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Background: To design a pulmonary ground-glass nodules (GGN) classification method based on computed tomography (CT) radiomics and machine learning for prediction of invasion in early-stage ground-glass opacity (GGO) pulmonary adenocarcinoma., Methods: This retrospective study included pulmonary GGN patients who were histologically confirmed to have adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma cancer (IAC) from 2020 to 2023. CT images of all patients were automatically segmented and 107 radiomic features were obtained for each patient. Classification models were developed using random forest (RF) and cross-validation, including three one-versus-others models and one three-class model. For each model, features were ranked by normalized Gini importance, and a minimal subset was selected with a cumulative importance exceeding 0.9. These selected features were then used to train the final models. The models' performance metrics, including area under the curve (AUC), accuracy, sensitivity, and specificity, were computed. AUC and accuracy were compared to determine the final optimal method., Results: The study comprised 193 patients (mean age 54 ± 11 years, 65 men), including 65 AIS, 54 MIA, and 74 IAC, divided into one training cohort (N = 154) and one test cohort (N = 39). The final three-class RF model outperformed three individual one-versus-others models in distinguishing each class from the other two. For the multiclass classification model, the AUC, accuracy, sensitivity, and specificity were 0.87, 0.79, 0.62, and 0.88 for AIS; 0.90, 0.79, 0.54, and 0.89 for MIA; and 0.87, 0.69, 0.73, and 0.67 for IAC, respectively., Conclusions: A radiomics-based multiclass RF model could effectively differentiate three types of pulmonary GGN, which enabled early diagnosis of GGO pulmonary adenocarcinoma., (© 2024. The Author(s).)

Molecular imaging of muscle-invasive bladder cancer (MBC) is restricted to its locoregional and distant metastases, since most radiopharmaceuticals have a urinary excretion that limits the visualization of the primary tumor. 64 CuCl 2 , a positron-emitting radiotracer with nearly exclusive biliary elimination, could be well suited to exploring urinary tract neoplasms. In this study, we evaluated the feasibility of 64 CuCl 2 -based staging of patients with MBC; furthermore, we compared the diagnostic capability of this method with those of the current gold standards, that is, contrast-enhanced CT (ceCT) and 18 F-FDG PET/CT. Methods: We prospectively enrolled patients referred to our institution for pathology-confirmed MBC staging/restaging between September 2021 and January 2023. All patients underwent ceCT, 18 F-FDG, and 64 CuCl 2 PET/CT within 2 wk. Patient-based analysis and lesion-based analysis were performed for all of the potentially affected districts (overall, bladder wall, lymph nodes, skeleton, liver, lung, and pelvic soft tissue). Results: Forty-two patients (9 women) were enrolled. Thirty-six (86%) had evidence of disease, with a total of 353 disease sites. On patient-based analysis, ceCT and 64 CuCl 2 PET/CT showed higher sensitivity than 18 F-FDG PET/CT in detecting the primary tumor ( P < 0.001); moreover, 64 CuCl 2 PET/CT was slightly more sensitive than 18 F-FDG PET/CT in disclosing soft-tissue lesions ( P < 0.05). Both PET methods were more specific and accurate than ceCT in classifying nodal lesions ( P < 0.05). On lesion-based analysis, 64 CuCl 2 PET/CT outperformed 18 F-FDG PET/CT and ceCT in detecting disease localizations overall ( P < 0.001), in the lymph nodes ( P < 0.01), in the skeleton ( P < 0.001), and in the soft tissue ( P < 0.05). Conclusion: 64 CuCl 2 PET/CT appears to be a sensitive modality for staging/restaging of MBC and might represent a "one-stop shop" diagnostic method in these scenarios., (© 2024 by the Society of Nuclear Medicine and Molecular Imaging.)

Background Contrast-enhanced US (CEUS) can be used preoperatively for evaluating muscle invasion in bladder cancer, which is important for determining appropriate treatment. However, diagnostic criteria for assessing this at CEUS have not been standardized. Purpose To develop and validate a CEUS Vesical Imaging Reporting and Data System (VI-RADS) for evaluating muscle invasion in bladder cancer. Materials and Methods This single-center prospective study consecutively enrolled patients with suspected bladder cancer. Participants underwent transabdominal or intracavity CEUS between July 2021 and May 2023. Participants were divided into a training set and a validation set at a 2:1 ratio based on the chronologic order of enrollment. The training set was used to identify major imaging features to include in CEUS VI-RADS, and the likelihood of muscle invasion per category was determined using a pathologic reference standard. The optimal VI-RADS category cutoff for muscle invasion was determined with use of the maximum Youden index. The validation set was assessed by novice and expert readers and used to validate the diagnostic performance and interreader agreement of the developed system. Results Overall, 126 participants (median age, 64 years [IQR, 57-71 years]; 107 male) and 67 participants (median age, 64 years [IQR, 56-69 years]; 49 male) were included in the training and validation set, respectively. In the training set, the optimal CEUS VI-RADS category cutoff for muscle invasion was VI-RADS 4 or higher (Youden index, 0.77). In the validation set, CEUS VI-RADS achieved good performance for both novice and expert readers (area under the receiver operating characteristic curve, 0.80 [95% CI: 0.70, 0.90] vs 0.88 [95% CI: 0.80, 0.97]; P = .09). The interreader agreement regarding the evaluation of CEUS VI-RADS category was 0.77 (95% CI: 0.65, 0.85) for novice readers, 0.87 (95% CI: 0.79, 0.92) for expert readers, and 0.78 (95% CI: 0.70, 0.84) for all readers. Conclusion The developed CEUS VI-RADS showed good performance and interreader agreement for the assessment of muscle invasion in bladder cancer. Chinese Clinical Trial Registry no. ChiCTR2100049435 © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Morrell in this issue.

Objectives: To develop radiomics-based classifiers for preoperative prediction of fibrous capsule invasion in renal cell carcinoma (RCC) patients by CT images., Methods: In this study, clear cell RCC (ccRCC) patients who underwent both preoperative abdominal contrast-enhanced CT and nephrectomy surgery at our hospital were analysed. By transfer learning, we used base model obtained from Kidney Tumour Segmentation challenge dataset to semi-automatically segment kidney and tumours from corticomedullary phase (CMP) CT images. Dice similarity coefficient (DSC) was measured to evaluate the performance of segmentation models. Ten machine learning classifiers were compared in our study. Performance of the models was assessed by their accuracy, precision, recall, and area under the receiver operating characteristic curve (AUC). The reporting and methodological quality of our study was assessed by the CLEAR checklist and METRICS score., Results: This retrospective study enrolled 163 ccRCC patients. The semiautomatic segmentation model using CMP CT images obtained DSCs of 0.98 in the training cohort and 0.96 in the test cohort for kidney segmentation, and DSCs of 0.94 and 0.86 for tumour segmentation in the training and test set, respectively. For preoperative prediction of renal capsule invasion, the AdaBoost had the best performance in batch 1, with accuracy, precision, recall, and F1-score equal to 0.8571, 0.8333, 0.9091, and 0.8696, respectively; and the same classifier was also the most suitable for this classification in batch 2. The AUCs of AdaBoost for batch 1 and batch 2 were 0.83 (95% CI: 0.68-0.98) and 0.74 (95% CI: 0.51-0.97), respectively. Nine common significant features for classification were found from 2 independent batch datasets, including morphological and texture features., Conclusions: The CT-based radiomics classifiers performed well for the preoperative prediction of fibrous capsule invasion in ccRCC., Advances in Knowledge: Noninvasive prediction of renal fibrous capsule invasion in RCC is rather difficult by abdominal CT images before surgery. A machine learning classifier integrated with radiomics features shows a promising potential to assist surgical treatment options for RCC patients., (© The Author(s) 2024. Published by Oxford University Press on behalf of the British Institute of Radiology. All rights reserved. For permissions, please email: journals.permissions@oup.com.)

