gutMDisorder v2.0: a comprehensive database for dysbiosis of gut microbiota in phenotypes and interventions.

Gut microbiota plays a significant role in maintaining host health, and conversely, disorders potentially lead to dysbiosis, an imbalance in the composition of the gut microbial community. Intervention approaches, such as medications, diets, and several others, also alter the gut microbiota in either a beneficial or harmful direction. In 2020, the gutMDisorder was developed to facilitate researchers in the investigation of dysbiosis of gut microbes as occurs in various disorders as well as with therapeutic interventions. The database has been updated this year, following revision of previous publications and newly published reports to manually integrate confirmed associations under multitudinous conditions. Additionally, the microbial contents of downloaded gut microbial raw sequencing data were annotated, the metadata of the corresponding hosts were manually curated, and the interactive charts were developed to enhance visualization. The improvements have assembled into gutMDisorder v2.0, a more advanced search engine and an upgraded web interface, which can be freely accessed via http://bio-annotation.cn/gutMDisorder/.

Incidence rate of occult lymph node metastasis in clinical T1-2N0M0 small cell lung cancer patients and radiomic prediction based on contrast-enhanced CT imaging: a multicenter study : Original research.

BACKGROUND: This study aimed to explore the incidence of occult lymph node metastasis (OLM) in clinical T1 - 2N0M0 (cT1 - 2N0M0) small cell lung cancer (SCLC) patients and develop machine learning prediction models using preoperative intratumoral and peritumoral contrast-enhanced CT-based radiomic data. METHODS: By conducting a retrospective analysis involving 242 eligible patients from 4 centeres, we determined the incidence of OLM in cT1 - 2N0M0 SCLC patients. For each lesion, two ROIs were defined using the gross tumour volume (GTV) and peritumoral volume 15 mm around the tumour (PTV). By extracting a comprehensive set of 1595 enhanced CT-based radiomic features individually from the GTV and PTV, five models were constucted and we rigorously evaluated the model performance using various metrics, including the area under the curve (AUC), accuracy, sensitivity, specificity, calibration curve, and decision curve analysis (DCA). For enhanced clinical applicability, we formulated a nomogram that integrates clinical parameters and the rad_score (GTV and PTV). RESULTS: The initial investigation revealed a 33.9% OLM positivity rate in cT1 - 2N0M0 SCLC patients. Our combined model, which incorporates three radiomic features from the GTV and PTV, along with two clinical parameters (smoking status and shape), exhibited robust predictive capabilities. With a peak AUC value of 0.772 in the external validation cohort, the model outperformed the alternative models. The nomogram significantly enhanced diagnostic precision for radiologists and added substantial value to the clinical decision-making process for cT1 - 2N0M0 SCLC patients. CONCLUSIONS: The incidence of OLM in SCLC patients surpassed that in non-small cell lung cancer patients. The combined model demonstrated a notable generalization effect, effectively distinguishing between positive and negative OLMs in a noninvasive manner, thereby guiding individualized clinical decisions for patients with cT1 - 2N0M0 SCLC.

Left ventricular strain changes at high altitude in rats: a cardiac magnetic resonance tissue tracking imaging study.

BACKGROUND: Long-term exposure to a high altitude environment with low pressure and low oxygen could cause abnormalities in the structure and function of the heart. Myocardial strain is a sensitive indicator for assessing myocardial dysfunction, monitoring myocardial strain is of great significance for the early diagnosis and treatment of high altitude heart-related diseases. This study applies cardiac magnetic resonance tissue tracking technology (CMR-TT) to evaluate the changes in left ventricular myocardial function and structure in rats in high altitude environment. METHODS: 6-week-old male rats were randomized into plateau hypoxia rats (plateau group, n = 21) as the experimental group and plain rats (plain group, n = 10) as the control group. plateau group rats were transported from Chengdu (altitude: 360 m), a city in a plateau located in southwestern China, to the Qinghai-Tibet Plateau (altitude: 3850 m), Yushu, China, and then fed for 12 weeks there, while plain group rats were fed in Chengdu(altitude: 360 m), China. Using 7.0 T cardiac magnetic resonance (CMR) to evaluate the left ventricular ejection fraction (EF), end-diastolic volume (EDV), end-systolic volume (ESV) and stroke volume (SV), as well as myocardial strain parameters including the peak global longitudinal (GLS), radial (GRS), and circumferential strain (GCS). The rats were euthanized and a myocardial biopsy was obtained after the magnetic resonance imaging scan. RESULTS: The plateau rats showed more lower left ventricular GLS and GRS (P < 0.05) than the plain rats. However, there was no statistically significant difference in left ventricular EDV, ESV, SV, EF and GCS compared to the plain rats (P > 0.05). CONCLUSIONS: After 12 weeks of exposure to high altitude low-pressure hypoxia environment, the left ventricular global strain was partially decreased and myocardium is damaged, while the whole heart ejection fraction was still preserved, the myocardial strain was more sensitive than the ejection fraction in monitoring cardiac function.

