Stepwise algorithm using computed tomography and magnetic resonance imaging for differential diagnosis of fat-poor angiomyolipoma in small renal masses: A prospective validation study.

OBJECTIVES: To validate the diagnostic accuracy of a stepwise algorithm to differentiate fat-poor angiomyolipoma (fp-AML) from renal cancer in small renal masses (SRMs). METHODS: We prospectively enrolled 223 patients with solid renal masses <4 cm and no visible fat on unenhanced computed tomography (CT). Patients were assessed using an algorithm that utilized the dynamic CT and MRI findings in a stepwise manner. The diagnostic accuracy of the algorithm was evaluated in patients whose histology was confirmed through surgery or biopsy. The clinical course of the patients was further analyzed. RESULTS: The algorithm classified 151 (68%)/42 (19%)/30 (13%) patients into low/intermediate/high AML probability groups, respectively. Pathological diagnosis was made for 183 patients, including 10 (5.5%) with fp-AML. Of these, 135 (74%)/36 (20%)/12 (6.6%) were classified into the low/intermediate/high AML probability groups, and each group included 1 (0.7%)/3 (8.3%)/6 (50%) fp-AMLs, respectively, leading to the area under the curve for predicting AML of 0.889. Surgery was commonly opted in the low and intermediate AML probability groups (84% and 64%, respectively) for initial management, while surveillance was selected in the high AML probability group (63%). During the 56-month follow-up, 36 (82%) of 44 patients initially surveyed, including 13 of 18 (72%), 6 of 7 (86%), and 17 of 19 (89%) in the low/intermediate/high AML probability groups, respectively, continued surveillance without any progression. CONCLUSIONS: This study confirmed the high diagnostic accuracy for differentiating fp-AMLs. These findings may help in the management of patients with SRMs.

English version of Japanese Clinical Practice Guidelines 2022 for gastrointestinal stromal tumor (GIST) issued by the Japan Society of Clinical Oncology.

The Japan Society of Clinical Oncology Clinical Practice Guidelines 2022 for gastrointestinal stromal tumor (GIST) have been published in accordance with the Minds Manual for Guideline Development 2014 and 2017. A specialized team independent of the working group for the revision performed a systematic review. Since GIST is a rare type of tumor, clinical evidence is not sufficient to answer several clinical and background questions. Thus, in these guidelines, we considered that consensus among the experts who manage GIST, the balance between benefits and harms, patients' wishes, medical economic perspective, etc. are important considerations in addition to the evidence. Although guidelines for the treatment of GIST have also been published by the National Comprehensive Cancer Network (NCCN) and the European Society for Medical Oncology (ESMO), there are some differences between the treatments proposed in those guidelines and the treatments in the present guidelines because of the differences in health insurance systems among countries.

Arterial Calcification Disappearance in Breast Imaging: A Key Indicator for Transition to Invasive Ductal Carcinoma.

A woman in her 70s, initially suspected of having fibroadenoma due to a well-defined mass in her breast, underwent regular mammography and ultrasound screenings. Over several years, no appreciable alterations in the mass were observed, maintaining the fibroadenoma diagnosis. However, in the fourth year, an ultrasound indicated slight enlargement and peripheral irregularities in the mass, even though the mammography images at that time showed no alterations. Interestingly, mammography images over time showed the gradual disappearance of previously observed arterial calcification around the mass. Pathological examination eventually identified the mass as invasive ductal carcinoma. Although the patient had breast tissue arterial calcification typical of atherosclerosis, none was present around the tumor-associated arteries. This case highlights the importance of monitoring arterial calcification changes in mammography, suggesting that they are crucial indicators in breast cancer diagnosis, beyond observing size and shape alterations.

End-of-treatment 18[F]-FDG PET can predict early progression in patients receiving bendamustine-rituximab for follicular lymphoma in first relapse: a prospective West Japan hematology Study Group (W-JHS) NHL01 trial.