Objective: This study aims to evaluate and compare the predictive accuracy of Sonazoid-contrast-enhanced ultrasound (CEUS) and Gd-EOB-DTPA-enhanced MRI for detecting microvascular invasion (MVI) in hepatocellular carcinoma (HCC)., Methods: In this single-center prospective study, we included 64 patients with histopathologically confirmed single HCC lesions. Based on post-operative pathologic data, patients were categorized into two groups: those with MVI (n = 21) and those without MVI (n = 43). The diagnostic efficacy of CEUS was compared with that of MRI in predicting MVI., Results: Multifactorial analysis revealed that US features (tumor size > 4.35 cm, peritumoral enhancement, post-vascular ring enhancement, peak energy in the arterial phase of the difference between the margin area of HCC and distal liver parenchyma <-1.0 × 10 6 a.u), MRI features (rim enhancement, irregular tumor margin, and the halo sign) were all independent predictors of MVI (p < 0.05). The sensitivity and specificity of CEUS features in predicting MVI ranged from 61.9% to 86.4% and from 42.9% to 71.4%, respectively. For MRI features, the sensitivity and specificity ranged from 33.3% to 76.3% and from 54.7% to 90.5%, respectively. No statistically significant differences were observed in the area under the curve between CEUS and MRI (p > 0.05). Notably, peak energy of the difference showed the highest sensitivity at 86.4%, while the halo sign in MRI exhibited the highest specificity at 90.5%., Conclusion: Sonazoid-CEUS and Gd-EOB-DTPA-enhanced MRI demonstrate potential in predicting MVI in HCC lesions. Notably, CEUS showed higher sensitivity, whereas MRI displayed greater specificity in predicting MVI., Competing Interests: Conflict of interest The authors declare no competing interests., (Copyright © 2024 World Federation for Ultrasound in Medicine & Biology. Published by Elsevier Inc. All rights reserved.)

Purpose: To evaluate lymphovascular invasion (LVI) in breast cancer by comparing the diagnostic performance of preoperative multimodal magnetic resonance imaging (MRI)-based radiomics and deep-learning (DL) models., Methods: This retrospective study included 262 patients with breast cancer-183 in the training cohort (144 LVI-negative and 39 LVI-positive cases) and 79 in the validation cohort (59 LVI-negative and 20 LVI-positive cases). Radiomics features were extracted from the intra- and peritumoral breast regions using multimodal MRI to generate gross tumor volume (GTV)&#95;radiomics and gross tumor volume plus peritumoral volume (GPTV)&#95;radiomics. Subsequently, DL models (GTV&#95;DL and GPTV&#95;DL) were constructed based on the GTV and GPTV to determine the LVI status. Finally, the most effective radiomics and DL models were integrated with imaging findings to establish a hybrid model, which was converted into a nomogram to quantify the LVI risk., Results: The diagnostic efficiency of GPTV&#95;DL was superior to that of GTV&#95;DL (areas under the curve [AUCs], 0.771 and 0.720, respectively). Similarly, GPTV&#95;radiomics outperformed GTV&#95;radiomics (AUC, 0.685 and 0.636, respectively). Univariate and multivariate logistic regression analyses revealed an association between imaging findings, such as MRI-axillary lymph nodes and peritumoral edema (AUC, 0.665). The hybrid model achieved the highest accuracy by combining GPTV&#95;DL, GPTV&#95;radiomics, and imaging findings (AUC, 0.872)., Conclusion: The diagnostic efficiency of the GPTV-derived radiomics and DL models surpassed that of the GTV-derived models. Furthermore, the hybrid model, which incorporated GPTV&#95;DL, GPTV&#95;radiomics, and imaging findings, demonstrated the effective determination of LVI status prior to surgery in patients with breast cancer., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Purpose: To investigate whether preoperative spectral CT quantitative parameters can assess perineural invasion (PNI) status in rectal cancer., Methods: Sixty-two patients diagnosed with rectal cancer who underwent preoperative spectral CT were retrospectively enrolled and divided into positive and negative PNI groups according to histopathologic results. The CT attenuation value (HU) of virtual monochromatic images (40-70 keV), spectral curve slope (K (HU) ), effective atomic number (Z eff ), and iodine concentration (IC) from spectral CT were compared between these two groups using t test or rank sum test. A nomogram was established by incorporating the independent predictors to assess the overall diagnostic efficacy. The area under the ROC curves (AUCs) were compared using the DeLong test., Results: The preoperative spectral CT parameters (40-70 keV attenuation, K (HU) , Z eff , and IC) were significantly higher in the PNI-positive group compared to the PNI-negative group (all p < 0.05). The highest predictive efficiency of PNI was observed at 40 keV attenuation, with an area under the curve (AUC), sensitivity, specificity, and accuracy of 0.847, 81.8%, 72.5%, and 75.8%, respectively. Binary logistic regression demonstrated that the clinical feature (cN stage) and 40 keV attenuation were independent predictors of PNI status. The nomogram incorporating these two predictors (cN stage and 40 keV attenuation) exhibited the best evaluation efficacy, with an AUC, sensitivity, specificity, and accuracy of 0.885, 86.4%, 77.5%, and 80.6%., Conclusion: Spectral CT quantitative parameters proved valuable in the preoperative assessment of PNI status in rectal cancer patients. The combination of spectral CT parameters and clinical features could further enhance the diagnostic efficiency., (© 2024. The Author(s) under exclusive licence to Japan Radiological Society.)

Purpose: In pancreatic adenocarcinoma, the difficult distinction between normal and affected pancreas on CT studies may lead to discordance between the pre-surgical assessment of vessel involvement and intraoperative findings. We hypothesize that a visual aid tool could improve the performance of radiology residents when detecting vascular invasion in pancreatic adenocarcinoma patients. Methods: This study consisted of 94 pancreatic adenocarcinoma patient CTs. The visual aid compared the estimated body fat density of each patient with the densities surrounding the superior mesenteric artery and mapped them onto the CT scan. Four radiology residents annotated the locations of perceived vascular invasion on each scan with the visual aid overlaid on alternating scans. Using 3 expert radiologists as the reference standard, we quantified the area under the receiver operating characteristic curve to determine the performance of the tool. We then used sensitivity, specificity, balanced accuracy ((sensitivity + specificity)/2), and spatial metrics to determine the performance of the residents with and without the tool. Results: The mean area under the curve was 0.80. Radiology residents' sensitivity/specificity/balanced accuracy for predicting vascular invasion were 50%/85%/68% without the tool and 81%/79%/80% with it compared to expert radiologists, and 58%/85%/72% without the tool and 78%/77%/77% with it compared to the surgical pathology. The tool was not found to impact the spatial metrics calculated on the resident annotations of vascular invasion. Conclusion: The improvements provided by the visual aid were predominantly reflected by increased sensitivity and accuracy, indicating the potential of this tool as a learning aid for trainees., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Purpose: Survivors of surgically managed prostate cancer may experience urinary incontinence and erectile dysfunction. Our aim was to determine if 68 Ga-prostate-specific membrane antigen-11 positron emission tomography CT (PSMA-PET) in addition to multiparametric (mp) MRI scans improved surgical decision-making for nonnerve-sparing or nerve-sparing approach., Materials and Methods: We prospectively enrolled 50 patients at risk for extraprostatic extension (EPE) who were scheduled for prostatectomy. After mpMRI and PSMA-PET images were read for EPE prediction, surgeons prospectively answered questionnaires based on mpMRI and PSMA-PET scans on the decision for nerve-sparing or nonnerve-sparing approach. Final whole-mount pathology was the reference standard. Sensitivity, specificity, positive predictive value, negative predictive value, and receiver operating characteristic curves were calculated and McNemar's test was used to compare imaging modalities., Results: The median age and PSA were 61.5 years and 7.0 ng/dL. The sensitivity for EPE along the posterior neurovascular bundle was higher for PSMA-PET than mpMRI (86% vs 57%, P = .03). For MRI, the specificity, positive predictive value, negative predictive value, and area under the curve for the receiver operating characteristic curves were 77%, 40%, 87%, and 0.67, and for PSMA-PET were 73%, 46%, 95%, and 0.80. PSMA-PET and mpMRI reads differed on 27 nerve bundles, with PSMA-PET being correct in 20 cases and MRI being correct in 7 cases. Surgeons predicted correct nerve-sparing approach 74% of the time with PSMA-PET scan in addition to mpMRI compared to 65% with mpMRI alone ( P = .01)., Conclusions: PSMA-PET scan was more sensitive than mpMRI for EPE along the neurovascular bundles and improved surgical decisions for nerve-sparing approach. Further study of PSMA-PET for surgical guidance is warranted in the unfavorable intermediate-risk or worse populations., Clinicaltrials.gov Identifier: NCT04936334.

