Leptomeningeal Metastases in IDH-wildtype Glioblastomas Revisited: Comprehensive Analysis of Incidence, Risk Factors, and Prognosis Based on Post-contrast FLAIR.

BACKGROUND: The incidence of leptomeningeal metastases (LM) has been reported diversely. This study aimed to investigate the incidence, risk factors, and prognosis of LM in patients with IDH-wildtype glioblastoma. METHODS: A total of 828 patients with IDH-wildtype glioblastoma were enrolled between 2005 and 2022. Baseline preoperative MRI including post-contrast fluid-attenuated inversion recovery (FLAIR) was used for LM diagnosis. Qualitative and quantitative features, including distance between tumor and subventricular zone (SVZ) and tumor volume by automatic segmentation of the lateral ventricles and tumor, were assessed. Logistic analysis of LM development was performed using clinical, molecular, and imaging data. Survival analysis was performed. RESULTS: The incidence of LM was 11.4%. MGMTp unmethylation (odds ratio [OR] = 1.92, P = 0.014), shorter distance between tumor and SVZ (OR = 0.94, P = 0.010), and larger contrast-enhancing tumor volume (OR = 1.02, P < 0.001) were significantly associated with LM. The overall survival (OS) was significantly shorter in patients with LM than in those without (log-rank test; P < 0.001), with median OS of 12.2 and 18.5 months, respectively. Presence of LM remained an independent prognostic factor for OS in IDH-wildtype glioblastoma (hazard ratio = 1.42, P = 0.011), along with other clinical, molecular, imaging, and surgical prognostic factors. CONCLUSION: The incidence of LM is high in patients with IDH-wildtype glioblastoma, and aggressive molecular and imaging factors are correlated with LM development. The prognostic significance of LM based on post-contrast FLAIR imaging suggests acknowledgement of post-contrast FLAIR as a reliable diagnostic tool for clinicians.

Impaired Glymphatic Flow on Diffusion Tensor MRI as a Marker of Neurodegeneration in Alzheimer's Disease: Correlation with Gray Matter Volume Loss and Cognitive Decline Independent of Cerebral Amyloid Deposition.

Background: Impaired glymphatic flow on the Alzheimer's disease (AD) spectrum may be evaluated using diffusion tensor image analysis along the perivascular space (DTI-ALPS). Objective: We aimed to validate impaired glymphatic flow and explore its association with gray matter volume, cognitive status, and cerebral amyloid deposition on the AD spectrum. Methods: 80 participants (mean age, 76.9±8.5 years; 57 women) with AD (n = 65) and cognitively normal (CN) (n = 15) who underwent 3T brain MRI including DTI and/or amyloid PET were included. After adjusting for age, sex, apolipoprotein E status, and burden of white matter hyperintensities, the ALPS-index was compared according to the AD spectrum. The association between the ALPS-index and gray matter volume, cognitive status, and quantitative amyloid from PET was assessed. Results: The ALPS-index in the AD was significantly lower (mean, 1.476; 95% CI, 1.395-1.556) than in the CN (1.784;1.615-1.952; p = 0.026). Volumes of the entorhinal cortex, hippocampus, temporal pole, and primary motor cortex showed significant associations with the ALPS-index (all, p < 0.05). There was a positive correlation between the ALPS-index and MMSE score (partial r = 0.435; p < 0.001), but there was no significant correlation between the ALPS-index and amyloid SUVRs (all, p > 0.05). Conclusions: Decreased glymphatic flow measured by DTI-ALPS in AD may serve as a marker of neurodegeneration correlating with structural atrophy and cognitive decline.

Diffusion- and Perfusion-Weighted MRI Radiomics for Survival Prediction in Patients with Lower-Grade Gliomas.