Response determined by 18[F]-fluoro-2-deoxy-D-glucose (18F-FDG) positron emission tomography (PET)-CT after induction therapy can predict progression-free survival (PFS) in follicular lymphoma (FL). However, little prospective research has examined the significance of PET after second-line therapy. We conducted a prospective multicenter phase II trial (W-JHS NHL01) of bendamustine plus rituximab (BR) without rituximab maintenance for FL in first relapse. This study aimed to evaluate the usefulness of end-of-treatment (EOT)-PET for predicting PFS in FL patients in first relapse. EOT-PET examinations were performed between 6 and 8 weeks from the start of the last BR cycle. The primary endpoint was 1-year PFS. Key secondary endpoints were overall response rate (ORR), complete response rate (CRR), and 1-year overall survival (OS). Seventy-five patients were enrolled, and 8 were excluded from analysis. ORR was 86.6% and CRR was 59.7%. One-year PFS was 88.9% (95% confidence interval [CI] 80.7-94.3%) and 1-year OS in 75 patients was 97.3% (95% CI 89.6-99.3%). One-year PFS was significantly inferior in EOT-PET-positive patients (n = 9) compared with PET-negative patients (n = 58) (77.8% vs. 93.1%; p = 0.02). We confirmed that EOT-PET after second-line BR therapy could predict early progression in FL patients in first relapse.

Investigation of imaging features in contrast-enhanced magnetic resonance imaging of benign and malignant breast lesions.

PURPOSE: This study aimed to enhance the diagnostic accuracy of contrast-enhanced breast magnetic resonance imaging (MRI) using gadobutrol for differentiating benign breast lesions from malignant ones. Moreover, this study sought to address the limitations of current imaging techniques and criteria based on the Breast Imaging Reporting and Data System (BI-RADS). MATERIALS AND METHODS: In a multicenter retrospective study conducted in Japan, 200 women were included, comprising 100 with benign lesions and 100 with malignant lesions, all classified under BI-RADS categories 3 and 4. The MRI protocol included 3D fast gradient echo T1- weighted images with fat suppression, with gadobutrol as the contrast agent. The analysis involved evaluating patient and lesion characteristics, including age, size, location, fibroglandular tissue, background parenchymal enhancement (BPE), signal intensity, and the findings of mass and non-mass enhancement. In this study, univariate and multivariate logistic regression analyses were performed, along with decision tree analysis, to identify significant predictors for the classification of lesions. RESULTS: Differences in lesion characteristics were identified, which may influence malignancy risk. The multivariate logistic regression model revealed age, lesion location, shape, and signal intensity as significant predictors of malignancy. Decision tree analysis identified additional diagnostic factors, including lesion margin and BPE level. The decision tree models demonstrated high diagnostic accuracy, with the logistic regression model showing an area under the curve of 0.925 for masses and 0.829 for non-mass enhancements. CONCLUSION: This study underscores the importance of integrating patient age, lesion location, and BPE level into the BI-RADS criteria to improve the differentiation between benign and malignant breast lesions. This approach could minimize unnecessary biopsies and enhance clinical decision-making in breast cancer diagnostics, highlighting the effectiveness of gadobutrol in breast MRI evaluations.

Detection support of lesions in patients with prostate cancer using 18 F -PSMA 1007 PET/CT.

PURPOSE: This study proposes a detection support system for primary and metastatic lesions of prostate cancer using 18 F -PSMA 1007 positron emission tomography/computed tomography (PET/CT) images with non-image information, including patient metadata and location information of an input slice image. METHODS: A convolutional neural network with condition generators and feature-wise linear modulation (FiLM) layers was employed to allow input of not only PET/CT images but also non-image information, namely, Gleason score, flag of pre- or post-prostatectomy, and normalized z-coordinate of an input slice. We explored the insertion position of the FiLM layers to optimize the conditioning of the network using non-image information. RESULTS: 18 F -PSMA 1007 PET/CT images were collected from 163 patients with prostate cancer and applied to the proposed system in a threefold cross-validation manner to evaluate the performance. The proposed system achieved a Dice score of 0.5732 (per case) and sensitivity of 0.8200 (per lesion), which are 3.87 and 4.16 points higher than the network without non-image information. CONCLUSION: This study demonstrated the effectiveness of the use of non-image information, including metadata of the patient and location information of the input slice image, in the detection of prostate cancer from 18 F -PSMA 1007 PET/CT images. Improvement in the sensitivity of inactive and small lesions remains a future challenge.