gutMGene: a comprehensive database for target genes of gut microbes and microbial metabolites.

gutMGene (http://bio-annotation.cn/gutmgene), a manually curated database, aims at providing a comprehensive resource of target genes of gut microbes and microbial metabolites in humans and mice. Metagenomic sequencing of fecal samples has identified 3.3 × 106 non-redundant microbial genes from up to 1500 different species. One of the contributions of gut microbiota to host biology is the circulating pool of bacterially derived small-molecule metabolites. It has been estimated that 10% of metabolites found in mammalian blood are derived from the gut microbiota, where they can produce systemic effects on the host through activating or inhibiting gene expression. The current version of gutMGene documents 1331 curated relationships between 332 gut microbes, 207 microbial metabolites and 223 genes in humans, and 2349 curated relationships between 209 gut microbes, 149 microbial metabolites and 544 genes in mice. Each entry in the gutMGene contains detailed information on a relationship between gut microbe, microbial metabolite and target gene, a brief description of the relationship, experiment technology and platform, literature reference and so on. gutMGene provides a user-friendly interface to browse and retrieve each entry using gut microbes, disorders and intervention measures. It also offers the option to download all the entries and submit new experimentally validated associations.

Prognostic and predictive impact of abnormal signal volume evolution early after chemoradiotherapy in glioblastoma.

INTRODUCTION: This study was designed to explore the feasibility of semiautomatic measurement of abnormal signal volume (ASV) in glioblastoma (GBM) patients, and the predictive value of ASV evolution for the survival prognosis after chemoradiotherapy (CRT). METHODS: This retrospective trial included 110 consecutive patients with GBM. MRI metrics, including the orthogonal diameter (OD) of the abnormal signal lesions, the pre-radiation enhancement volume (PRRCE), the volume change rate of enhancement (rCE), and fluid attenuated inversion recovery (rFLAIR) before and after CRT were analyzed. Semi-automatic measurements of ASV were done through the Slicer software. RESULTS: In logistic regression analysis, age (HR = 2.185, p = 0.012), PRRCE (HR = 0.373, p < 0.001), post CE volume (HR = 4.261, p = 0.001), rCE1m (HR = 0.519, p = 0.046) were the significant independent predictors of short overall survival (OS) (< 15.43 months). The areas under the receiver operating characteristic curve (AUCs) for predicting short OS with rFLAIR3m and rCE1m were 0.646 and 0.771, respectively. The AUCs of Model 1 (clinical), Model 2 (clinical + conventional MRI), Model 3 (volume parameters), Model 4 (volume parameters + conventional MRI), and Model 5 (clinical + conventional MRI + volume parameters) for predicting short OS were 0.690, 0.723, 0.877, 0.879, 0.898, respectively. CONCLUSION: Semi-automatic measurement of ASV in GBM patients is feasible. The early evolution of ASV after CRT was beneficial in improving the survival evaluation after CRT. The efficacy of rCE1m was better than that of rFLAIR3m in this evaluation.

Prognostic value of multi b-value DWI in patients with locally advanced rectal cancer.

OBJECTIVES: To evaluate the potential of multi b-value DWI in predicting the prognosis of patients with locally advanced rectal cancer (LARC). METHODS: From 2015 to 2019, a total of 161 patients with LARC were enrolled and randomly sampled into a training set (n = 113) and validation set (n = 48). Multi b-value DWI (b = 0~1500 s/mm2) scans were postprocessed to generate functional parameters, including apparent diffusion coefficient (ADC), Dt, Dp, f, distributed diffusion coefficient (DDC), and α. Histogram features of each functional parameter were submitted into Least absolute shrinkage and selection operator (LASSO) and stepwise multivariate COX analysis to generate DWI_score based on the training set. The prognostic model was constructed with functional parameter, DWI_score, and clinicopathologic factors by using univariate and multivariate COX analysis on the training set and verified on the validation set. RESULTS: Multivariate COX analysis revealed that DWI_score was an independent indicator for 5-year progression-free survival (PFS, HR = 5.573, p < 0.001), but not for overall survival (OS, HR = 2.177, p = 0.051). No mean value of functional parameters was correlated with PFS or OS. Prognostic model for 5-year PFS based on DWI_score, TNM-stage, mesorectal fascia (MRF), and extramural venous invasion (EMVI) showed good performance both in the training set (AUC = 0.819) and validation set (AUC = 0.815). CONCLUSIONS: The DWI_score based on histogram features of multi b-value DWI functional parameters was an independent factor for PFS of LARC and the prognostic model with a combination of DWI_score and clinicopathologic factors could indicate the progression risk before treatment. KEY POINTS: • Mean value of functional parameters obtained from multi b-value DWI might not be useful to assess the prognosis of LARC. • The DWI_score based on histogram features of multi b-value DWI functional parameters was an independent prognosis factor for PFS of LARC. • Prognostic model based on DWI_score and clinicopathologic factors could indicate the progression risk of LARC before treatment.

