraction = 0.019). Compared with 12.5% and 16.6% absolute benefit in the validation cohort (3-year FFS from 69.9 to 82.4% and 5-year FFS from 63.4 to 80.0% from additional ICT), high ICTOS group in this cohort had 27.9% and 36.9% absolute benefit. Furthermore, no significant survival improvement was found from additional ICT in both groups after stratifying low ICTOS patients into low-risk and high-risks groups, by clinical risk factors. CONCLUSION: An imaging biomarker, ICTOS, as proposed, identified patients who were more likely to gain additional survival benefit from ICT+CCRT (high ICTOS), which could influence clinical decisions, such as the indication for ICT treatment. TRIAL REGISTRATION: ClinicalTrials.gov , NCT01245959 . Registered 23 November 2010.

Radiomic signature as a predictive factor for lymph node metastasis in early-stage cervical cancer.

BACKGROUND: Lymph node metastasis (LNM) is the principal risk factor for poor outcomes in early-stage cervical cancer. Radiomics may offer a noninvasive way for predicting the stage of LNM. PURPOSE: To evaluate a radiomic signature of LN involvement based on sagittal T1 contrast-enhanced (CE) and T2 MRI sequences. STUDY TYPE: Retrospective. POPULATION: In all, 143 patients were randomly divided into two primary and validation cohorts with 100 patients in the primary cohort and 43 patients in the validation cohort. FIELD STRENGTH/SEQUENCE: T1 CE and T2 MRI sequences at 3T. ASSESSMENT: The gold standard of LN status was based on histologic results. A radiologist with 10 years of experience used the ITK-SNAP software for 3D manual segmentation. A senior radiologist with 15 years of experience validated all segmentations. The area under the receiver operating characteristics curve (ROC AUC), classification accuracy, sensitivity, and specificity were used between LNM and non-LNM groups. STATISTICAL TESTS: A total of 970 radiomic features and seven clinical characteristics were extracted. Minimum redundancy / maximum relevance and support vector machine algorithms were applied to select features and construct a radiomic signature. The Mann-Whitney U-test and the chi-square test were used to test the performance of clinical characteristics and potential prognostic outcomes. The results were used to assess the quantitative discrimination performance of the SVM-based radiomic signature. RESULTS: The radiomic signatures allowed good discrimination between LNM and non-LNM groups. The ROC AUC was 0.753 (95% confidence interval [CI], 0.656-0.850) in the primary cohort and 0.754 (95% CI, 0584-0.924) in the validation cohort. DATA CONCLUSIONS: A multiple-sequence MRI radiomic signature can be used as a noninvasive biomarker for preoperative assessment of LN status and potentially influence the therapeutic decision-making in early-stage cervical cancer patients. LEVEL OF EVIDENCE: 3 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:304-310.

Quantitative radiomic biomarkers for discrimination between neuromyelitis optica spectrum disorder and multiple sclerosis.

BACKGROUND: Precise diagnosis and early appropriate treatment are of importance to reduce neuromyelitis optica spectrum disorder (NMOSD) and multiple sclerosis (MS) morbidity. Distinguishing NMOSD from MS based on clinical manifestations and neuroimaging remains challenging. PURPOSE: To investigate radiomic signatures as potential imaging biomarkers for distinguishing NMOSD from MS, and to develop and validate a diagnostic radiomic-signature-based nomogram for individualized disease discrimination. STUDY TYPE: Retrospective, cross-sectional study. SUBJECTS: Seventy-seven NMOSD patients and 73 MS patients. FIELD STRENGTH/SEQUENCE: 3T/T2 -weighted imaging. ASSESSMENT: Eighty-eight patients and 62 patients were respectively enrolled in the primary and validation cohorts. Quantitative radiomic features were automatically extracted from lesioned regions on T2 -weighted imaging. A least absolute shrinkage and selection operator analysis was used to reduce the dimensionality of features. Finally, we constructed a radiomic nomogram for disease discrimination. STATISTICAL TESTS: Features were compared using the Mann-Whitney U-test with a nonnormal distribution. We depicted the nomogram on the basis of the results of the logistic regression using the rms package in R. The Hmisc package was used to investigate the performance of the nomogram via Harrell's C-index. RESULTS: A total of 273 quantitative radiomic features were extracted from lesions. A multivariable analysis selected 11 radiomic features and five clinical features to be included in the model. The radiomic signature (P < 0.001 for both the primary and validation cohorts) showed good potential for building a classification model for disease discrimination. The area under the receiver operating characteristic curve was 0.9880 for the training cohort and 0.9363 for the validation cohort. The nomogram exhibited good discrimination, a concordance index of 0.9363, and good calibration in the primary cohort. The nomogram showed similar discrimination, concordance (0.9940), and calibration in the validation cohort. DATA CONCLUSION: The diagnostic radiomic-signature-based nomogram has potential utility for individualized disease discrimination of NMOSD from MS in clinical practice. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1113-1121.