Intraventricular Pleomorphic Xanthoastrocytoma: A Case Report and Systemic Review.

We present a unique case of a 45-year-old male with cerebral palsy, who experienced walking difficulties and altered consciousness. The initial MRI revealed an intraventricular mass that rapidly enlarged over a month, consisting of two distinct components with different characteristics on CT and MRI, and was associated with agenesis of the corpus callosum. Despite initial treatment, surgical intervention was necessary, where preoperative imaging suggested an exophytically growing glioblastoma. However, postsurgical pathological examination identified the mass as pleomorphic xanthoastrocytoma (PXA), World Health Organization (WHO) Classification of Tumours of the Central Nervous System (CNS) grade 3. This study is notable for its rarity and complexity, challenging standard diagnostic approaches. PXA is an uncommon astrocytic tumor, and its occurrence intraventricularly is extremely rare. This study highlights its unique imaging features and the critical role of MRI in preoperative assessment, underlining the tumor's unusual intraventricular location, and its relationship with corpus callosum agenesis. Our comprehensive review of PXA's history and imaging spectrum offers valuable insights for neuroradiologists and neurosurgeons, emphasizing the diagnostic challenges of such rare tumor locations and the importance of meticulous MRI analysis for accurate diagnosis.

Transient receptor potential cation channel subfamily V member 1 (TRPV1) targeted PET imaging.

Transient receptor potential cation channel subfamily V member 1 (TRPV1) was identified using capsaicin, a pungent compound that is present in red pepper. The activation of TRPV1 induces an influx of calcium ions into cells and causes excitation of sensory neurons, associating with thermal sensing, sweating and pain. TRPV1 is also identified in various types of cancer cells. The expression of TRPV1 in cancer cells depends on the type of cancer and the stage of the disease. Therefore, TRPV1 has been considered a potential target of medicinal chemistry for drug development, and blocking its activation may lead to cancer therapy and pain relief. However, the details of the pathophysiological function of TRPV1 in vivo are still unclear. To explore practical use of TRPV1, we focused on positron emission tomography imaging and developed a 11C-radiolabeled tracer to visualize TRPV1.

Association of excess visceral fat and severe illness in hospitalized COVID-19 patients in Japan: a retrospective cohort study.

BACKGROUND/OBJECTIVES: Obesity, defined by body mass index (BMI), is a well-known risk factor for the severity of coronavirus disease 2019 (COVID-19). Adipose tissue distribution has also been implicated as an important factor in the body's response to infection, and excess visceral fat (VF), which is prevalent in Japanese, may contribute significantly to the severity. Therefore, this study aimed to evaluate the association of obesity and VF with COVID-19 severe illness in Japan. SUBJECTS/METHODS: This retrospective cohort study involved 550 COVID-19 patients admitted to a tertiary care hospital with BMI and body composition data, including VF. The primary endpoint was severe illness, including death, due to COVID-19 during hospitalization. Logistic regression analysis was applied to examine the quartiles of BMI and VF on severe illness after adjusting for covariates such as age, sex, subcutaneous fat, paraspinal muscle radiodensity, and comorbidities affecting VF (COPD, cancer within 5 years, immunosuppressive agent use). RESULTS: The median age was 56.0 years; 71.8% were males. During hospitalization, 82 (14.9%) experienced COVID-19 severe illness. In the multivariate logistic regression analysis, Q4 of BMI was not significantly associated with severe illness compared to Q1 of BMI (OR 1.03; 95% CI 0.37-2.86; p = 0.95). Conversely, Q3 and Q4 of VF showed a higher risk for severe illness compared to Q1 of VF (OR 2.68; 95% CI 1.01-7.11; p = 0.04, OR 3.66; 95% CI 1.30-10.26; p = 0.01, respectively). Stratified analysis by BMI and adjusted for covariates showed the positive association of VF with severe illness only in the BMI < 25 kg/m2 group. CONCLUSIONS: High BMI was not an independent risk factor for COVID-19 severe illness in hospitalized patients in Japan, whereas excess VF significantly influenced severe illness, especially in patients with a BMI < 25 kg/m2.