Purpose: To develop and validate a multiparametric magnetic resonance imaging (mpMRI)-based radiomics model for predicting lymph-vascular space invasion (LVSI) of cervical cancer (CC)., Methods: The data of 177 CC patients were retrospectively collected and randomly divided into the training cohort (n=123) and testing cohort (n = 54). All patients received preoperative MRI. Feature selection and radiomics model construction were performed using max-relevance and min-redundancy (mRMR) and the least absolute shrinkage and selection operator (LASSO) on the training cohort. The models were established based on the extracted features. The optimal model was selected and combined with clinical independent risk factors to establish the radiomics fusion model and the nomogram. The diagnostic performance of the model was assessed by the area under the curve., Results: Feature selection extracted the thirteen most important features for model construction. These radiomics features and one clinical characteristic were selected showed favorable discrimination between LVSI and non-LVSI groups. The AUCs of the radiomics nomogram and the mpMRI radiomics model were 0.838 and 0.835 in the training cohort, and 0.837 and 0.817 in the testing cohort., Conclusion: The nomogram model based on mpMRI radiomics has high diagnostic performance for preoperative prediction of LVSI in patients with CC., (© 2024. The Author(s).)

Rationale and Objectives: To evaluate the diagnostic performance of dual-energy CT (DECT) parameters and quantitative-semantic features for differentiating the invasiveness of lung adenocarcinoma manifesting as ground glass nodules (GGNs)., Materials and Methods: Between June 2022 and September 2023, 69 patients with 74 surgically resected GGNs who underwent DECT examinations were included. CT numbers on virtual monochromatic images were calculated at 40-130 keV generated from DECT. Quantitative morphological measurements and semantic features were evaluated on unenhanced CT images and compared between pathologically confirmed adenocarcinoma in situ (AIS)-minimally invasive adenocarcinoma (MIA) and invasive lung adenocarcinoma (IAC). Multivariable logistic regression analysis was used to identify independent predictors. The diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC) and compared using DeLong's test., Results: Monochromatic CT numbers at 40-130 keV were significantly higher in IAC than in AIS-MIA (all P < 0.05). Multivariate logistic analysis revealed that CT number of 130 keV (odds ratio [OR] = 1.02, P = 0.013), maximum cross-sectional long diameter (OR =1.40, P = 0.014), deep or moderate lobulation sign (OR =19.88, P = 0.005), and abnormal intranodular vessel morphology (OR = 25.57, P = 0.017) were independent predictors of IAC. The combined prediction model showed a favorable differentiation performance with an AUC of 0.966 (95.2% sensitivity, 94.3% specificity, 94.8% accuracy), which was significantly higher than that for each risk factor (AUC = 0.791-0.822, all P < 0.05)., Conclusion: A multi-parameter combined prediction model integrating monochromatic CT numbers from DECT and quantitative-semantic features is promising for the preoperative discrimination of IAC and AIS-MIA in GGN-predominant lung adenocarcinoma., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Chenggong Yan reports financial support was provided by National Natural Science Foundation of China (grant no.82271987). If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Rationale and Objectives: This study aimed to determine the feasibility of using the deep learning (DL) method to determine the degree (whether myometrial invasion [MI] >50%) of MI in patients with endometrial cancer (EC) based on ultrasound (US) images., Materials and Methods: From September 2017 to April 2023, 1289 US images of 604 patients with EC who underwent surgical resection at center 1, center 2 or center 3 were obtained and divided into a training set and an internal validation set. Ninety-five patients from center 4 and center 5 were randomly selected as the external testing set according to the same criteria as those for the primary cohort. This study evaluated three DL models trained on the training set and tested them on the validation and testing sets. The models' performance was analyzed based on accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC), and the performance of the models was subsequently compared with that of 15 radiologists., Results: In the final clinical diagnosis of MI in patients with EC, EfficientNet-B6 showed the best performance in the testing set in terms of area under the curve (AUC) [0.814, 95% CI (0.746-0.882]; accuracy [0.802, 95% CI (0.733-0.855]; sensitivity [0.623]; specificity [0.879]; positive likelihood ratio (PLR) [6.750]; and negative likelihood ratio (NLR) [0.389]. The diagnostic efficacy of EfficientNet-B6 was significantly better than that of the 15 radiologists, with an average diagnostic accuracy of 0.681, average AUC of 0.678, AUC of the best performance of 0.739, accuracy of 0.716, sensitivity of 0.806, specificity 0.672, PLR2.457, and NLR 0.289., Conclusion: Based on the preoperative US images of patients with EC, the DL model can accurately determine the degree of endometrial MI; the performance of this model is significantly better than that of radiologists, and it can effectively assist in clinical treatment decisions., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Xiachuan Qin reports financial support was provided by the Nanchong school science and technology strategic cooperation project (19SXHZ0165), Science and Technology Plan Project of Nanchong City (22JCYJPT0004) and Xiaoling Liu reports financial support was provided by the Nanchong Social Science Federation 14th Five-Year planning project (NC23B001)., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Objectives: To assess the diagnostic accuracy of nerve thickening on MRI to predict early-stage postlaminar optic nerve invasion (PLONI) in retinoblastoma. Furthermore, this study aimed to incorporate measurements into a multiparametric model for radiological determination of PLONI., Methods: In this retrospective multicenter case-control study, high-spatial-resolution 3D T2-weighted MR images were used to measure the distal optic nerve. Histopathology was the reference standard for PLONI. Two neuroradiologists independently measured the optic nerve width, height, and surface at 0, 3, and 5 mm from the most distal part of the optic nerve. Subsequently, PLONI was scored on contrast-enhanced T1-weighted and 3D T2-weighted images, blinded for clinical data. Optic nerve measurements with the highest diagnostic accuracy for PLONI were incorporated into a prediction model for radiological determination of PLONI., Results: One hundred twenty-four retinoblastoma patients (median age, 22 months [range, 0-113], 58 female) were included, resulting in 25 retinoblastoma eyes with histopathologically proven PLONI and 206 without PLONI. ROC analysis of axial optic nerve width measured at 0 mm yielded the best area under the curve of 0.88 (95% confidence interval: 0.79, 0.96; p < 0.001). The optimal width cutoff was ≥ 2.215 mm, with a sensitivity of 84% (95% CI: 64, 95%) and specificity of 83% (95% CI: 75, 89%) for detecting PLONI. Combining width measurements with the suspicion of PLONI on MRI sequences resulted in a prediction model with an improved sensitivity and specificity of respectively up to 88% and 92%., Conclusion: Postlaminar optic nerve thickening can predict early-stage postlaminar optic nerve invasion in retinoblastoma., Clinical Relevance Statement: This study provides an additional tool for clinicians to help determine postlaminar optic nerve invasion, which is a risk factor for developing metastatic disease in retinoblastoma patients., Key Points: • The diagnostic accuracy of contrast-enhanced MRI for detecting postlaminar optic nerve invasion is limited in retinoblastoma patients. • Optic nerve thickening can predict postlaminar optic nerve invasion. • A prediction model combining MRI features has a high sensitivity and specificity for detecting postlaminar optic nerve invasion., (© 2023. The Author(s), under exclusive licence to European Society of Radiology.)