MR-Based Radiomics Nomogram of Cervical Cancer in Prediction of the Lymph-Vascular Space Invasion preoperatively.

BACKGROUND: Lymph-vascular space invasion (LVSI) is an unfavorable prognostic factor in cervical cancer. Unfortunately, there are no current clinical tools for the preoperative prediction of LVSI. PURPOSE: To develop and validate an axial T1 contrast-enhanced (CE) MR-based radiomics nomogram that incorporated a radiomics signature and some clinical parameters for predicting LVSI of cervical cancer preoperatively. STUDY TYPE: Retrospective. POPULATION: In all, 105 patients were randomly divided into two cohorts at a 2:1 ratio. FIELD STRENGTH/SEQUENCE: T1 CE MRI sequences at 1.5T. ASSESSMENT: Univariate analysis was performed on the radiomics features and clinical parameters. Multivariate analysis was performed to determine the optimal feature subset. The receiver operating characteristic (ROC) analysis was performed to evaluate the performance of prediction model and radiomics nomogram. STATISTICAL TESTS: The Mann-Whitney U-test and the chi-square test were used to evaluate the performance of clinical characteristics and LVSI status by pathology. The minimum-redundancy/maximum-relevance and recursive feature elimination methods were applied to select the features. The radiomics model was constructed using logistic regression. RESULTS: Three radiomics features and one clinical characteristic were selected. The radiomics nomogram showed favorable discrimination between LVSI and non-LVSI groups. The AUC was 0.754 (95% confidence interval [CI], 0.6326-0.8745) in the training cohort and 0.727 (95% CI, 0.5449-0.9097) in the validation cohort. The specificity and sensitivity were 0.756 and 0.828 in the training cohort and 0.773 and 0.692 in the validation cohort. DATA CONCLUSION: T1 CE MR-based radiomics nomogram serves as a noninvasive biomarker in the prediction of LVSI in patients with cervical cancer preoperatively. LEVEL OF EVIDENCE: 4 Technical Efficacy: Stage 2 J. Magn. Reson. Imaging 2019;49:1420-1426.

Radiomics signature: a biomarker for the preoperative discrimination of lung invasive adenocarcinoma manifesting as a ground-glass nodule.