PURPOSE: Lower-grade gliomas of histologic grades 2 and 3 follow heterogenous clinical outcomes, which necessitates risk stratification. This study aimed to evaluate whether diffusion-weighted and perfusion-weighted MRI radiomics allow overall survival (OS) prediction in patients with lower-grade gliomas and investigate its prognostic value. MATERIALS AND METHODS: In this retrospective study, radiomic features were extracted from apparent diffusion coefficient, relative cerebral blood volume map, and Ktrans map in patients with pathologically confirmed lower-grade gliomas (January 2012-February 2019). The radiomics risk score (RRS) calculated from selected features constituted a radiomics model. Multivariable Cox regression analysis, including clinical features and RRS, was performed. The models' integrated area under the receiver operating characteristic curves (iAUCs) were compared. The radiomics model combined with clinical features was presented as a nomogram. RESULTS: The study included 129 patients (median age, 44 years; interquartile range, 37-57 years; 63 female): 90 patients for training set and 39 patients for test set. The RRS was an independent risk factor for OS with a hazard ratio of 6.01. The combined clinical and radiomics model achieved superior performance for OS prediction compared to the clinical model in both training (iAUC, 0.82 vs. 0.72, p=0.002) and test sets (0.88 vs. 0.76, p=0.04). The radiomics nomogram combined with clinical features exhibited good agreement between the actual and predicted OS with C-index of 0.83 and 0.87 in the training and test sets, respectively. CONCLUSION: Adding diffusion- and perfusion-weighted MRI radiomics to clinical features improved survival prediction in lower-grade glioma.

Deep learning-based metastasis detection in patients with lung cancer to enhance reproducibility and reduce workload in brain metastasis screening with MRI: a multi-center study.

OBJECTIVES: To assess whether a deep learning-based system (DLS) with black-blood imaging for brain metastasis (BM) improves the diagnostic workflow in a multi-center setting. MATERIALS AND METHODS: In this retrospective study, a DLS was developed in 101 patients and validated on 264 consecutive patients (with lung cancer) having newly developed BM from two tertiary university hospitals, which performed black-blood imaging between January 2020 and April 2021. Four neuroradiologists independently evaluated BM either with segmented masks and BM counts provided (with DLS) or not provided (without DLS) on a clinical trial imaging management system (CTIMS). To assess reading reproducibility, BM count agreement between the readers and the reference standard were calculated using limits of agreement (LoA). Readers' workload was assessed with reading time, which was automatically measured on CTIMS, and were compared between with and without DLS using linear mixed models considering the imaging center. RESULTS: In the validation cohort, the detection sensitivity and positive predictive value of the DLS were 90.2% (95% confidence interval [CI]: 88.1-92.2) and 88.2% (95% CI: 85.7-90.4), respectively. The difference between the readers and the reference counts was larger without DLS (LoA: -0.281, 95% CI: -2.888, 2.325) than with DLS (LoA: -0.163, 95% CI: -2.692, 2.367). The reading time was reduced from mean 66.9 s (interquartile range: 43.2-90.6) to 57.3 s (interquartile range: 33.6-81.0) (P <.001) in the with DLS group, regardless of the imaging center. CONCLUSION: Deep learning-based BM detection and counting with black-blood imaging improved reproducibility and reduced reading time, on multi-center validation.

Comparison of elasticity changes in the paraspinal muscles of adolescent patients with scoliosis treated with surgery and bracing.

Scoliosis is a three-dimensional spinal deformity, and paraspinal muscles play an important role as stabilizers of the spinal curve. In this prospective study, we compared elasticity changes in the paraspinal muscles of adolescent patients with scoliosis after surgery or bracing. Elasticity was measured on the concave and convex sides of the paraspinal muscles at the apex of the curve at the beginning of treatment and 6 and 12 months after treatment. Twenty-six patients with correction surgery (n = 15) or bracing (n = 11) were included. At initial evaluation, the Cobb angle was larger in the surgery group (72.3 ± 20.2° in surgery vs. 30.6 ± 5.1° in brace, p < 0.001). The estimated mean elasticity value of the paraspinal muscles was lower in the surgery group at baseline on the convex side (15.8 vs. 22.8 kPa, p = 0.037) and 6 months on both the concave (12.1 vs. 22.7 kPa, p = 0.004) and convex (13.4 vs. 23.8 kPa, p = 0.005) sides. There was a significant stiffness decrease from baseline to 6 months on the concave side in the surgery group (5.9 kPa, p = 0.025). However, the elasticity change recovered at 12 months without significant differences between the two groups.