Background: Preoperative discrimination between non-muscle-invasive bladder cancer (NMIBC) and the muscle invasive bladder cancer (MIBC) is a determinant of management. The purpose of this research is to employ radiomics to evaluate the diagnostic value in determining muscle invasiveness of compressed sensing (CS) accelerated 3D T2-weighted-SPACE sequence with high resolution and short acquisition time., Methods: This prospective study involved 108 participants who underwent preoperative 3D-CS-T2-weighted-SPACE, 3D-T2-weighted-SPACE and T2-weighted sequences. The cohort was divided into training and validation cohorts in a 7:3 ratio. In the training cohort, a Rad-score was constructed based on radiomic features selected by intraclass correlation coefficients, pearson correlation coefficient and least absolute shrinkage and selection operator . Multivariate logistic regression was used to develop a nomogram combined radiomics and clinical indices. In the validation cohort, the performances of the models were evaluated by ROC, calibration, and decision curves., Results: In the validation cohort, the area under ROC curve of 3D-CS-T2-weighted-SPACE, 3D-T2-weighted-SPACE and T2-weighted models were 0.87(95% confidence interval (CI):0.73-1.00), 0.79(95%CI:0.63-0.96) and 0.77(95%CI:0.60-0.93), respectively. The differences in signal-to-noise ratio and contrast-to-noise ratio between 3D-CS-T2-weighted-SPACE and 3D-T2-weighted-SPACE sequences were not statistically significant(p > 0.05). While the clinical model composed of three clinical indices was 0.74(95%CI:0.55-0.94) and the radiomics-clinical nomogram model was 0.88(95%CI:0.75-1.00). The calibration curves confirmed high goodness of fit, and the decision curve also showed that the radiomics model and combined nomogram model yielded higher net benefits than the clinical model., Conclusion: The radiomics model based on compressed sensing 3D T2WI sequence, which was acquired within a shorter acquisition time, showed superior diagnostic efficacy in muscle invasion of bladder cancer. Additionally, the nomogram model could enhance the diagnostic performance., (© 2024. The Author(s).)

Background: Most pituitary adenomas (PAs), also termed pituitary neuroendocrine tumors, are benign in nature and can be treated effectively by surgical resection, medical treatment, and in special cases by radiotherapy. However, invasive growth can be an important feature of a more aggressive behavior and adverse prognosis. The extension of PAs into the cavernous sinus can be categorized according to the Knosp criteria on magnetic resonance imaging (MRI). Comparative analyses of MRI features and intraoperative findings of invasive growth regarding different clinical factors are still scarce., Materials and Methods: We performed a retrospective single-center analysis of 764 PAs that were surgically treated between October 2004 and April 2018. Invasive growth was assessed according to the surgical reports and preoperative MRI (Knosp criteria). Clinical data, such as patient age at diagnosis and gender, histopathological adenoma type, and extent of resection, were collected., Results: Invasive features on MRI were seen in 24.4% (Knosp grades 3A-4, 186/764) of the cases. Intraoperatively, invasion was present in 42.4% (324/764). Complete resection was achieved in 80.0% of adenomas and subtotal resection, in 20.1%. By multivariate analysis, invasion according to intraoperative findings was associated with the sparsely granulated corticotroph (SGCA, P = .0026) and sparsely granulated somatotroph (SGSA, P = .0103) adenoma type as well as age (P = .0287). Radiographic invasion according to Knosp grades 3A-4 correlated with age (P = .0098), SGCAs (P = .0005), SGSAs (P = .0351), and gonadotroph adenomas (P = .0478). Both criteria of invasion correlated with subtotal resection (P = .0001, respectively)., Conclusions: Both intraoperative and radiographic signs of invasive growth are high-risk lesions for incomplete extent of resection and occur more frequently in older patients. A particularly high prevalence of invasion can be found in the SGCA and SGSA types. Cavernous sinus invasion is also more common in gonadotroph adenomas. Usage of the Knosp classification is a valuable preoperative estimation tool., Competing Interests: Conflict of interest: B.B. is co-founder, shareholder, and CTO of AIRAmed GmbH (outside the submitted work). No other potential conflicts of interest were declared., (© The Author(s) 2024. Published by Oxford University Press on behalf of European Society of Endocrinology. All rights reserved. For commercial re-use, please contact reprints@oup.com for reprints and translation rights for reprints. All other permissions can be obtained through our RightsLink service via the Permissions link on the article page on our site—for further information please contact journals.permissions@oup.com.)

As the proportion of women diagnosed with invasive breast cancer increases, the role of imaging for staging and surveillance purposes should be determined based on evidence-based guidelines. It is important to understand the indications for extent of disease evaluation and staging, as unnecessary imaging can delay care and even result in adverse outcomes. In asymptomatic patients that received treatment for curative intent, there is no role for imaging to screen for distant recurrence. Routine surveillance with an annual 2-D mammogram and/or tomosynthesis is recommended to detect an in-breast recurrence or a new primary breast cancer in women with a history of breast cancer, and MRI is increasingly used as an additional screening tool in this population, especially in women with dense breasts. The American College of Radiology Appropriateness Criteria are evidence-based guidelines for specific clinical conditions that are reviewed annually by a multidisciplinary expert panel. The guideline development and revision process support the systematic analysis of the medical literature from peer reviewed journals. Established methodology principles such as Grading of Recommendations Assessment, Development, and Evaluation or GRADE are adapted to evaluate the evidence. The RAND/UCLA Appropriateness Method User Manual provides the methodology to determine the appropriateness of imaging and treatment procedures for specific clinical scenarios. In those instances where peer reviewed literature is lacking or equivocal, experts may be the primary evidentiary source available to formulate a recommendation., (Copyright © 2024 American College of Radiology. Published by Elsevier Inc. All rights reserved.)