Risk prediction of preoperative acute ischemic stroke in acute type A aortic dissection.

OBJECTIVES: To predict preoperative acute ischemic stroke (AIS) in acute type A aortic dissection (ATAAD). METHODS: In this multi-center retrospective study, 508 consecutive patients diagnosed as ATAAD between April 2020 and March 2021 were considered for inclusion. The patients were divided into a development cohort and two validation cohorts based on time periods and centers. Clinical data and imaging findings obtained were analyzed. Univariable and multivariable logistic regression analyses were performed to identify predictors associated with preoperative AIS. The performance of resulting nomogram was evaluated in discrimination and calibration on all cohorts. RESULTS: A total of 224 patients were in the development cohort, 94 in the temporal validation cohort, and 118 in the geographical validation cohort. Six predictors were identified: age, syncope, D-dimer, moderate to severe aortic valve insufficiency, diameter ratio of true lumen in ascending aorta < 0.33, and common carotid artery dissection. The nomogram established showed good discrimination (area under the receiver operating characteristic curve [AUC], 0.803; 95% CI: 0.742, 0.864) and calibration (Hosmer-Lemeshow test p = 0.300) in the development cohort. External validation showed good discrimination and calibration abilities in both temporal (AUC, 0.778; 95% CI: 0.671, 0.885; Hosmer-Lemeshow test p = 0.161) and geographical cohort (AUC, 0.806; 95% CI: 0.717, 0.895; Hosmer-Lemeshow test p = 0.100). CONCLUSIONS: A nomogram, based on simple imaging and clinical variables collected on admission, showed good discrimination and calibration abilities in predicting preoperative AIS for ATAAD patients. KEY POINTS: • A nomogram based on simple imaging and clinical findings may predict preoperative acute ischemic stroke in patients with acute type A aortic dissection in emergencies. • The nomogram showed good discrimination and calibration abilities in validation cohorts.

The Plasma Concentration of Ticagrelor and Aspirin as a Predictor of Bleeding Complications in Chinese Acute Coronary Syndrome Patients With Dual Antiplatelet Therapy: A Prospective Observational Study.

PURPOSE: This study evaluated the association among the plasma concentration of ticagrelor, ARC124910XX, aspirin, and salicylic acid with the risk of recent bleeding in patients with the acute coronary syndrome. To this end, we developed an accurate model to predict bleeding. METHODS: A total of 84 patients included in this study cohort between May 2021 and November 2021. The risk factors were identified by univariate and multivariate analyses, and statistically significant risk factors identified in the multivariate analysis were included in the nomogram. We used the calibration curve and the receiver operating characteristic curve to verify the accuracy of the prediction model. RESULTS: Multivariable logistic analysis showed that ticagrelor concentration (odds ratio [OR]: 2.47, 95% confidence interval [CI], 1.51-4.75, P = 0.002), ST-segment elevation acute myocardial infarction (OR: 32.2, 95% CI, 2.37-780, P = 0.016), and lipid-lowering drugs (OR: 11.52, 95% CI, 1.91-110, P = 0.015) were positively correlated with bleeding. However, angiotensin-converting enzyme inhibitor/angiotensin II receptor blocker (OR: 0.04, 95% CI, 0.004-0.213, P < 0.001) was negatively correlated with bleeding. The receiver operating characteristic curve analysis showed that ticagrelor concentration and these factors together predict the occurrence of bleeding (area under receiver operating characteristic curve = 0.945, 95% CI, 0.896-0.994) and that ticagrelor concentration >694.90 ng/mL is the threshold of bleeding concentration (area under receiver operating characteristic curve = 0.696, 95% CI, 0.558-0.834). CONCLUSION: In patients with acute coronary syndrome treated with dual antiplatelet therapy, ticagrelor concentration >694.90 ng/mL was an independent risk factor for bleeding (OR: 2.47, 95% CI, 1.51-4.75, P = 0.002), but ARC124910XX and salicylic acid concentration did not affect bleeding risk ( P > 0.05).

A Nomogram of Magnetic Resonance Imaging for Preoperative Assessment of Microvascular Invasion and Prognosis of Hepatocellular Carcinoma.