Computed Tomography Radiomics for Preoperative Prediction of Spread Through Air Spaces in the Early Stage of Surgically Resected Lung Adenocarcinomas.

PURPOSE: To assess the added value of radiomics models from preoperative chest CT in predicting the presence of spread through air spaces (STAS) in the early stage of surgically resected lung adenocarcinomas using multiple validation datasets. MATERIALS AND METHODS: This retrospective study included 550 early-stage surgically resected lung adenocarcinomas in 521 patients, classified into training, test, internal validation, and temporal validation sets (n=211, 90, 91, and 158, respectively). Radiomics features were extracted from the segmented tumors on preoperative chest CT, and a radiomics score (Rad-score) was calculated to predict the presence of STAS. Diagnostic performance of the conventional model and the combined model, based on a combination of conventional and radiomics features, for the diagnosis of the presence of STAS were compared using the area under the curve (AUC) of the receiver operating characteristic curve. RESULTS: Rad-score was significantly higher in the STAS-positive group compared to the STAS-negative group in the training, test, internal, and temporal validation sets. The performance of the combined model was significantly higher than that of the conventional model in the training set {AUC: 0.784 [95% confidence interval (CI): 0.722-0.846] vs. AUC: 0.815 (95% CI: 0.759-0.872), p=0.042}. In the temporal validation set, the combined model showed a significantly higher AUC than that of the conventional model (p=0.001). The combined model showed a higher AUC than the conventional model in the test and internal validation sets, albeit with no statistical significance. CONCLUSION: A quantitative CT radiomics model can assist in the non-invasive prediction of the presence of STAS in the early stage of lung adenocarcinomas.

Assessment of Feasibility and Interscan Variability of Short-time Cardiac MRI for Cardiotoxicity Evaluation in Breast Cancer.

Purpose To investigate the feasibility and interscan variability of short-time cardiac MRI protocol after chemotherapy in individuals with breast cancer. Materials and Methods A total of 13 healthy female controls (mean age, 52.4 years ± 13.2 [SD]) and 85 female participants with breast cancer (mean age, 51.8 years ± 9.9) undergoing chemotherapy prospectively underwent routine breast MRI and short-time cardiac MRI using a 3-T scanner with peripheral pulse gating in the prone position. Interscan, intercoil, and interobserver reproducibility and variability of native T1 and extracellular volume (ECV), as well as ventricular functional parameters, were measured using the intraclass correlation coefficient (ICC), standard error of measurement (SEM), or coefficient of variation (CoV). Results Left ventricular functional parameters had excellent interscan reproducibility (ICC ≥ 0.80). Left ventricular ejection fraction showed low interscan variability in control and chemotherapy participants (SEM, 2.0 and 1.2; CoV, 3.1 and 1.9, respectively). Native T1 showed excellent interscan (ICC, 0.75) and intercoil (ICC, 0.81) reproducibility in the control group and good interscan reproducibility (ICC, 0.72 and 0.73, respectively) in the participants undergoing immediate and remote chemotherapy. Interscan reproducibility for ECV was excellent in the control group and in the remote chemotherapy group (ICC, 0.93 and 0.88, respectively) and fair in the immediate chemotherapy group (ICC, 0.52). In the regional analysis, interscan repeatability and variability of native T1 and ECV were superior in the anteroseptum or inferoseptum than in other segments in the immediate chemotherapy group. Native T1 and ECV had good to excellent interobserver agreement across all groups. Conclusion Short-time cardiac MRI showed excellent results for interscan, intercoil, and interobserver reproducibility and variability for ventricular functional or tissue characterization parameters, suggesting that this modality is feasible for routine surveillance of cardiotoxicity evaluation in individuals with breast cancer. Keywords: Cardiac MRI, Heart, Cardiomyopathy ClinicalTrials.gov registration no. NCT03301389 Supplemental material is available for this article. © RSNA, 2024.

Screening in Patients With Dense Breasts: Comparison of Mammography, Artificial Intelligence, and Supplementary Ultrasound.