[18F]FDG PET/CT Findings of Mass-Forming Actinomycosis in an Uncontrolled Diabetic Patient.

We report a case of cervicofacial actinomycosis mimicking malignant sarcoma in a 78-year-old man with diabetes. High lesion uptake and decreased cerebral uptake on [18F]FDG PET/CT provide a potentially important diagnostic clue suggesting infectious disease in a poorly controlled diabetic patient.

Phase I/IIa trial of 18F-prostate specific membrane antigen (PSMA) 1007 PET/CT in healthy volunteers and prostate cancer patients.

OBJECTIVE: 18F-PSMA 1007 is a promising PET tracer for prostate cancer. We aimed to examine the safety, biodistribution, radiation dosimetry, and clinical effectiveness in Japanese healthy volunteers and patients with prostate cancer. METHODS: Part A evaluated the pharmacokinetics and exposure doses in three healthy volunteers. Part B evaluated the diagnostic accuracy in patients with untreated preoperative prostate cancer (Cohort 1, n = 7) and patients with biochemical recurrence (Cohort 2, n = 3). All subjects received a single dose of 3.7 MBq/kg 18F-PSMA 1007. Results: 18F-PSMA 1007 was found to be safe and well tolerated in all subjects. No serous AEs or drug-related AEs were identified during the present study. The average blood radioactivity concentration reached a maximum of 47.87 ± 1.05 (percentage of injected dose [%ID]/ml) at 5 min and then decreased to 1.60 ± 0.78 in 6 h. The systemic radioactivity reached a maximum of 211.05 ± 6.77 (%ID$\times$103) at 5 min and decreased to 7.18 ± 3.91 in 6 h. The sensitivity and positive predictive value were 100% and 100% based on both pathologic and imaging confirmation as gold standard. In Cohort 1, 15 primary foci (11.9%) were >5 mm in the largest diameter and identified in 39 of 126 segments (30.1%). The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy for 60 min uptake time acquisition were 80.0, 96.5, 91.4, 91.2 and 91.3%, respectively. CONCLUSIONS: Our study revealed that 18F-PSMA 1007 was safe, well tolerated and showed high accuracy in the diagnosis of prostate cancer.

A New Deep Learning Algorithm for Detecting Spinal Metastases on Computed Tomography Images.

STUDY DESIGN: Retrospective diagnostic study. OBJECTIVE: To automatically detect osteolytic bone metastasis lesions in the thoracolumbar region using conventional computed tomography (CT) scans, we developed a new deep learning (DL)-based computer-aided detection model. SUMMARY OF BACKGROUND DATA: Radiographic detection of bone metastasis is often difficult, even for orthopedic surgeons and diagnostic radiologists, with a consequent risk for pathologic fracture or spinal cord injury. If we can improve detection rates, we will be able to prevent the deterioration of patients' quality of life at the end stage of cancer. MATERIALS AND METHODS: This study included CT scans acquired at Tokyo Medical and Dental University (TMDU) Hospital between 2016 and 2022. A total of 263 positive CT scans that included at least one osteolytic bone metastasis lesion in the thoracolumbar spine and 172 negative CT scans without bone metastasis were collected for the datasets to train and validate the DL algorithm. As a test data set, 20 positive and 20 negative CT scans were separately collected from the training and validation datasets. To evaluate the performance of the established artificial intelligence (AI) model, sensitivity, precision, F1-score, and specificity were calculated. The clinical utility of our AI model was also evaluated through observer studies involving six orthopaedic surgeons and six radiologists. RESULTS: Our AI model showed a sensitivity, precision, and F1-score of 0.78, 0.68, and 0.72 (per slice) and 0.75, 0.36, and 0.48 (per lesion), respectively. The observer studies revealed that our AI model had comparable sensitivity to orthopaedic or radiology experts and improved the sensitivity and F1-score of residents. CONCLUSION: We developed a novel DL-based AI model for detecting osteolytic bone metastases in the thoracolumbar spine. Although further improvement in accuracy is needed, the current AI model may be applied to current clinical practice. LEVEL OF EVIDENCE: Level III.