OBJECTIVES: To identify the radiomics signature allowing preoperative discrimination of lung invasive adenocarcinomas from non-invasive lesions manifesting as ground-glass nodules. METHODS: This retrospective primary cohort study included 160 pathologically confirmed lung adenocarcinomas. Radiomics features were extracted from preoperative non-contrast CT images to build a radiomics signature. The predictive performance and calibration of the radiomics signature were evaluated using intra-cross (n=76), external non-contrast-enhanced CT (n=75) and contrast-enhanced CT (n=84) validation cohorts. The performance of radiomics signature and CT morphological and quantitative indices were compared. RESULTS: 355 three-dimensional radiomics features were extracted, and two features were identified as the best discriminators to build a radiomics signature. The radiomics signature showed a good ability to discriminate between invasive adenocarcinomas and non-invasive lesions with an accuracy of 86.3%, 90.8%, 84.0% and 88.1%, respectively, in the primary and validation cohorts. It remained an independent predictor after adjusting for traditional preoperative factors (odds ratio 1.87, p < 0.001) and demonstrated good calibration in all cohorts. It was a better independent predictor than CT morphology or mean CT value. CONCLUSIONS: The radiomics signature showed good predictive performance in discriminating between invasive adenocarcinomas and non-invasive lesions. Being a non-invasive biomarker, it could assist in determining therapeutic strategies for lung adenocarcinoma. KEY POINTS: • The radiomics signature was a non-invasive biomarker of lung invasive adenocarcinoma. • The radiomics signature outweighed CT morphological and quantitative indices. • A three-centre study showed that radiomics signature had good predictive performance.

Radiomics in multiple sclerosis and neuromyelitis optica spectrum disorder.

OBJECTIVE: To develop and validate an individual radiomics nomogram for differential diagnosis between multiple sclerosis (MS) and neuromyelitis optica spectrum disorder (NMOSD). METHODS: We retrospectively collected 67 MS and 68 NMOSD with spinal cord lesions as a primary cohort and prospectively recruited 28 MS and 26 NMOSD patients as a validation cohort. Radiomic features were extracted from the spinal cord lesions. A prediction model for differentiating MS and NMOSD was built by combining the radiomic features with several clinical and routine MRI measurements. The performance of the model was assessed with respect to its calibration plot and clinical discrimination in the primary and validation cohorts. RESULTS: Nine radiomics features extracted from an initial set of 485, predominantly reflecting lesion heterogeneity, combined with lesion length, patient sex, and EDSS, were selected to build the model for differentiating MS and NMOSD. The areas under the ROC curves (AUC) for differentiating the two diseases were 0.8808 and 0.7115, for the primary and validation cohort, respectively. This model demonstrated good calibration (C-index was 0.906 and 0.802 in primary and validation cohort). CONCLUSIONS: A validated nomogram that incorporates the radiomic signature of spinal cord lesions, as well as cord lesion length, sex, and EDSS score, can usefully differentiate MS and NMOSD. KEY POINTS: • Radiomic features of spinal cord lesions in MS and NMOSD were different. • Radiomic signatures can capture pathological alterations and help differentiate MS and NMOSD.

Prognostic value of computed tomography radiomics features in patients with gastric cancer following curative resection.

OBJECTIVES: The present study aimed to investigate the clinical prognostic significance of radiomics signature (R-signature) in patients with gastric cancer who had undergone radical resection. METHODS: A total of 181 patients with gastric cancer who had undergone radical resection were enrolled in this retrospective study. The association between the R-signature and overall survival (OS) was assessed in the primary cohort and verified in the validation cohort. Furthermore, the performance of a radiomics nomogram integrating the R-signature and significant clinicopathological risk factors was evaluated. RESULTS: The R-signature, which consisted of six imaging features, stratified patients with gastric cancer who had undergone radical resection into two prognostic risk groups in both cohorts. The radiomics nomogram incorporating R-signature and significant clinicopathological risk factors (T stage, N stage, and differentiation) exhibited significant prognostic superiority over clinical nomogram and R-signature alone (Harrell concordance index, 0.82 vs 0.71 and 0.82 vs 0.74, respectively, p < 0.001 in both analyses). All calibration curves showed remarkable consistency between predicted and actual survival, and decision curve analysis verified the usefulness of the radiomics nomogram for clinical practice. CONCLUSIONS: The R-signature could be used to stratify patients with gastric cancer following radical resection into high- and low-risk groups. Furthermore, the radiomics nomogram provided better predictive accuracy than other predictive models and might aid clinicians with therapeutic decision-making and patient counseling. KEY POINTS: • Radiomics can stratify the gastric cancer patients following radical resection into high- and low-risk groups. • Radiomics can improve the prognostic value of TNM staging system. • Radiomics may facilitate personalized treatment of gastric cancer patients.