BACKGROUND: Microvascular invasion (MVI) is a predictor of recurrence and overall survival in hepatocellular carcinoma (HCC), the preoperative diagnosis of MVI through noninvasive methods play an important role in clinical treatment. AIMS: To investigate the effectiveness of radiomics features in evaluating MVI in HCC before surgery. METHODS: We included 190 patients who had undergone contrast-enhanced MRI and curative resection for HCC between September 2015 and November 2021 from two independent institutions. In the training cohort of 117 patients, MVI-related radiomics models based on multiple sequences and multiple regions from MRI were constructed. An independent cohort of 73 patients was used to validate the proposed models. A final Clinical-Imaging-Radiomics nomogram for preoperatively predicting MVI in HCC patients was generated. Recurrence-free survival was analyzed using the log-rank test. RESULTS: For tumor-extracted features, the performance of signatures in fat-suppressed T1-weighted images and hepatobiliary phase was superior to that of other sequences in a single-sequence model. The radiomics signatures demonstrated better discriminatory ability than that of the Clinical-Imaging model for MVI. The nomogram incorporating clinical, imaging and radiomics signature showed excellent predictive ability and achieved well-fitted calibration curves, outperforming both the Radiomics and Clinical-Radiomics models in the training and validation cohorts. CONCLUSIONS: The Clinical-Imaging-Radiomics nomogram model of multiple regions and multiple sequences based on serum alpha-fetoprotein, three MRI characteristics, and 12 radiomics signatures achieved good performance for predicting MVI in HCC patients, which may help clinicians select optimal treatment strategies to improve subsequent clinical outcomes.

Multisequence magnetic resonance imaging-based radiomics models for the prediction of microsatellite instability in endometrial cancer.

PURPOSE: To evaluate the performance of multisequence magnetic resonance imaging (MRI)-based radiomics models in the assessment of microsatellite instability (MSI) status in endometrial cancer (EC). MATERIALS AND METHODS: This retrospective multicentre study included 338 EC patients with available MSI status and preoperative MRI scans, divided into training (37 MSI, 123 microsatellite stability [MSS]), internal validation (15 MSI, 52 MSS), and external validation cohorts (30 MSI, 81 MSS). Radiomics features were extracted from T2-weighted images, diffusion-weighted images, and contrast-enhanced T1-weighted images. The ComBat harmonisation method was applied to remove intrascanner variability. The Boruta wrapper algorithm was used for key feature selection. Three classification algorithms, logistic regression (LR), random forest (RF), and support vector machine (SVM), were applied to build the radiomics models. The area under the receiver operating characteristic curve (AUC) was calculated to compare the diagnostic performance of the models. Decision curve analysis (DCA) was conducted to determine the clinical usefulness of the models. RESULTS: Among the 1980 features, Boruta finally selected nine radiomics features. A higher MSI prediction performance was achieved after running the ComBat harmonisation method. The SVM algorithm had the best performance, with AUCs of 0.921, 0.903, and 0.937 in the training, internal validation, and external validation cohorts, respectively. The DCA results showed that the SVM algorithm achieved higher net benefits than the other classifiers over a threshold range of 0.581-0.783. CONCLUSION: The multisequence MRI-based radiomics models showed promise in preoperatively predicting the MSI status in EC in this multicentre setting.

Multiparametric MRI-based radiomics nomogram for preoperative prediction of lymphovascular invasion and clinical outcomes in patients with breast invasive ductal carcinoma.

OBJECTIVE: To develop a multiparametric MRI-based radiomics nomogram for predicting lymphovascular invasion (LVI) status and clinical outcomes in patients with breast invasive ductal carcinoma (IDC). METHODS: A total of 160 patients with pathologically confirmed breast IDC (training cohort: n = 112; validation cohort: n = 48) who underwent preoperative breast MRI were included. Imaging features were extracted from T2-weighted imaging (T2WI), apparent diffusion coefficient (ADC) maps, and contrast-enhanced T1-weighted imaging (cT1WI) sequences. A four-step procedure was applied for feature selection and radiomics signature building. Univariate and multivariate logistic regression analyses were conducted to identify the features associated with LVI, which were then incorporated into the radiomics nomogram. The performance of the nomogram was evaluated by its discrimination, calibration, and clinical usefulness. Kaplan-Meier survival curves based on the two radiomics models were used to estimate disease-free survival (DFS). RESULTS: The fusion radiomics signature of the T2WI, cT1WI, and ADC maps achieved a better predictive efficacy for LVI than either of them alone. The proposed radiomics nomogram, incorporating the fusion radiomics signature and MRI-reported peritumoral edema, showed satisfactory capabilities of calibration and discrimination in both training and validation datasets, with AUCs of 0.919 (95% CI: 0.871-0.967) and 0.863 (95% CI: 0.726-0.999), respectively. The radiomics signature and nomogram-defined high-risk groups had a shorter DFS than those in the low-risk groups (both p < 0.05). Higher Rad-scores were independently associated with a worse DFS in the whole cohort (p < 0.05). CONCLUSIONS: The proposed nomogram, incorporating multiparametric MRI-based radiomics signature and MRI-reported peritumoral edema, achieved a satisfactory preoperative prediction of LVI and clinical outcomes in IDC patients. KEY POINTS: • The fusion radiomics signature of the T2WI, cT1WI, and ADC maps achieved a better predictive efficacy for LVI than either of them alone. • The proposed nomogram achieved a favorable prediction of LVI in IDC patients with AUCs of 0.919 and 0.863 in the training and validation datasets, respectively. • The radiomics model could classify patients into high- and low-risk groups with significant differences in DFS.