Deep convolutional neural network for differentiating between sarcoidosis and lymphoma based on [18F]FDG maximum-intensity projection images.

OBJECTIVES: To compare the [18F]FDG PET/CT findings of untreated sarcoidosis and malignant lymphoma (ML) and develop convolutional neural network (CNN) models to differentiate between these diseases using maximum intensity projection (MIP) [18F]FDG PET images. METHODS: We retrospectively collected data on consecutive patients newly diagnosed with sarcoidosis and ML who underwent [18F]FDG PET/CT before treatment. Two nuclear radiologists reviewed the images. CNN models were created using MIP PET images and evaluated with k-fold cross-validation. The points of interest were visualized using gradient-weighted class activation mapping (Grad-CAM). RESULTS: A total of 56 patients with sarcoidosis and 62 patients with ML were included. Patients with sarcoidosis had more prominent FDG accumulation in the mediastinal lymph nodes and lung lesions, while those with ML had more prominent accumulation in the cervical lymph nodes (all p < 0.001). For the mediastinal lymph nodes, sarcoidosis patients had significant FDG accumulation in the level 2, 4, 7, and 10 lymph nodes (all p < 0.01). Otherwise, the accumulation in ML patients tended to be in the level 1 lymph nodes (p = 0.08). The CNN model using frontal and lateral MIP images achieved an average accuracy of 0.890 (95% CI: 0.804-0.977), a sensitivity of 0.898 (95% CI: 0.782-1.000), a specificity of 0.907 (95% CI: 0.799-1.000), and an area under the curve of 0.963 (95% CI: 0.899-1.000). Grad-CAM showed that the model focused on the sites of abnormal FDG accumulation. CONCLUSIONS: CNN models based on differences in FDG accumulation sites archive high performance in differentiating between sarcoidosis and ML. CLINICAL RELEVANCE STATEMENT: We developed a CNN model using MIP images of [18F]FDG PET/CT to distinguish between sarcoidosis and malignant lymphoma. It achieved high performance and could be useful in diagnosing diseases with involvement across organs and lymph nodes. KEY POINTS: • There are differences in FDG distribution when comparing whole-body [18F]FDG PET/CT findings in patients with sarcoidosis and malignant lymphoma before treatment. • Convolutional neural networks, a type of deep learning technique, trained with maximum-intensity projection PET images from two angles showed high performance. • A deep learning model that utilizes differences in FDG distribution may be helpful in differentiating between diseases with lesions that are characteristically widespread among organs and lymph nodes.

Correlation between choline kinase alpha expression and 11C-choline accumulation in breast cancer using positron emission tomography/computed tomography: a retrospective study.