Rationale and Objectives: To explore and compare the performance of LI-RADS® and radiomics from multiparametric MRI in predicting microvascular invasion (MVI) preoperatively in patients with solitary hepatocellular carcinoma (HCC)< 5 cm., Methods: We enrolled 143 patients with pathologically proven HCC and randomly stratified them into training (n = 100) and internal validation (n = 43) cohorts. Besides, 53 patients were enrolled to constitute an independent test cohort. Clinical factors and imaging features, including LI-RADS and three other features (non-smooth margin, incomplete capsule, and two-trait predictor of venous invasion), were reviewed and analyzed. Radiomic features from four MRI sequences were extracted. The independent clinic-imaging (clinical) and radiomics model for MVI-prediction were constructed by logistic regression and AdaBoost respectively. And the clinic-radiomics combined model was further constructed by logistic regression. We assessed the model discrimination, calibration, and clinical usefulness by using the area under the receiver operating characteristic curve (AUC), calibration curve, and decision-curve analysis respectively., Results: Incomplete tumor capsule, corona enhancement, and radiomic features were related to MVI in solitary HCC＜5 cm. The clinical model achieved AUC of 0.694/0.661 (training/internal validation). The single-sequence-based radiomic model's AUCs were 0.753-0.843/0.698-0.767 (training/internal validation). The combination model exhibited superior diagnostic performance to the clinical model (AUC: 0.895/0.848 [training/ internal validation]) and yielded an AUC of 0.858 in an independent test cohort., Conclusion: Incomplete tumor capsule and corona enhancement on preoperative MRI were significantly related to MVI in solitary HCC＜5 cm. Multiple-sequence radiomic features potentially improve MVI-prediction-model performance, which could potentially help determining HCC's appropriate therapy., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Bing Feng reports article publishing charges was provided by Beijing Hope Run Special Fund of Cancer Foundation of China (No. LC2021B05). The authors of this manuscript declare that Siyun Liu and Sicong Wang are statisticians from GE Healthcare. The other authors declare no competing interests., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Objective: To investigate the independent risk variables associated with the potential invasiveness of ductal carcinoma in situ (DCIS) on multi-parametric ultrasonography, and further construct a nomogram for risk assessment., Methods: Consecutive patients from January 2017 to December 2022 who were suspected of having ductal carcinoma in situ (DCIS) based on magnetic resonance imaging or mammography were prospectively enrolled. Histopathological findings after surgical resection served as the gold standard. Grayscale ultrasound, Doppler ultrasound, shear wave elastography (SWE), and contrast-enhanced ultrasound (CEUS) examinations were preoperative performed. Binary logistic regression was used for multifactorial analysis to identify independent risk factors from multi-parametric ultrasonography. The correlation between independent risk factors and pathological prognostic markers was analyzed. The predictive efficacy of DCIS associated with invasiveness was assessed by logistic analysis, and a nomogram was established., Results: A total of 250 DCIS lesions were enrolled from 249 patients, comprising 85 pure DCIS and 165 DCIS with invasion (DCIS-IDC), of which 41 exhibited micro-invasion. The multivariate analysis identified independent risk factors for DCIS with invasion on multi-parametric ultrasonography, including image size (>2cm), Doppler ultrasound RI (≥0.72), SWE's Emax (≥66.4 kPa), hyper-enhancement, centripetal enhancement, increased surrounding vessel, and no contrast agent retention on CEUS. These factors correlated with histological grade, Ki-67, and human epidermal growth factor receptor 2 (HER2) (P < 0.1). The multi-parametric ultrasound approach demonstrated good predictive performance (sensitivity 89.7 %, specificity 73.8 %, AUC 0.903), surpassing single US modality or combinations with SWE or CEUS modalities. Utilizing these factors, a predictive nomogram achieved a respectable performance (AUC of 0.889) for predicting DCIS with invasion. Additionally, a separate nomogram for predicting DCIS with micro-invasion, incorporating independent risk factors such as RI (≥0.72), SWE's Emax (≥65.2 kPa), and centripetal enhancement, demonstrated an AUC of 0.867., Conclusion: Multi-parametric ultrasonography demonstrates good discriminatory ability in predicting both DCIS with invasion and micro-invasion through the analysis of lesion morphology, stiffness, neovascular architecture, and perfusion. The use of a nomogram based on ultrasonographic images offers an intuitive and effective method for assessing the risk of invasion in DCIS. Although the nomogram is not currently considered a clinically applicable diagnostic tool due to its AUC being below the threshold of 0.9, further research and development are anticipated to yield positive outcomes and enhance its viability for clinical utilization., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Purpose: To predict solid and micropapillary components in lung invasive adenocarcinoma using radiomic analyses based on high-spatial-resolution CT (HSR-CT)., Materials and Methods: For this retrospective study, 64 patients with lung invasive adenocarcinoma were enrolled. All patients were scanned by HSR-CT with 1024 matrix. A pathologist evaluated subtypes (lepidic, acinar, solid, micropapillary, or others). Total 61 radiomic features in the CT images were calculated using our modified texture analysis software, then filtered and minimized by least absolute shrinkage and selection operator (LASSO) regression to select optimal radiomic features for predicting solid and micropapillary components in lung invasive adenocarcinoma. Final data were obtained by repeating tenfold cross-validation 10 times. Two independent radiologists visually predicted solid or micropapillary components on each image of the 64 nodules with and without using the radiomics results. The quantitative values were analyzed with logistic regression models. The receiver operating characteristic curves were generated to predict of solid and micropapillary components. P values < 0.05 were considered significant., Results: Two features (Coefficient Variation and Entropy) were independent indicators associated with solid and micropapillary components (odds ratio, 30.5 and 11.4; 95% confidence interval, 5.1-180.5 and 1.9-66.6; and P = 0.0002 and 0.0071, respectively). The area under the curve for predicting solid and micropapillary components was 0.902 (95% confidence interval, 0.802 to 0.962). The radiomics results significantly improved the accuracy and specificity of the prediction of the two radiologists., Conclusion: Two texture features (Coefficient Variation and Entropy) were significant indicators to predict solid and micropapillary components in lung invasive adenocarcinoma., (© 2024. The Author(s).)