BACKGROUND. Screening mammography has decreased performance in patients with dense breasts. Supplementary screening ultrasound is a recommended option in such patients, although it has yielded mixed results in prior investigations. OBJECTIVE. The purpose of this article is to compare the performance characteristics of screening mammography alone, standalone artificial intelligence (AI), ultrasound alone, and mammography in combination with AI and/or ultrasound in patients with dense breasts. METHODS. This retrospective study included 1325 women (mean age, 53 years) with dense breasts who underwent both screening mammography and supplementary breast ultrasound within a 1-month interval from January 2017 to December 2017; prior mammography and prior ultrasound examinations were available for comparison in 91.2% and 91.8%, respectively. Mammography and ultrasound examinations were interpreted by one of 15 radiologists (five staff; 10 fellows); clinical reports were used for the present analysis. A commercial AI tool was used to retrospectively evaluate mammographic examinations for presence of cancer. Screening performances were compared among mammography, AI, ultrasound, and test combinations, using generalized estimating equations. Benign diagnoses required 24 months or longer of imaging stability. RESULTS. Twelve cancers (six invasive ductal carcinoma; six ductal carcinoma in situ) were diagnosed. Mammography, standalone AI, and ultrasound showed cancer detection rates (per 1000 patients) of 6.0, 6.8, and 6.0 (all p > .05); recall rates of 4.4%, 11.9%, and 9.2% (all p < .05); sensitivity of 66.7%, 75.0%, and 66.7% (all p > .05); specificity of 96.2%, 88.7%, and 91.3% (all p < .05); and accuracy of 95.9%, 88.5%, and 91.1% (all p < .05). Mammography with AI, mammography with ultrasound, and mammography with both ultrasound and AI showed cancer detection rates of 7.5, 9.1, and 9.1 (all p > .05); recall rates of 14.9, 11.7, and 21.4 (all p < .05); sensitivity of 83.3%, 100.0%, and 100.0% (all p > .05); specificity of 85.8%, 89.1%, and 79.4% (all p < .05); and accuracy of 85.7%, 89.2%, and 79.5% (all p < .05). CONCLUSION. Mammography with supplementary ultrasound showed higher accuracy, higher specificity, and lower recall rate in comparison with mammography with AI and in comparison with mammography with both ultrasound and AI. CLINICAL IMPACT. The findings fail to show benefit of AI with respect to screening mammography performed with supplementary breast ultrasound in patients with dense breasts.

Risk Stratification of Pancreatic Ductal Adenocarcinoma Patients Undergoing Curative-Intent Surgery after Neoadjuvant Therapy.

PURPOSE: Clinical prognostic criteria using preoperative factors were not developed for post-neoadjuvant therapy (NAT) surgery of pancreatic ductal adenocarcinoma (PDAC). We aimed to identify preoperative factors associated with overall survival (OS) in PDAC patients who underwent post-NAT curative-intent surgery and develop risk stratification criteria. MATERIALS AND METHODS: Consecutive PDAC patients who underwent post-NAT curative-intent surgeries between 2007 and 2020 were retrospectively analyzed. Demographic, laboratory, surgical, and histopathologic variables were collected. Baseline, preoperative, and interval changes of computed tomography (CT) findings proposed by the Society of Abdominal Radiology and the American Pancreatic Association were analyzed. Cox proportional hazard analysis was used to select preoperative variables associated with OS. We developed risk stratification criteria composed of the significant preoperative variables, i.e., post-NAT response criteria. We compared the discrimination performance of post-NAT response criteria with that of post-NAT pathological (yp) American Joint Cancer Committee TNM staging system. RESULTS: One hundred forty-five PDAC patients were included. Stable or increased tumor size on CT (hazard ratio [HR], 2.58; 95% confidence interval [CI], 1.58 to 4.21; p < 0.001) and elevated preoperative carbohydrate antigen 19-9 (CA19-9) level (HR, 1.98; 95% CI, 1.11 to 3.55; p=0.021) were independent factors of OS. The OS of the patient groups stratified by post-NAT response criteria which combined changes in tumor size and CA19-9 showed significant difference (p < 0.001). Such stratification was comparable to ypTNM staging in discrimination performance (difference of C-index, 0.068; 95% CI, -0.012 to 0.142). CONCLUSION: "Any degree of decrease in tumor size on CT" and CA19-9 normalization or staying normal were independent favorable factors of OS. The combination of the two factors discriminated OS comparably to ypTNM staging.