Preoperative Diagnosis of Regional Lymph Node Metastasis of Colorectal Cancer With Quantitative Parameters From Dual-Energy CT.

OBJECTIVE. The purpose of this study was to investigate the performance of quantitative parameters derived from dual-energy CT (DECT) in the preoperative diagnosis of regional metastatic lymph nodes (LNs) in patients with colorectal cancer. SUBJECTS AND METHODS. Triphasic contrast-enhanced DECT was performed for 178 patients with colon or high rectal cancer. The morphologic criteria, short-axis diameter, and quantitative DECT parameters of the largest regional LN were measured and compared between pathologically metastatic and nonmetastatic LNs. Univariate and multivariable logistic regression analyses were used to determine the independent DECT parameters for predicting LN metastasis. Diagnostic performance measures were assessed by ROC curve analysis and compared by McNemar test. RESULTS. A total of 178 largest LNs (72 metastatic, 106 nonmetastatic) were identified in 178 patients. The best single DECT parameter for differentiation between metastatic and nonmetastatic LNs was normalized effective atomic number (Zeff) in the portal venous phase (AUC, 0.871; accuracy, 84.8%). These values were higher than those of morphologic criteria (AUC, 0.505-0.624; accuracy, 47.8-62.4%) and short-axis diameter (AUC, 0.647; accuracy, 66.3%) (p < 0.05). The diagnostic accuracy of combined normalized iodine concentration in the arterial phase and normalized effective atomic number in the portal venous phase was further improved to 87.1% (AUC, 0.916). CONCLUSION. Quantitative parameters derived from DECT can be used to improve preoperative diagnostic accuracy in evaluation for regional metastatic LNs in patients with colorectal cancer.

Can CT-based radiomics signature predict KRAS/NRAS/BRAF mutations in colorectal cancer?

OBJECTIVES: To investigate whether CT-based radiomics signature can predict KRAS/NRAS/BRAF mutations in colorectal cancer (CRC). METHODS: This retrospective study consisted of a primary cohort (n = 61) and a validation cohort (n = 56) with pathologically confirmed CRC. Patients underwent KRAS/NRAS/BRAF mutation tests and contrast-enhanced CT before treatment. A total of 346 radiomics features were extracted from portal venous-phase CT images of the entire primary tumour. Associations between the genetic mutations and clinical background, tumour staging, and histological differentiation were assessed using univariate analysis. RELIEFF and support vector machine methods were performed to select key features and build a radiomics signature. RESULTS: The radiomics signature was significantly associated with KRAS/NRAS/BRAF mutations (P < 0.001). The area under the curve, sensitivity, and specificity for predicting KRAS/NRAS/BRAF mutations were 0.869, 0.757, and 0.833 in the primary cohort, respectively, while they were 0.829, 0.686, and 0.857 in the validation cohort, respectively. Clinical background, tumour staging, and histological differentiation were not associated with KRAS/NRAS/BRAF mutations in both cohorts (P>0.05). CONCLUSIONS: The proposed CT-based radiomics signature is associated with KRAS/NRAS/BRAF mutations. CT may be useful for analysis of tumour genotype in CRC and thus helpful to determine therapeutic strategies. KEY POINTS: • Key features were extracted from CT images of the primary colorectal tumour. • The proposed radiomics signature was significantly associated with KRAS/NRAS/BRAF mutations. • In the primary cohort, the proposed radiomics signature predicted mutations. • Clinical background, tumour staging, and histological differentiation were unable to predict mutations.

LGE-CMR-derived texture features reflect poor prognosis in hypertrophic cardiomyopathy patients with systolic dysfunction: preliminary results.