Choline kinase (CK) is reportedly overexpressed in various malignancies. Among its isoforms, CKα overexpression is presumably related to oncogenic change. Choline positron emission tomography (PET) is reportedly useful for detecting and evaluating therapy outcomes in malignancies. In this study, we investigated the correlation between CKα expression and 11C-choline accumulation in breast cancer cells. We also compared the CKα expression level with other pathological findings for investigating tumour activity. Fifty-six patients with breast cancer (mean age: 51 years) who underwent their first medical examination between May 2007 and December 2008 were enrolled. All the patients underwent 11C-choline PET/computed tomography imaging prior to surgery. The maximum standardised uptake value was recorded for evaluating 11C-choline accumulation. The intensity of CKα expression was classified using immunostaining. A significant correlation was observed between CKα expression and 11C-choline accumulation (P < 0.0001). A comparison of breast cancer mortality demonstrated that strong CKα expression was associated with a shorter survival time (P < 0.0001). 11C-choline accumulation was also negatively correlated with survival time (P < 0.0001). Tumours with strong CKα expression are reportedly highly active in breast cancer. A correlation was observed between CKα expression and 11C-choline accumulation, suggesting their role as prognostic indicators of breast cancer.

Deep learning model with collage images for the segmentation of dedicated breast positron emission tomography images.

BACKGROUND: Dedicated breast positron emission tomography (dbPET) has high contrast and resolution optimized for detecting small breast cancers, leading to its noisy characteristics. This study evaluated the application of deep learning to the automatic segmentation of abnormal uptakes on dbPET to facilitate the assessment of lesions. To address data scarcity in model training, we used collage images composed of cropped abnormal uptakes and normal breasts for data augmentation. METHODS: This retrospective study included 1598 examinations between April 2015 and August 2020. A U-Net-based model with an uptake shape classification head was trained using either the original or augmented dataset comprising collage images. The Dice score, which measures the pixel-wise agreement between a prediction and its ground truth, of the models was compared using the Wilcoxon signed-rank test. Moreover, the classification accuracies were evaluated. RESULTS: After applying the exclusion criteria, 662 breasts were included; among these, 217 breasts had abnormal uptakes (mean age: 58 ± 14 years). Abnormal uptakes on the cranio-caudal and mediolateral maximum intensity projection images of 217 breasts were annotated and labeled as focus, mass, or non-mass. The inclusion of collage images into the original dataset yielded a Dice score of 0.884 and classification accuracy of 91.5%. Improvement in the Dice score was observed across all subgroups, and the score of images without breast cancer improved significantly from 0.750 to 0.834 (effect size: 0.76, P = 0.02). CONCLUSIONS: Deep learning can be applied for the automatic segmentation of dbPET, and collage images can improve model performance.

Examining the effectiveness of a deep learning-based computer-aided breast cancer detection system for breast ultrasound.

PURPOSE: This study aimed to evaluate the clinical usefulness of a deep learning-based computer-aided detection (CADe) system for breast ultrasound. METHODS: The set of 88 training images was expanded to 14,000 positive images and 50,000 negative images. The CADe system was trained to detect lesions in real- time using deep learning with an improved model of YOLOv3-tiny. Eighteen readers evaluated 52 test image sets with and without CADe. Jackknife alternative free-response receiver operating characteristic analysis was used to estimate the effectiveness of this system in improving lesion detection. RESULT: The area under the curve (AUC) for image sets was 0.7726 with CADe and 0.6304 without CADe, with a 0.1422 difference, indicating that with CADe was significantly higher than that without CADe (p < 0.0001). The sensitivity per case was higher with CADe (95.4%) than without CADe (83.7%). The specificity of suspected breast cancer cases with CADe (86.6%) was higher than that without CADe (65.7%). The number of false positives per case (FPC) was lower with CADe (0.22) than without CADe (0.43). CONCLUSION: The use of a deep learning-based CADe system for breast ultrasound by readers significantly improved their reading ability. This system is expected to contribute to highly accurate breast cancer screening and diagnosis.

Prognostic impact of the radiological infiltrative feature of primary renal tumor in metastatic renal cell carcinoma.