Rethinking extent of resection of contrast-enhancing and non-enhancing tumor: different survival impacts on adult-type diffuse gliomas in 2021 World Health Organization classification.

OBJECTIVES: Extent of resection (EOR) of contrast-enhancing (CE) and non-enhancing (NE) tumors may have different impacts on survival according to types of adult-type diffuse gliomas in the molecular era. This study aimed to evaluate the impact of EOR of CE and NE tumors in glioma according to the 2021 World Health Organization classification. METHODS: This retrospective study included 1193 adult-type diffuse glioma patients diagnosed between 2001 and 2021 (183 oligodendroglioma, 211 isocitrate dehydrogenase [IDH]-mutant astrocytoma, and 799 IDH-wildtype glioblastoma patients) from a single institution. Patients had complete information on IDH mutation, 1p/19q codeletion, and O6-methylguanine-methyltransferase (MGMT) status. Cox survival analyses were performed within each glioma type to assess predictors of overall survival, including clinical, imaging data, histological grade, MGMT status, adjuvant treatment, and EOR of CE and NE tumors. Subgroup analyses were performed in patients with CE tumor. RESULTS: Among 1193 patients, 935 (78.4%) patients had CE tumors. In entire oligodendrogliomas, gross total resection (GTR) of NE tumor was not associated with survival (HR = 0.56, p = 0.223). In 86 (47.0%) oligodendroglioma patients with CE tumor, GTR of CE tumor was the only independent predictor of survival (HR = 0.16, p = 0.004) in multivariable analysis. GTR of CE and NE tumors was independently associated with better survival in IDH-mutant astrocytoma and IDH-wildtype glioblastoma (all ps < 0.05). CONCLUSIONS: GTR of both CE and NE tumors may significantly improve survival within IDH-mutant astrocytomas and IDH-wildtype glioblastomas. In oligodendrogliomas, the EOR of CE tumor may be crucial in survival; aggressive GTR of NE tumor may be unnecessary, whereas GTR of the CE tumor is recommended. CLINICAL RELEVANCE STATEMENT: Surgical strategies on contrast-enhancing (CE) and non-enhancing (NE) tumors should be reassessed considering the different survival outcomes after gross total resection depending on CE and NE tumors in the 2021 World Health Organization classification of adult-type diffuse gliomas. KEY POINTS: The survival impact of extent of resection of contrast-enhancing (CE) and non-enhancing (NE) tumors was evaluated in adult-type diffuse gliomas. Gross total resection of both CE and NE tumors may improve survival in isocitrate dehydrogenase (IDH)-mutant astrocytomas and IDH-wildtype glioblastomas, while only gross total resection of the CE tumor improves survival in oligodendrogliomas. Surgical strategies should be reconsidered according to types in adult-type diffuse gliomas.

Comparison of image quality, contrast administration, and radiation doses in pediatric abdominal dual-layer detector dual-energy CT using propensity score matching analysis.

PURPOSE: To compare the image quality, contrast administration, and radiation dose between single-energy CT (SECT) and dual-energy CT (DECT) in pediatric patients. METHODS: From March to December 2021, children who underwent abdominal SECT or DECT were retrospectively included in this study. The DECT group received 10-30 % less contrast than the routine dose. CT images were obtained at hepatic venous phase using a routine reconstruction method (iDose4). DECT scans were additionally reconstructed with a virtual monoenergetic image (VMI) at 40 and 65 keV. Quantitative image evaluations compared the contrast-to-noise ratio (CNR) and signal-to-noise ratio (SNR) of the liver, portal vein, and pancreas. Qualitative analysis assessed degree of contrast enhancement, lesion or organ conspicuity, image noise, artificiality, and overall image quality. RESULTS: Among 318 patients, 112 (median age, 16 years; 56 in each group) were included after propensity score matching. Compared with the SECT group, DECT group with iDose4 demonstrated lower CNRs and SNRs, while VMI at 40 or 65 keV showed no significant difference. In qualitative analysis, iDose4 produced higher scores on artificiality, and VMI at 40 keV demonstrated superior contrast enhancement and lesion conspicuity in the DECT group. Overall image quality was higher with VMI 65 keV among the DECT patients, and there was no significant difference compared to SECT. The volume CT dose index (CTDIvol) did not differ significantly between the two groups (median, 2.8 mGy vs. 2.9 mGy; p = 0.802). The injected contrast volume was reduced by 10 % in the DECT group. CONCLUSION: Pediatric abdominal DECT with reduced contrast administration showed no significant differences in image quality and radiation dose compared to SECT.