OBJECTIVES: To evaluate the prognostic value of texture features based on late gadolinium enhancement cardiac magnetic resonance (LGE-CMR) images in hypertrophic cardiomyopathy (HCM) patients with systolic dysfunction. METHODS: 67 HCM patients with systolic dysfunction (41 male and 26 female, mean age ± standard deviation, 46.20 years ± 13.38) were enrolled. All patients underwent 1.5 T CMR cine and LGE imaging. Texture features were extracted from LGE images. Cox proportional hazard analysis and Kaplan-Meier analysis were used to determine the association of texture features and traditional parameters with event free survival. RESULTS: Family history (hazard ratio [HR]=2.558, 95 % confidence interval [CI]=1.060-6.180), NYHA III-IV (HR=5.627, CI=1.652-19.173), left ventricular ejection fraction (HR=0.945, CI=0.902-0.991), left ventricular end-diastolic volume index (HR=1.006, CI=1.000-1.012), LGE extent (HR=1.911, CI=1.348-2.709) and three texture parameters [X0_H_skewness (HR=0.783, CI=0.691-0.889), X0_GLCM_cluster_tendency (HR=0.735, CI=0.616-0.877) and X0_GLRLM_energy (HR=1.344, CI=1.173-1.540)] were significantly associated with event free survival in univariate analysis (p<0.05). The HR of LGE extent (HR=1.548 [CI=1.046-2.293], 1.650 [CI=1.122-2.428] and 1.586 [CI=1.044-2.409] per 10 % increase, p<0.05) remained significant when adjusted by one of the three texture features. CONCLUSION: Increased LGE heterogeneity (higher X0_GLRLM_energy, lower X0_H_skewness and lower X0_GLCM_cluster_tendency) was associated with adverse events in HCM patients with systolic dysfunction. KEY POINTS: • Textural analysis from CMR can be applied in HCM. • Texture features derived from LGE images can capture fibrosis heterogeneity. • CMR texture analysis provides prognostic information in HCM patients.

Diagnostic accuracy of dual-energy CT-based nomograms to predict lymph node metastasis in gastric cancer.

OBJECTIVES: To develop and validate a dual-energy CT based nomogram for the preoperative prediction of lymph node metastasis (LNM) in patients with gastric cancer (GC). METHODS: A total of 210 surgically confirmed GC patients (159 males, 51 females; mean age: 59.8 ± 7.7 years, range: 28-79 years) who underwent spectral CT scans were retrospectively enrolled and split into a primary cohort (n = 140) and validation cohort (n = 70). Clinical information and follow-up data including overall survival (OS) and progression-free survival (PFS) were collected. The iodine concentration (IC) of the primary tumors at the arterial phase (AP) and venous phase (VP) were measured and then normalized to the aorta (nICs). Univariate, multivariable logistic regression and Cox regression analyses were performed to screen predictive indicators for LNM and outcome. A nomogram for risk factors of LNM was developed, and its performance was measured using the ROC, accuracy and Harrell's concordance index (C-index). RESULTS: Tumor thickness, Borrmann classification and ICVP were independent predictors of LNM. The nomogram was significantly associated with LN status (p < 0.001). It yielded an AUC of 0.793 [95% confidence interval (95% CI), 0.678-0.908] and an accuracy of 0.757 (95% CI, 0.640-0.852) in the internal-validation cohort. The nomogram also exhibited a prognostic ability with C-indices of 0.675 (95% CI, 0.571-0.779; p < 0.001) for PFS and 0.643 (95% CI, 0.518-0.768; p = 0.025) for OS. CONCLUSION: This study presented a dual-energy quantification-based nomogram, which can be used to facilitate the preoperative individualized prediction of LNM in patients with GC. KEY POINTS: • This study first developed and internally validated a dual-energy CT-based nomogram to predict lymph node metastasis in patients with gastric cancer. • The nomogram incorporated the clinical risk factors and iodine concentration, which would enable superior preoperative individual prediction of lymph node metastasis and add more information for the optimal therapeutic strategy. • The nomogram also exhibited a significant prognostic ability for progression-free and overall survival.