Objective: To investigate the potential value of quantitative parameters derived from synthetic magnetic resonance imaging (syMRI) for discriminating axillary lymph nodes metastasis (ALNM) in breast cancer patients., Materials and Methods: A total of 56 females with histopathologically proven invasive breast cancer who underwent both conventional breast MRI and additional syMRI examinations were enrolled in this study, including 30 patients with ALNM and 26 with non-ALNM. SyMRI has enabled quantification of T1 relaxation time (T1), T2 relaxation time (T2) and proton density (PD). The syMRI quantitative parameters of breast primary tumors before (T1 tumor , T2 tumor , PD tumor ) and after (T1 + tumor , T2 + tumor , PD + tumor ) contrast agent injection were obtained. Similarly, measurements were taken for axillary lymph nodes before (T1 LN , T2 LN , PD LN ) and after (T1 + LN , T2 + LN , PD + LN ) the injection, then theΔT1 (T1-T1 + ), ΔT2 (T2-T2 + ), ΔPD (PD-PD + ), T1/T2 and T1 + /T2 + were calculated. All parameters were compared between ANLM and non-ALNM group. Intraclass correlation coefficient for assessing interobserver agreement. The independent Student's t test or Mann-Whitney U test to determine the relationship between the mean quantitative values and the ALNM. Multivariate logistic regression analyses followed by receiver operating characteristics (ROC) analysis for discriminating ALN status. A P value < 0.05 was considered statistically significant., Results: The short-diameter of lymph nodes (D LN ) in ALNM group was significantly longer than that in the non-ALNM group (10.22 ± 3.58 mm vs. 5.28 ± 1.39 mm, P < 0.001). The optimal cutoff value was determined to be 5.78 mm, with an AUC of 0.894 (95 % CI: 0.838-0.939), a sensitivity of 86.7 %, and a specificity of 90.2 %. In syMRI quantitative parameters of breast tumors, T2 tumor , ΔT2 tumor and ΔPD tumor values showed statistically significant differences between the two groups (P < 0.05). T2 tumor value had the best performance in discriminating ALN status (AUC = 0.712), and the optimal cutoff was 90.12 ms, the sensitivity and specificity were 65.0 % and 83.6 % respectively. In terms of syMRI quantitative parameters of lymph nodes, T1 LN , T2 LN , T1 LN /T2 LN , T2 + LN and ΔT1 LN values were significantly different between the two groups (P < 0.05), and their AUCs were 0.785, 0.840, 0.886, 0.702 and 0.754, respectively. Multivariate analyses indicated that the T1 LN value was the only independent predictor of ALNM (OR=1.426, 95 % CI: 1.130-1.798, P = 0.039). The diagnostic sensitivity and specificity of T1 LN was 86.7 % and 69.4 % respectively at the best cutoff point of 1371.00 ms. The combination of T1 LN , T2 LN , T1 LN /T2 LN , ΔT1 LN and D LN had better performance for differentiating ALNM and non-ALNM, with AUCs of 0.905, 0.957, 0.964 and 0.897, respectively., Conclusion: The quantitative parameters derived from syMRI have certain value for discriminating ALN status in invasive breast cancer, with T2 tumor showing the highest diagnostic efficiency among breast lesions parameters. Moreover, T1 LN acted as an independent predictor of ALNM., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Rationale and Objectives: To explore the potential value of the apparent diffusion coefficient (ADC)-based nomogram models in preoperatively assessing the depth of myometrial invasion of endometrial endometrioid adenocarcinoma (EEA)., Materials and Methods: Preoperative magnetic resonance imaging (MRI) of 210 EEA patients were retrospectively analyzed. ADC histogram metrics derive from the whole-tumor regions of interest. Univariate and multivariate analyses were used to screen the ADC histogram metrics and clinical characteristics for nomogram model building. The diagnostic sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of two radiologists without and with the assistance of models were calculated and compared., Results: Two nomogram models were developed for predicting no myometrial invasion (NMI) and deep myometrial invasion (DMI) with area under the curves of 0.85 and 0.82, respectively. With the assistance of models, the overall accuracies were significantly improved [radiologist&#95;1, 73.3% vs 86.2% (p = 0.001); radiologist&#95;2, 80.0% vs 91.0% (p = 0.002)]. In determining NMI, the sensitivity and PPV were greatly improved but not significant for radiologist&#95;1 (51.9% vs 77.8% and 46.7% vs 75.0%, p = 0.229 and 0.511), and under/near the significance level for radiologist&#95;2 (59.3% vs 88.9% and 57.1% vs 82.8%, p = 0.041 and 0.065), while the specificity, accuracy, and NPV were significantly improved (all p < 0.001). In determining DMI, all sensitivity, specificity, accuracy, PPV, and NPV were significantly improved (all p < 0.001)., Conclusion: The ADC-based nomogram models can improve the diagnostic performance of radiologist in preoperatively assessing the depth of myometrial invasion and facilitate optimizing clinical individualized treatment decisions., Competing Interests: Declaration of Competing Interest The authors declared no potential conflicts of interests associated with this study., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Purpose: To develop a convolutional neural network (CNN) model to diagnose skull-base invasion by nasopharyngeal malignancies in CT images and evaluate the model's diagnostic performance., Materials and Methods: We divided 100 malignant nasopharyngeal tumor lesions into a training (n = 70) and a test (n = 30) dataset. Two head/neck radiologists reviewed CT and MRI images and determined the positive/negative skull-base invasion status of each case (training dataset: 29 invasion-positive and 41 invasion-negative; test dataset: 13 invasion-positive and 17 invasion-negative). Preprocessing involved extracting continuous slices of the nasopharynx and clivus. The preprocessed training dataset was used for transfer learning with Residual Neural Networks 50 to create a diagnostic CNN model, which was then tested on the preprocessed test dataset to determine the invasion status and model performance. Original CT images from the test dataset were reviewed by a radiologist with extensive head/neck imaging experience (senior reader: SR) and another less-experienced radiologist (junior reader: JR). Gradient-weighted class activation maps (Grad-CAMs) were created to visualize the explainability of the invasion status classification., Results: The CNN model's diagnostic accuracy was 0.973, significantly higher than those of the two radiologists (SR: 0.838; JR: 0.595). Receiver operating characteristic curve analysis gave an area under the curve of 0.953 for the CNN model (versus 0.832 and 0.617 for SR and JR; both p < 0.05). The Grad-CAMs suggested that the invasion-negative cases were present predominantly in bone marrow, while the invasion-positive cases exhibited osteosclerosis and nasopharyngeal masses., Conclusions: This CNN technique would be useful for CT-based diagnosis of skull-base invasion by nasopharyngeal malignancies., (© 2024. The Author(s).)

Background: Apparent diffusion coefficient (ADC) value is an important part of bladder cancer magnetic resonance imaging (MRI) assessment and can predict the aggressive and invasive potentials. There is growing interest in whole tumor volume measurements., Purpose: To investigate if the volumetric ADC measurement method will significantly exceed the diagnostic performance of the selected region of interest (ROI) method in everyday practice., Material and Methods: A prospective evaluation was carried out of 50 patients with bladder cancer by two radiologists. The mean and the minimum ADC values were measured using both methods. The inter-reader agreement was determined by the intraclass correlation coefficient. The ADC values were compared between different grades, states of muscle invasion, and lympho-vascular invasion (LVI); then, validity was evaluated using receiver operating characteristic (ROC) curves. Areas under the curve (AUC) were then compared for the level of statistical significance., Results: The inter-observer agreement was excellent for the ADC values using both methods. The volumetric measurement provides higher mean and lower minimum ADC values with statistically significant differences ( P <0.00001). The highest diagnostic accuracy for differentiating tumor grade and predicting muscle invasion was for the minimum ADC by a selected ROI. However, the differences between the achieved AUCs were of no statistical significance. None of the ADC values predicted LVI with statistical significance., Conclusion: The selected ROI and volumetric measurement methods of mean and minimum ADC in bladder cancer yield different values, still having comparable diagnostic performance with accurate ROI sampling. The minimum ADC value by ROI is preferred in everyday clinical practice., Competing Interests: Declaration of conflicting interestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Purpose: Our single-centre retrospective study aimed to investigate the relationship between preoperative dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) findings and apparent diffusion coefficient (ADC) values and lymphovascular invasion (LVI) status of the lesions in patients with clinically-radiologically lymph node-negative invasive breast cancer. Methods: A total of 250 breast lesions diagnosed in preoperative magnetic resonance imaging were identified. All patients were divided into 2 subgroups: LVI-negative and LVI-positive according to the pathological findings of surgical specimens. The 2 groups' DCE-MRI findings, ADC values, and histopathological results of lesions were compared. Results: LVI was detected in 100 of 250 lesions. Younger age than 45 years and larger lesion size than 20 mm were found to be associated with the presence of LVI ( P  < .001). High histological and nuclear grade ( P  = .001), HER2-enriched molecular subtype ( P  = .001), and Ki-67 positivity ( P  = .016) were significantly associated with LVI. The LVI positivity rate was significantly higher in the lesions with medium-rapid initial phase kinetic curve and washout delayed phase kinetic curve ( P  = .001). The presence of LVI was significantly associated with the presence of peritumoural edema, sentinel lymph node metastasis, adjacent vessel sign, and increased whole breast vascularity ( P  < .001). When diffusion-weighted imaging findings were evaluated, it was determined that tumoural ADC values lower than 1068 × 10 -6  mm 2 /second ( P  = .002) and peritumoural-tumoural ADC ratios higher than 1.5 ( P  = .001) statistically increased the probability of LVI. Conclusion: The patient's age, various histopathological and DCE-MRI findings, tumoural ADC value, and peritumoural-tumoural ADC ratio may be useful in the preoperative prediction of LVI status in breast cancer lesions., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Purpose: To investigate the influence of deep learning reconstruction (DLR) on bladder MRI, specifically examination time, image quality, and diagnostic performance of vesical imaging reporting and data system (VI-RADS) within a prospective clinical cohort., Methods: Seventy participants with bladder cancer who underwent MRI between August 2022 and February 2023 with a protocol containing standard T2-weighted imaging (T2WI S ), standard diffusion-weighted imaging (DWI S ), fast T2WI with DLR (T2WI DL ), and fast DWI with DLR (DWI DL ) were enrolled in this prospective study. Imaging quality was evaluated by measuring signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and qualitative image quality scoring. Additionally, the apparent diffusion coefficient (ADC) of bladder lesions derived from DWI S and DWI DL was measured and VI-RADS scoring was performed. Paired t-test or paired Wilcoxon signed-rank test were performed to compare image quality score, SNR, CNR, and ADC between standard sequences and fast sequences with DLR. The diagnostic performance for VI-RADS was assessed using the area under the receiver operating characteristic curve (AUC)., Results: Compared to T2WI S and DWI S , T2WI DL and DWI DL reduced the acquisition time from 5:57 min to 3:13 min and showed significantly higher SNR, CNR, qualitative image quality score of overall image quality, image sharpness, and lesion conspicuity. There were no significant differences in ADC and AUC of VI-RADS between standard sequences and fast sequences with DLR., Conclusions: The application of DLR to T2WI and DWI reduced examination time and significantly improved image quality, maintaining ADC and the diagnostic performance of VI-RADS for evaluating muscle invasion in bladder cancer., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Rationale and Objectives: Treatment strategies for invasive breast cancer require accurate lymphovascular invasion (LVI) predictions. This study aimed to investigate the effectiveness of delta radiomics based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) for assessing LVI and develop a nomogram to aid treatment decisions., Materials and Methods: Overall, 293 patients with resectable invasive breast cancer underwent preoperative DCE-MRI. Radiomic features were extracted from pre-contrast (A0), first post-contrast (A1), and subtracted images of A0 and A1. Three radiomics models were developed using several data analyses; logistic analyses were performed to identify radiological features to predict the LVI status. A hybrid model integrating both radiological features and optimal radiomics was developed. Receiver operating characteristic analysis was employed to evaluate model performance, using the area under the curve (AUC) as a quantitative metric for discriminative ability., Results: In the test set, the Radiomics-Delta model, with 17 radiomic features, had an AUC of 0.781 and accuracy of 0.705. Radiomics-A0, with 10 features, had an AUC of 0.619 and accuracy of 0.523, while Radiomics-A1, with 8 features, had an AUC of 0.715 and accuracy of 0.591. The hybrid model exhibited better performance, with an AUC of 0.868 and accuracy of 0.875, than the radiological and Radiomics-Delta models, with an AUC of 0.759 and 0.781, respectively, and accuracy of 0.773 and 0.705, respectively., Conclusion: Compared to Radiomics-A0 and Radiomics-A1, Radiomics-Delta demonstrated superior performance. Moreover, the hybrid model incorporating Radiomics-Delta and radiological features exhibited excellent performance in determining the LVI status in cases of invasive breast cancer., Competing Interests: Declaration of Competing Interest The authors declare that they have no competing interests., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Rationale and Objectives: This study aimed to create a nomogram model that combines clinical factors with radiomics analysis of both intra- and peritumoral regions extracted from preoperative digital breast tomosynthesis (DBT) images, in order to develop a reliable method for predicting the lymphovascular invasion (LVI) status in invasive breast cancer (IBC) patients., Materials and Methods: A total of 178 patients were randomly split into a training dataset (N = 124) and a validation dataset (N = 54). Comprehensive clinical data, encompassing DBT features, were gathered for all cases. Radiomics features were extracted and selected from intra- and peritumoral region to establish radiomics signature (Radscore). To construct the clinical model and nomogram model, univariate and multivariate logistic regression analyses were utilized to identify independent risk factors. To assess and validate these models, various analytical methods were employed, including receiver operating characteristic (ROC) curve analysis, calibration curve analysis, decision curve analysis (DCA), net reclassification improvement (NRI), and integrated discriminatory improvement (IDI)., Results: The clinical model is constructed based on two independent risk factors: tumor margin and the DBT-reported lymph node metastasis (DBT&#95;reported&#95;LNM). Incorporating Radscore&#95;Combine (utilizing both intra- and peritumoral radiomics features), tumor margin, and DBT&#95;reported&#95;LNM into the nomogram achieved a reliable predictive performance, with area under the curve (AUC) values of 0.906 and 0.905 in both datasets, respectively. The significant improvement demonstrated by the NRI and IDI indicates that the Radscore&#95;Combine could be a valuable biomarker for effectively predicting the status of LVI., Conclusion: The nomogram demonstrated a reliable ability to predict LVI in IBC patients., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights reserved.)