Pretreatment MRI-detected extramural venous invasion as a prognostic and predictive biomarker for neoadjuvant chemoradiotherapy in non-metastatic rectal cancer: a propensity score matched analysis.

OBJECTIVES: This study evaluated pretreatment magnetic resonance imaging (MRI)-detected extramural venous invasion (pmrEMVI) as a predictor of survival after neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC). MATERIALS AND METHODS: Medical records of 1184 patients with rectal adenocarcinoma who underwent TME between January 2011 and December 2016 were reviewed. MRI data were collected from a computerized radiologic database. Cox proportional hazards analysis was used to assess local, systemic recurrence, and disease-free survival risk based on pretreatment MRI-assessed tumor characteristics. After propensity score matching (PSM) for pretreatment MRI features, nCRT therapeutic outcomes according to pmrEMVI status were evaluated. Cox proportional hazards analysis was used to identify risk factors for early recurrence in patients receiving nCRT. RESULTS: Median follow-up was 62.8 months. Among all patients, the presence of pmrEMVI was significantly associated with worse disease-free survival (DFS; HR 1.827, 95% CI 1.285-2.597, p = 0.001) and systemic recurrence (HR 2.080, 95% CI 1.400-3.090, p < 0.001) but not local recurrence. Among patients with pmrEMVI, nCRT provided no benefit for oncological outcomes before or after PSM. Furthermore, pmrEMVI( +) was the only factor associated with early recurrence on multivariate analysis in patients receiving nCRT. CONCLUSIONS: pmrEMVI is a poor prognostic factor for DFS and SR in patients with non-metastatic rectal cancer and also serves as a predictive biomarker of poor DFS and SR following nCRT in LARC. Therefore, for patients who are positive for pmrEMVI, consideration of alternative treatment strategies may be warranted. CLINICAL RELEVANCE STATEMENT: This study demonstrated the usefulness of pmrEMVI as a predictive biomarker for nCRT, which may assist in initial treatment decision-making in patients with non-metastatic rectal cancer. KEY POINTS: • Pretreatment MRI-detected extramural venous invasion (pmrEMVI) was significantly associated with worse disease-free survival and systemic recurrence in patients with non-metastatic rectal cancer. • pmrEMVI is a predictive biomarker of poor DFS following nCRT in patients with LARC. • The presence of pmrEMVI was the only factor associated with early recurrence on multivariate analysis in patients receiving nCRT.

MRI radiomics may predict early tumor recurrence in patients with sinonasal squamous cell carcinoma.

OBJECTIVES: Sinonasal squamous cell carcinoma (SCC) follows a poor prognosis with high tendency for local recurrence. We aimed to evaluate whether MRI radiomics can predict early local failure in sinonasal SCC. METHODS: Sixty-eight consecutive patients with node-negative sinonasal SCC (January 2005-December 2020) were enrolled, allocated to the training (n = 47) and test sets (n = 21). Early local failure, which occurred within 12 months of completion of initial treatment, was the primary endpoint. For clinical features (age, location, treatment modality, and clinical T stage), binary logistic regression analysis was performed. For 186 extracted radiomic features, different feature selections and classifiers were combined to create two prediction models: (1) a pure radiomics model; and (2) a combined model with clinical features and radiomics. The areas under the receiver operating characteristic curves (AUCs) were calculated and compared using DeLong's method. RESULTS: Early local failure occurred in 38.3% (18/47) and 23.8% (5/21) in the training and test sets, respectively. We identified several radiomic features which were strongly associated with early local failure. In the test set, both the best-performing radiomics model and the combined model (clinical + radiomic features) yielded higher AUCs compared to the clinical model (AUC, 0.838 vs. 0.438, p = 0.020; 0.850 vs. 0.438, p = 0.016, respectively). The performances of the best-performing radiomics model and the combined model did not differ significantly (AUC, 0.838 vs. 0.850, p = 0.904). CONCLUSION: MRI radiomics integrated with a machine learning classifier may predict early local failure in patients with sinonasal SCC. CLINICAL RELEVANCE STATEMENT: MRI radiomics intergrated with machine learning classifiers may predict early local failure in sinonasal squamous cell carcinomas more accurately than the clinical model. KEY POINTS: • A subset of radiomic features which showed significant association with early local failure in patients with sinonasal squamous cell carcinomas was identified. • MRI radiomics integrated with machine learning classifiers can predict early local failure with high accuracy, which was validated in the test set (area under the curve = 0.838). • The combined clinical and radiomics model yielded superior performance for early local failure prediction compared to that of the radiomics (area under the curve 0.850 vs. 0.838 in the test set), without a statistically significant difference.