Deep learning predicts resistance to neoadjuvant chemotherapy for locally advanced gastric cancer: a multicenter study.

BACKGROUND: Accurate pre-treatment prediction of neoadjuvant chemotherapy (NACT) resistance in patients with locally advanced gastric cancer (LAGC) is essential for timely surgeries and optimized treatments. We aim to evaluate the effectiveness of deep learning (DL) on computed tomography (CT) images in predicting NACT resistance in LAGC patients. METHODS: A total of 633 LAGC patients receiving NACT from three hospitals were included in this retrospective study. The training and internal validation cohorts were randomly selected from center 1, comprising 242 and 104 patients, respectively. The external validation cohort 1 comprised 128 patients from center 2, and the external validation cohort 2 comprised 159 patients from center 3. First, a DL model was developed using ResNet-50 to predict NACT resistance in LAGC patients, and the gradient-weighted class activation mapping (Grad-CAM) was assessed for visualization. Then, an integrated model was constructed by combing the DL signature and clinical characteristics. Finally, the performance was tested in internal and external validation cohorts using area under the receiver operating characteristic (ROC) curves (AUC). RESULTS: The DL model achieved AUCs of 0.808 (95% CI 0.724-0.893), 0.755 (95% CI 0.660-0.850), and 0.752 (95% CI 0.678-0.825) in validation cohorts, respectively, which were higher than those of the clinical model. Furthermore, the integrated model performed significantly better than the clinical model (P < 0.05). CONCLUSIONS: A CT-based model using DL showed promising performance for predicting NACT resistance in LAGC patients, which could provide valuable information in terms of individualized treatment.

Can lymphovascular invasion be predicted by contrast-enhanced CT imaging features in patients with esophageal squamous cell carcinoma? A preliminary retrospective study.

BACKGROUND: To investigate the value of contrast-enhanced CT (CECT)-derived imaging features in predicting lymphovascular invasion (LVI) status in esophageal squamous cell carcinoma (ESCC) patients. METHODS: One hundred and ninety-seven patients with postoperative pathologically confirmed esophageal squamous cell carcinoma treated in our hospital between January 2017 and January 2019 were enrolled in our study, including fifty-nine patients with LVI and one hundred and thirty-eight patients without LVI. The CECT-derived imaging features of all patients were analyzed. The CECT-derived imaging features were divided into quantitative features and qualitative features. The quantitative features consisted of the CT attenuation value of the tumor (CTVTumor), the CT attenuation value of the normal esophageal wall (CTVNormal), the CT attenuation value ratio of the tumor-to-normal esophageal wall (TNR), the CT attenuation value difference between the tumor and normal esophageal wall (ΔTN), the maximum thickness of the tumor measured by CECT (Thickness), the maximum length of the tumor measured by CECT (Length), and the gross tumor volume measured by CECT (GTV). The qualitative features consisted of an enhancement pattern, tumor margin, enlarged blood supply or drainage vessels to the tumor (EVFDT), and tumor necrosis. For the clinicopathological characteristics and CECT-derived imaging feature analysis, the chi-squared test was used for categorical variables, the Mann-Whitney U test was used for continuous variables with a nonnormal distribution, and the independent sample t-test was used for the continuous variables with a normal distribution. The trend test was used for ordinal variables. The association between LVI status and CECT-derived imaging features was analyzed by univariable logistic analysis, followed by multivariable logistic regression and receiver operating characteristic (ROC) curve analysis. RESULTS: The CTVTumor, TNR, ΔTN, Thickness, Length, and GTV in the group with LVI were higher than those in the group without LVI (P < 0.05). A higher proportion of patients with heterogeneous enhancement pattern, irregular tumor margin, EVFDT, and tumor necrosis were present in the group with LVI (P < 0.05). As revealed by the univariable logistic analysis, the CECT-derived imaging features, including CTVTumor, TNR, ΔTN and enhancement pattern, Thickness, Length, GTV, tumor margin, EVFDT, and tumor necrosis were associated with LVI status (P < 0.05). Only the TNR (OR 8.655; 95% CI 2.125-37.776), Thickness (OR 6.531; 95% CI 2.410-20.608), and tumor margin (OR 4.384; 95% CI 2.004-9.717) were independent risk factors for LVI in the multivariable logistic regression analysis. The ROC curve analysis incorporating the above three CECT-derived imaging features showed that the area under the curve obtained by the multivariable logistic regression model was 0.820 (95% CI 0.754-0.885). CONCLUSION: The CECT-derived imaging features, including TNR, Thickness, tumor margin, and their combination, can be used as predictors of LVI status for patients with ESCC.