OBJECTIVES: Recent studies suggest that the radiological infiltrative feature (r-IF) of renal tumors is strongly correlated with poor oncologic outcomes in locally advanced renal cell carcinoma (RCC). This study investigated the prognostic impact of r-IF of primary renal tumors in metastatic RCC (mRCC) in comparison with International Metastatic RCC Database Consortium (IMDC) risk model. METHODS: We retrospectively analyzed 91 patients with previously untreated mRCC. Dynamic computed tomography of the primary renal tumor was reviewed to assess r-IF, defined as a focally/extensively ill-defined tumor interface with normal renal parenchyma. RESULTS: The median age was 67 years, and 69 patients (76%) were men. Prior nephrectomy was performed in 47 patients (52%). The median size of the primary renal tumor was 6.7 cm, and 50 patients (55%) presented with cT3-4 stage. Overall, 25 (28%)/52 (57%)/14 (15%) patients were classified into IMDC favorable/intermediate/poor-risk groups, respectively. An image review identified r-IFs in the primary renal tumor in 40 patients (44%). The incidences of r-IFs were 28%/46%/64% in IMDC favorable/intermediate/poor-risk groups, respectively. During a median follow-up of 2.6 years, 31 patients (34%) died of RCC. On multivariable analysis, r-IF and IMDC intermediate-poor risks were independently associated with poor cancer-specific survival (CSS). Two-year CSS were 64%/87% in patients with/without r-IF, respectively. C-index was improved from 0.73 to 0.81 by adding r-IF to the IMDC risk factors. CONCLUSIONS: R-IF of the primary renal tumor was an independent risk factor for poor CSS in patients with mRCC, which may improve the prognostic accuracy when combined with the IMDC risk model.

Novel utility of Vesical Imaging-Reporting and Data System in multimodal treatment for muscle-invasive bladder cancer.

OBJECTIVES: To examine the clinical significance of the Vesical Imaging-Reporting and Data System (VI-RADS) in predicting outcome of multimodal treatment (MMT) in muscle-invasive bladder cancer (MIBC) patients. METHODS: We reviewed 78 pathologically proven MIBC patients who underwent MMT including transurethral resection and chemoradiotherapy, followed by partial or radical cystectomy. Treatment response was assessed through histologic evaluation of cystectomy specimens. Two radiologists categorized the index lesions of pretherapeutic MRI according to the 5-point VI-RADS score. The associations of VI-RADS score with the therapeutic effect of MMT were analyzed. The diagnostic performance of VI-RADS scores with a cut-off VI-RADS scores ≤ 2 or ≤ 3 for predicting pathologic complete response to MMT (MMT-CR) was evaluated. RESULTS: MMT-CR was achieved in 2 (100%) of VI-RADS score 1 (n = 2), 16 (84%) of score 2 (n = 19), 12 (86%) of score 3 (n = 14), 7 (64%) of score 4 (n = 11), and 14 (44%) of score 5 (n = 32). VI-RADS score was inversely associated with the incidence of MMT-CR (p = 0.00049). The cut-off VI-RADS score ≤ 2 and ≤ 3 could predict the favorable therapeutic outcome of MMT with high specificity (0.89 with 95% confidence interval [CI]: 0.71-0.98 and 0.82 with 95% CI: 0.62-0.94, respectively) and high positive predictive value (0.86 with 95% CI: 0.64-0.97 and 0.86 with 95% CI: 0.70-0.95, respectively). CONCLUSION: VI-RADS score may serve as an imaging marker in MIBC patients for predicting the therapeutic outcome of MMT. CLINICAL RELEVANCE STATEMENT: Muscle-invasive bladder cancer patients with a lower Vesical Imaging-Reporting and Data System score can be a good candidate for bladder-sparing treatment incorporating multimodal treatment. KEY POINTS: • Vesical Imaging-Reporting and Data System (VI-RADS) score was potentially valuable for classifying pathologic tumor response in patients with muscle-invasive bladder cancer. • The likelihood of achieving complete response of multimodal treatment (MMT) decreased with increasing VI-RADS score. • VI-RADS score could serve as an imaging marker that optimizes patient selection for MMT.