Multiparametric MRI-based radiomics model for predicting human papillomavirus status in oropharyngeal squamous cell carcinoma: optimization using oversampling and machine learning techniques.

OBJECTIVES: To develop and validate a multiparametric MRI-based radiomics model with optimal oversampling and machine learning techniques for predicting human papillomavirus (HPV) status in oropharyngeal squamous cell carcinoma (OPSCC). METHODS: This retrospective, multicenter study included consecutive patients with newly diagnosed and pathologically confirmed OPSCC between January 2017 and December 2020 (110 patients in the training set, 44 patients in the external validation set). A total of 293 radiomics features were extracted from three sequences (T2-weighted images [T2WI], contrast-enhanced T1-weighted images [CE-T1WI], and ADC). Combinations of three feature selection, five oversampling, and 12 machine learning techniques were evaluated to optimize its diagnostic performance. The area under the receiver operating characteristic curve (AUC) of the top five models was validated in the external validation set. RESULTS: A total of 154 patients (59.2 ± 9.1 years; 132 men [85.7%]) were included, and oversampling was employed to account for data imbalance between HPV-positive and HPV-negative OPSCC (86.4% [133/154] vs. 13.6% [21/154]). For the ADC radiomics model, the combination of random oversampling and ridge showed the highest diagnostic performance in the external validation set (AUC, 0.791; 95% CI, 0.775-0.808). The ADC radiomics model showed a higher trend in diagnostic performance compared to the radiomics model using CE-T1WI (AUC, 0.604; 95% CI, 0.590-0.618), T2WI (AUC, 0.695; 95% CI, 0.673-0.717), and a combination of both (AUC, 0.642; 95% CI, 0.626-0.657). CONCLUSIONS: The ADC radiomics model using random oversampling and ridge showed the highest diagnostic performance in predicting the HPV status of OPSCC in the external validation set. CLINICAL RELEVANCE STATEMENT: Among multiple sequences, the ADC radiomics model has a potential for generalizability and applicability in clinical practice. Exploring multiple oversampling and machine learning techniques was a valuable strategy for optimizing radiomics model performance. KEY POINTS: • Previous radiomics studies using multiparametric MRI were conducted at single centers without external validation and had unresolved data imbalances. • Among the ADC, CE-T1WI, and T2WI radiomics models and the ADC histogram models, the ADC radiomics model was the best-performing model for predicting human papillomavirus status in oropharyngeal squamous cell carcinoma. • The ADC radiomics model with the combination of random oversampling and ridge showed the highest diagnostic performance.

Validation of a deep learning-based software for automated analysis of T2 mapping in cardiac magnetic resonance imaging.