Multiparametric MR radiomics in brain glioma: models comparation to predict biomarker status.

BACKGROUND: Genotype status of glioma have important significance to clinical treatment and prognosis. At present, there are few studies on the prediction of multiple genotype status in glioma by method of multi-sequence radiomics. The purpose of the study is to compare the performance of clinical features (age, sex, WHO grade, MRI morphological features etc.), radiomics features from multi MR sequence (T2WI, T1WI, DWI, ADC, CE-MRI (contrast enhancement)), and a combined multiple features model in predicting biomarker status (IDH, MGMT, TERT, 1p/19q of glioma. METHODS: In this retrospective analysis, 81 glioma patients confirmed by histology were enrolled in this study. Five MRI sequences were used for radiomic feature extraction. Finally, 107 features were extracted from each sequence on Pyradiomics software, separately. These included 18 first-order metrics, such as the mean, standard deviation, skewness, and kurtosis etc., 14 shape features and second-order metrics including 24 grey level run length matrix (GLCM), 16 grey level run length matrix (GLRLM), 16 grey level size zone matrix (GLSZM), 5 neighboring gray tone difference matrix (NGTDM), and 14 grey level dependence matrix (GLDM). Then, Univariate analysis and LASSO (Least absolute shrinkage and selection operator regression model were used to data dimension reduction, feature selection, and radiomics signature building. Significant features (p < 0.05 by multivariate logistic regression were retained to establish clinical model, T1WI model, T2WI model, T1 + C (T1WI contrast enhancement model, DWI model and ADC model, multi sequence model. Clinical features were combined with multi sequence model to establish a combined model. The predictive performance was validated by receiver operating characteristic curve (ROC analysis and decision curve analysis (DCA). RESULTS: The combined model showed the better performance in some groups of genotype status among some models (IDH AUC = 0.93, MGMT AUC = 0.88, TERT AUC = 0.76). Multi sequence model performed better than single sequence model in IDH, MGMT, TERT. There was no significant difference among the models in predicting 1p/19q status. Decision curve analysis showed combined model has higher clinical benefit than multi sequence model. CONCLUSION: Multi sequence model is an effective method to identify the genotype status of cerebral glioma. Combined with clinical models can better distinguish genotype status of glioma. KEY POINTS: The combined model showed the higher performance compare with other models in predicting genotype status of IDH, MGMT, TERT. Multi sequence model showed a better predictive model than that of a single sequence model. Compared with other models, the combined model and multi sequence model show no advantage in prediction of 1p/19q status.

Nomogram based on MRI can preoperatively predict brain invasion in meningioma.

Predicting brain invasion preoperatively should help to guide surgical decision-making and aid the prediction of meningioma grading and prognosis. However, only a few imaging features have been identified to aid prediction. This study aimed to develop and validate an MRI-based nomogram to predict brain invasion by meningioma. In this retrospective study, 658 patients were examined via routine MRI before undergoing surgery and were diagnosed with meningioma by histopathology. Least absolute shrinkage and selection operator (LASSO) regularization was used to determine the optimal combination of clinical characteristics and MRI features for predicting brain invasion by meningiomas. Logistic regression and receiver operating characteristic (ROC) curve analyses were used to determine the discriminatory ability. Furthermore, a nomogram was constructed using the optimal MRI features, and decision curve analysis was used to validate the clinical usefulness of the nomogram. Eighty-one patients with brain invasion and 577 patients without invasion were enrolled. According to LASSO regularization, tumour shape, tumour boundary, peritumoral oedema, and maximum diameter were independent predictors of brain invasion. The model showed good discriminatory ability for predicting brain invasion in meningiomas, with an AUC of 0.905 (95% CI, 0.871-0.940) vs 0.898 (95% CI, 0.849-0.947) and sensitivity of 93.0% vs 92.6% in the training vs validation cohorts. Our predictive model based on MRI features showed good performance and high sensitivity for predicting the risk of brain invasion in meningiomas and can be applied in the clinical setting.

Radiomics based on multisequence magnetic resonance imaging for the preoperative prediction of peritoneal metastasis in ovarian cancer.