Objective: This study aims to assess the utility of magnetic resonance imaging (MRI) 3D SPACE T2-weighted imaging (T2WI) sequences in evaluating cavernous sinus invasion by pituitary adenomas., Methods: Data were collected from patients who underwent continuous pituitary MRIexaminations at the Medical Imaging Center of our hospital from October 2019 to February 2021. Eligible cases were evaluated for sagittal and axial T1WI sequences, coronal 3D SPACE T2WI sequences, and sagittal and coronal enhanced T1-weighted imaging (T1WI) sequences using the INFINITT PACS workstation. The Wilcoxon signed-rank test for paired samples and the Mann-Whitney U test for 2 independent samples were used to statistically analyze differences in image quality scores among various groups. In addition, the sensitivity, specificity, positive predictive value, and negative predictive value of each observation index were compared with intraoperative results., Results: 3D SPACE T2WI showed superior cavernous sinus imaging quality compared with contrast enhanced T1WI and T2WI plain scans (P < 0.05). The sensitivity, specificity, positive predictive value, and negative predictive value were 100%, 90.0%, 55.60%, and 100.0%, respectively. The accuracy for pituitary adenoma invasiveness diagnosis based on cavernous sinus medial wall integrity was 94.40%., Conclusions: The imaging quality of the medial wall of the cavernous sinus on the 3D SPACE T2WI plain scan sequence surpassed that of contrast enhanced T1WI TSE-enhanced scans and T2WI TSE plain scans. The continuous observation of the medial wall of the cavernous sinus using this sequence holds great diagnostic value for assessing cavernous sinus invasion by pituitary adenomas. This strategy is more reliable than traditional MRI observation indicators., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Objectives: The aim of the study is to assess the efficacy of the established computed tomography (CT)-based radiomics nomogram combined with radiomics and clinical features for predicting muscle invasion status in bladder cancer (BCa)., Methods: A retrospective analysis was conducted using data from patients who underwent CT urography at our institution between May 2018 and April 2023 with urothelial carcinoma of the bladder confirmed by postoperative histology. There were 196 patients enrolled in all, and each was randomized at random to either the training cohort (n = 137) or the test cohort (n = 59). Eight hundred fifty-one radiomics features in all were retrieved. For feature selection, the significance test and least absolute shrinkage and selection operator (LASSO) approaches were utilized. Subsequently, the radiomics score (Radscore) was obtained by applying linear weighting based on the selected features. The clinical and radiomics model, as well as radiomics-clinical nomogram were all established using logistic regression. Three models were evaluated using analysis of the receiver operating characteristic curve. An area under the curve (AUC) and 95% confidence intervals (CI) as well as specificity, sensitivity, accuracy, negative predictive value, and positive predictive value were included in the analysis. Radiomics-clinical nomogram's performance was assessed based on discrimination, calibration, and clinical utility., Results: After obtaining 851 radiomics features, 12 features were ultimately selected. Histopathological grading and tortuous blood vessels were included in the clinical model. The Radscore and clinical histopathology grading were among the final predictors in the unique nomogram. The three models had an AUC of 0.811 (95% CI, 0.742-0.880), 0.845 (95% CI, 0.781-0.908), and 0.896 (95% CI, 0.846-0.947) in the training cohort and in the test cohort they were 0.808 (95% CI, 0.703-0.913), 0.847 (95% CI, 0.739-0.954), and 0.887 (95% CI, 0.803-0.971). According to the DeLong test, the radiomics-clinical nomogram's AUC in the training cohort substantially differed from that of the clinical model (AUC: 0.896 versus 0.845, p = 0.015) and the radiomics model (AUC: 0.896 versus 0.811, p = 0.002). The Delong test in the test cohort revealed no significant difference among the three models., Conclusions: CT-based radiomics-clinical nomogram can be a useful tool for quantitatively predicting the status of muscle invasion in BCa., (© 2024. The Author(s).)