Background: The reliability and diagnostic performance of deep learning (DL)-based automated T2 measurements on T2 map of 3.0-T cardiac magnetic resonance imaging (MRI) using multi-institutional datasets have not been investigated. We aimed to evaluate the performance of a DL-based software for measuring automated T2 values from 3.0-T cardiac MRI obtained at two centers. Methods: Eighty-three subjects were retrospectively enrolled from two centers (42 healthy subjects and 41 patients with myocarditis) to validate a commercial DL-based software that was trained to segment the left ventricular myocardium and measure T2 values on T2 mapping sequences. Manual reference T2 values by two experienced radiologists and those calculated by the DL-based software were obtained. The segmentation performance of the DL-based software and the non-inferiority of automated T2 values were assessed compared with the manual reference standard per segment level. The software's performance in detecting elevated T2 values was assessed by calculating the sensitivity, specificity, and accuracy per segment. Results: The average Dice similarity coefficient for segmentation of myocardium on T2 maps was 0.844. The automated T2 values were non-inferior to the manual reference T2 values on a per-segment analysis (45.35 vs. 44.32 ms). The DL-based software exhibited good performance (sensitivity: 83.6-92.8%; specificity: 82.5-92.0%; accuracy: 82.7-92.2%) in detecting elevated T2 values. Conclusions: The DL-based software for automated T2 map analysis yields non-inferior measurements at the per-segment level and good performance for detecting myocardial segments with elevated T2 values compared with manual analysis.

Machine Learning Predicts Pathologic Complete Response to Neoadjuvant Chemotherapy for ER+HER2- Breast Cancer: Integrating Tumoral and Peritumoral MRI Radiomic Features.

BACKGROUND: This study aimed to predict pathologic complete response (pCR) in neoadjuvant chemotherapy for ER+HER2- locally advanced breast cancer (LABC), a subtype with limited treatment response. METHODS: We included 265 ER+HER2- LABC patients (2010-2020) with pre-treatment MRI, neoadjuvant chemotherapy, and confirmed pathology. Using data from January 2016, we divided them into training and validation cohorts. Volumes of interest (VOI) for the tumoral and peritumoral regions were segmented on preoperative MRI from three sequences: T1-weighted early and delayed contrast-enhanced sequences and T2-weighted fat-suppressed sequence (T2FS). We constructed seven machine learning models using tumoral, peritumoral, and combined texture features within and across the sequences, and evaluated their pCR prediction performance using AUC values. RESULTS: The best single sequence model was SVM using a 1 mm tumor-to-peritumor VOI in the early contrast-enhanced phase (AUC = 0.9447). Among the combinations, the top-performing model was K-Nearest Neighbor, using 1 mm tumor-to-peritumor VOI in the early contrast-enhanced phase and 3 mm peritumoral VOI in T2FS (AUC = 0.9631). CONCLUSIONS: We suggest that a combined machine learning model that integrates tumoral and peritumoral radiomic features across different MRI sequences can provide a more accurate pretreatment pCR prediction for neoadjuvant chemotherapy in ER+HER2- LABC.

Three-dimensional topology-based T-index as an indicator of surgical difficulty of partial nephrectomy in patients with small renal mass.

PURPOSE: To accurately describe the three-dimensional topology of renal tumors, our study suggests a new nephrometry scoring system, the T-index, that combines information about intraparenchymal extension and peripherality of the renal tumor. MATERIALS AND METHODS: This study included 113 patients who underwent partial nephrectomy for small clear cell renal cell carcinoma between 2007 and 2014. Manual segmentation of the renal parenchyma, sinus, and tumor was performed using preoperative computed tomography images. The T-index was calculated by adding the reciprocals of the distances from all points on the tumor-parenchyma interface to the renal sinus. Correlations with perioperative factors and the impact of the T-index on postoperative complications were evaluated and compared with existing nephrometry scoring systems (PADUA, RENAL, contact surface area [CSA], and C-index). RESULTS: The mean value of the T-index among the 113 patients was 116.1±100.5 (1/mm). The T-index showed the strongest correlation with perioperative factors compared with other nephrometry scoring systems. The T-index was able to predict the risk for postoperative complications, either overall (p=0.015) or major complications (p=0.030). A predictive model based on the T-index of the overall postoperative complications presented the best performance (area under the curve, 0.692; 95% CI, 0.599-0.776) compared with other nephrometry scoring systems. CONCLUSIONS: The T-index can be considered as a single value comprising key structural indicators for surgical complexity. Our findings suggest that the T-index can provide a quantitative and objective scoring system associated with surgical difficulty and postoperative complications of partial nephrectomy.