OBJECTIVES: To develop a radiomics signature based on multisequence magnetic resonance imaging (MRI) to preoperatively predict peritoneal metastasis (PM) in ovarian cancer (OC). METHODS: Eighty-nine patients with OC were divided into a training cohort including patients (n = 54) with a single lesion and a validation cohort including patients (n = 35) with bilateral lesions. Radiomics features were extracted from the T2-weighted images (T2WIs), fat-suppressed T2WIs, multi-b-value diffusion-weighted images (DWIs), and corresponding parametric maps. A radiomics signature and nomogram incorporating the radiomics signature and clinical predictors were developed and validated on the training and validation cohorts, respectively. RESULTS: The radiomics signature generated by 6 selected features showed a favorable discriminatory ability to predict PM in OC with an area under the curve (AUC) of 0.963 in the training cohort and an AUC of 0.928 in the validation cohort. The nomogram, comprising the radiomics signature, pelvic fluid, and CA-125 level, showed more favorable discrimination with an AUC of 0.969 in the training cohort and 0.944 in the validation cohort. Net reclassification index with values of 0.548 in the training cohort and 0.500 in the validation cohort. CONCLUSION: Radiomics signature based on multisequence MRI serves as an effective quantitative approach to predict PM in OC patients. A nomogram of radiomics signature and clinical predictors could further improve the prediction ability of PM in patients with OC. KEY POINTS: • Multisequence MRI-based radiomics showed a favorable discriminatory ability to predict PM in OC. • The nomogram incorporating the radiomics signature and clinical predictors was clinically useful to preoperatively predict PM in patients with OC.

Radiomics derived from dynamic contrast-enhanced MRI pharmacokinetic protocol features: the value of precision diagnosis ovarian neoplasms.

OBJECTIVES: To evaluate the efficiency of 2- and 3-class classification predictive tasks constructed from radiomics features extracted from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) pharmacokinetic (PK) protocol in discriminating among benign, borderline, and malignant ovarian tumors. METHODS: One hundred and four ovarian lesions were evaluated using preoperative DCE-MRI. Radiomics features were extracted from 7 types of DCE-MR images. To explore the differential ability of radiomics between three types of ovarian tumors, two- and three-class classification tasks were established. The 2-class classification task was divided into three subtasks: benign vs. borderline (task A), benign vs. malignant (task B), and borderline vs. malignant (task C). For the 3-class classification task, 104 lesions were randomly divided into training (72 lesions) and validation (32 lesions) cohorts. The discrimination abilities of the radiomics signatures were established with the training cohort and tested with the independent validation cohort. The predictive performance of the task was evaluated by receiver operating characteristic (ROC) curve, calibration curve analysis, and decision curve analysis (DCA). RESULTS: For the 2-class classification task, the combination of PK radiomics signatures model (PK model) showed a good diagnostic ability with the highest area under the ROC curves (AUCs) of 0.899, 0.865, and 0.893 for tasks A, B, and C, respectively. Additionally, the 3-class classification task demonstrated a good discrimination performance with AUCs of 0.893, 0.944, and 0.891 for the benign, borderline, and malignant groups, respectively. CONCLUSIONS: Radiomics analysis based on the DCE-MRI PK protocol showed promise for discriminating among benign, borderline, and malignant ovarian tumors. KEY POINTS: • Two-class classification predictive task of DCE-MRI PK protocol enabled the classification of 3 categories of ovarian tumors through the pairwise comparison strategy with a perfect diagnostic ability. • Three-class classification predictive task maintained good performance to effectively judge each category of ovarian tumors directly.

MR imaging of thymomas: a combined radiomics nomogram to predict histologic subtypes.

OBJECTIVES: Accurately predicting the WHO classification of thymomas is urgently needed to optimize individualized therapeutic strategies. We aimed to develop and validate a combined radiomics nomogram for personalized prediction of histologic subtypes in patients with thymomas. METHODS: A total of 182 thymoma patients were divided into training (n = 128) and test (n = 54) cohorts. Radiomics features were extracted from T2-weighted, T2-weighted fat suppression, and diffusion-weighted images to establish a radiomics signature in the training cohort. Multivariate logistic regression analysis was used to develop a combined radiomics nomogram that incorporated clinical, conventional MR imaging variables, apparent diffusion coefficient (ADC) value, and radiomics signature. The efficacy of clinical, conventional MR imaging, or ADC model was also evaluated respectively. The performances of different models were compared by receiver operating characteristic analysis and Delong test. The discrimination, calibration, and clinical usefulness of the combined radiomics nomogram were assessed. RESULTS: The radiomics signature, consisting of 14 features, achieved favorable predictive efficacy in differentiating low-risk from high-risk thymomas, outperforming clinical, conventional MR imaging, and ADC models. The combined radiomics nomogram incorporating tumor shape, ADC value, and radiomics signature yielded the best performance (training cohort: area under the curve [AUC] = 0.946, test cohort: AUC = 0.878). The calibration curve and decision curve analysis indicated the clinical utility of the combined radiomics nomogram. CONCLUSIONS: The radiomics signature is a useful tool that can be used to predict histologic subtypes of thymomas. The combined radiomics nomogram improved the individualized subtype prediction in patients with thymomas. KEY POINTS: • Fourteen robust features were selected to develop a radiomics signature for preoperative prediction of thymoma subtype. • MRI-based radiomics signature can differentiate low-risk thymomas from high-risk thymomas with favorable predictive efficacy compared with clinical, conventional MR imaging, and ADC models. • Combined radiomics nomogram based on tumor shape, ADC value, and radiomics signature could improve the individualized subtype prediction in patients with thymomas.