Background: The diagnostic ability of endoscopic ultrasound (EUS) for intestinal infiltration by pelvic masses has aroused considerable interest in many oncological settings. This study aimed to evaluate the effectiveness of EUS in predicting colorectal invasion in patients with pelvic masses and compare its accuracy with that of other imaging methods, namely pelvic MRI and abdominal computed tomography (CT), in predicting intestinal involvement in patients with histologically confirmed colorectal invasion., Methods: A hundred and eighty-four female patients with histologically confirmed benign or malignant pelvic masses were enrolled in a retrospective-prospective study. All patients underwent EUS, pelvic MRI, and one or more of abdominal CT, transvaginal sonography, and colonoscopy examinations before surgery. The surgical and pathological results were used as the gold standard to evaluate the diagnostic accuracy of EUS for colorectal invasion of pelvic masses., Results: This study included 184 patients who underwent surgery, with the time between EUS and surgery ranging from 1 to 309 (mean, 13.2) days. The diagnostic sensitivity, specificity, positive predictive value, and negative predictive value of EUS for benign and malignant pelvic masses infiltrating the intestine were 83.3, 97.8, 99.1, and 66.2%, respectively. The overall diagnostic accuracy was 87.0%., Conclusions: EUS is a simple, noninvasive, reliable, and accurate technique for the preoperative diagnosis of pelvic masses infiltrating the intestine. The authors recommend the use of this technology by gynecologists, as well as its incorporation into the preoperative diagnostic process to determine the most suitable surgical method. This would help in avoiding unexpected situations and unnecessary resource wastage during surgery., (Copyright © 2024 The Author(s). Published by Wolters Kluwer Health, Inc.)

Purpose: To develop a deep learning (DL) model based on preoperative contrast-enhanced computed tomography (CECT) images to predict microvascular invasion (MVI) and pathological differentiation of hepatocellular carcinoma (HCC)., Methods: This retrospective study included 640 consecutive patients who underwent surgical resection and were pathologically diagnosed with HCC at two medical institutions from April 2017 to May 2022. CECT images and relevant clinical parameters were collected. All the data were divided into 368 training sets, 138 test sets and 134 validation sets. Through DL, a segmentation model was used to obtain a region of interest (ROI) of the liver, and a classification model was established to predict the pathological status of HCC., Results: The liver segmentation model based on the 3D U-Network had a mean intersection over union (mIoU) score of 0.9120 and a Dice score of 0.9473. Among all the classification prediction models based on the Swin transformer, the fusion models combining image information and clinical parameters exhibited the best performance. The area under the curve (AUC) of the fusion model for predicting the MVI status was 0.941, its accuracy was 0.917, and its specificity was 0.908. The AUC values of the fusion model for predicting poorly differentiated, moderately differentiated and highly differentiated HCC based on the test set were 0.962, 0.957 and 0.996, respectively., Conclusion: The established DL models established can be used to noninvasively and effectively predict the MVI status and the degree of pathological differentiation of HCC, and aid in clinical diagnosis and treatment., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Background: The range of mandibular invasion by a tumour needs to be determined accurately to minimize unnecessary damage to the mandible. This study aimed to compare tumour boundary lines on computed tomography/magnetic resonance (CT/MR) images with those from pathological findings during the preoperative assessment of mandibular invasion by oral squamous cell carcinoma (OSCC). By comparing the methods, the potential of CT/MR for this application could be further elucidated., Methods: Eight patients with OSCC were imaged with CT/MR, mandibular specimens were collected, and the material site was measured. Haematoxylin-eosin staining was used for histopathological assessment. The presence and boundaries of bone invasion were evaluated. The CT/MR and histopathological boundaries of bone invasion were delineated and merged to compare and calculate the deviation of CT/MR and histopathological boundaries using the Fréchet distance., Results: The mean Fréchet distance between the CT and pathological tumour boundaries was 2.69 mm (standard error 0.46 mm), with a minimum of 1.18 mm, maximum of 3.64 mm, median of 3.10 mm, and 95% confidence interval of 1.40-3.97 mm. The mean Fréchet distance between the tumour boundaries on the MR and pathological images was 3.07 mm (standard error 0.56 mm), with a minimum of 1.53 mm, maximum of 4.74 mm, median of 2.90 mm, and 95% confidence interval of 1.53-4.61 mm., Conclusions: CT/MR imaging can provide an effective preoperative assessment of mandibular invasion of OSCC. Pathology images can be positioned on CT/MR scans with the help of computer software to improve the accuracy of the findings. The introduction of the Fréchet distance to compare tumour boundary lines is conducive to computer image diagnosis of tumour invasion of jaw boundaries., (© 2024. The Author(s).)

Purpose: To develop a nomogram for preoperative assessment of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) based on the radiological features of enhanced CT and to verify two imaging techniques (CT and MRI) in an external centre., Method: A total of 346 patients were retrospectively included (training, n = 185, CT images; external testing 1, n = 90, CT images; external testing 2, n = 71, MRI images), including 229 MVI-negative patients and 117 MVI-positive patients. The radiological features and clinical information of enhanced CT images were analysed, and the independent variables associated with MVI in HCC were determined by logistic regression analysis. Then, a nomogram prediction model was constructed. External validation was performed on CT (n = 90) and MRI (n = 71) images from another centre., Results: Among the 23 radiological and clinical features, size, arterial peritumoral enhancement (APE), tumour margin and alpha-fetoprotein (AFP) were independent influencing factors for MVI in HCC. The nomogram integrating these risk factors had a good predictive effect, with AUC, specificity and sensitivity values of 0.834 (95% CI: 0.774-0.895), 75.0% and 83.5%, respectively. The AUC values of external verification based on CT and MRI image data were 0.794 (95% CI: 0.700-0.888) and 0.883 (95% CI: 0.807-0.959), respectively. No statistical difference in AUC values among training set and testing sets was found., Conclusion: The proposed nomogram prediction model for MVI in HCC has high accuracy, can be used with different imaging techniques, and has good clinical applicability., (© 2024. The Author(s).)

Background: To explore the diagnostic value of multidetector computed tomography (MDCT) extramural vascular invasion (EMVI) in preoperative N Staging of gastric cancer patients., Methods: According to the MR-defined EMVI scoring standard of rectal cancer, we developed a 5-point scale scoring system to evaluate the status of CT-detected extramural vascular invasion(ctEMVI), 0-2 points were ctEMVI-negative status, and 3-4 points were positive status for ctEMVI. Patients were divided into ctEMVI positive group and ctEMVI negative group. The correlation between ctEMVI and clinical features was analyzed. Receiver operating characteristic (ROC) curve was used to evaluate the diagnostic efficacy of ctEMVI for pathological metastatic lymph nodes and N staging, The sensitivity, specificity, accuracy, positive predictive value (PPV), and negative predictive value (NPV) of pathological N staging using ctEMVI and short-axis diameter were generated and compared., Results: The occurrence rate of lymphovascular invasion (LVI) and proportion of tumors with a greatest diameter > 6 cm in the ctEMVI positive group was higher than that in the ctEMVI negative group (P < 0.05). Spearman correlation analysis showed a positive correlation between ctEMVI and LVI, N stage, and tumor size (P < 0.05). For ctEMVI scores ≥ 3,The AUC of ctEMVI for diagnosing lymph node metastasis, N stage ≥ N2, and N3 stage were 0.857, 0.802, and 0.758, respectively. The sensitivity, NPV and accuracy of ctEMVI for diagnosing N stage ≥ N2 were superior to those of short-axis diameter (P < 0.05), while sensitivity, specificity, PPV, NPV, and accuracy of ctEMVI for diagnosing N3 stage were superior to those of short-axis diameter (P < 0.05)., Conclusion: ctEMVI has important value in diagnosing metastatic lymph nodes and advanced N staging. As an important imaging marker, ctEMVI can be included in the preoperative imaging evaluation of patients, providing important assistance for clinical guidance and treatment., (© 2024. The Author(s).)