Intravoxel Incoherent Motion Imaging in Differentiation Borderline From Malignant Ovarian Epithelial Tumors: Correlation With Histological Cell Proliferation and Vessel Characteristics.

BACKGROUND: The differentiation of borderline from malignant ovarian epithelial tumors (OETs) is difficult based on morphologic characteristics. Diffusion and perfusion information from intravoxel incoherent motion (IVIM) diffusion-weighted imaging (DWI) might be useful for this distinction. PURPOSE: To investigate the potential of IVIM-DWI in discriminating borderline from malignant OETs by correlating with cell proliferation and microvessel density (MVD). STUDY TYPE: Prospective. SUBJECTS: Sixty-six patients with OETs (22 borderline, BOETs; 44 malignant, MOETs) underwent IVIM-DWI before surgery. FIELD STRENGTH: 3.0T IVIM-DWI sequence with 12 b-values (0-1000 sec/mm2 ). ASSESSMENT: Apparent diffusion coefficient (ADC) and IVIM-DWI parameters (diffusion coefficient [D], microvascular volume fraction [f], and pseudodiffusion coefficient [D*]) were measured. Cell proliferation and MVD was determined by immunohistochemical staining of Ki-67 and CD-31, respectively. STATISTICAL TESTS: Mann-Whitney U-test; two-sample t-test; intraclass correlation coefficient; Bland-Altman analysis; receiver operating characteristics (ROC) curves; and Spearman correlation. RESULTS: ADC and D in BOETs was significantly higher than those in MOETs (P < 0.001), while f in BOETs was significantly lower than those in MOETs (P < 0.001). No significant difference was found in D* between borderline and malignancies (P = 0.324). In the differential diagnosis of borderline from malignant OETs; D demonstrated the highest area under the curve (AUC) of 0.951, while ADC and f had a lower AUC of 0.921 and 0.847, respectively. The ADC and D was negatively correlated with cell proliferation (r = -0.682, r = -0.694, respectively, P < 0.001), while f was positively correlated with MVD of the OETs (r = 0.558, P < 0.001). DATA CONCLUSION: IVIM-DWI may be a useful tool for differentiating BOETs from MOETs. D and f could be image biomarkers to reflect histopathological characteristics of cell proliferation and MVD in OETs. LEVEL OF EVIDENCE: 1 Technical Efficacy Stage: 2 J. Magn. Reson. Imaging 2020;51:928-935.

MR imaging of epithelial ovarian cancer: a combined model to predict histologic subtypes.

OBJECTIVE: To compare the performance of clinical features, conventional MR image features, ADC value, T2WI, DWI, DCE-MRI radiomics, and a combined multiple features model in predicting the type of epithelial ovarian cancer (EOC). METHODS: In this retrospective analysis, 61 EOC patients were confirmed by histology. Significant features (p < 0.05) by multivariate logistic regression were retained to establish a clinical model, conventional MRI morphological model, ADC model, and traditional model. The radiomics model included FS-T2WI, DWI, and DCE-MRI, and also, a multisequence model was established. A total of 1070 radiomics features of each sequence were extracted; then, univariate analysis and LASSO were used to select important features. Traditional models were combined with a combined radiomics model to establish a mixed model. The predictive performance was validated by receiver operating characteristic curve (ROC) analysis, calibration curve, and decision curve analysis (DCA). A stratified analysis was conducted to compare the differences between the combined radiomics model and the traditional model in identifying early- and late-stage EOC. RESULTS: Traditional models showed the highest performance (AUC = 0.96). The performance of the mixed model (AUC = 0.97) was not significantly different from that of the traditional model. The calibration curve showed that the traditional model had the highest reliability. Stratified analysis showed the potential of the combined radiomics model in the early distinction of the two tumor types. CONCLUSION: The traditional model is an effective tool to distinguish EOC type I/II. Combined radiomics models have the potential to better distinguish EOC types in early FIGO stage disease. KEY POINTS: • The combined radiomics model resulted in a better predictive model than that from a single sequence model. • The traditional model showed higher classification accuracy than the combined radiomics model. • Combined radiomics models have the potential to better distinguish EOC types in early FIGO stage disease.