Development of a novel prediction model for differential diagnosis between spinal myxopapillary ependymoma and schwannoma.

Spinal myxopapillary ependymoma (MPE) and schwannoma represent clinically distinct intradural extramedullary tumors, albeit with shared and overlapping magnetic resonance imaging (MRI) characteristics. We aimed to identify significant MRI features that can differentiate between MPE and schwannoma and develop a novel prediction model using these features. In this study, 77 patients with MPE (n = 24) or schwannoma (n = 53) who underwent preoperative MRI and surgical removal between January 2012 and December 2022 were included. MRI features, including intratumoral T2 dark signals, subarachnoid hemorrhage (SAH), leptomeningeal seeding, and enhancement patterns, were analyzed. Logistic regression analysis was conducted to distinguish between MPE and schwannomas based on MRI parameters, and a prediction model was developed using significant MRI parameters. The model was validated internally using a stratified tenfold cross-validation. The area under the curve (AUC) was calculated based on the receiver operating characteristic curve analysis. MPEs had a significantly larger mean size (p = 0.0035), higher frequency of intratumoral T2 dark signals (p = 0.0021), associated SAH (p = 0.0377), and leptomeningeal seeding (p = 0.0377). Focal and diffuse heterogeneous enhancement patterns were significantly more common in MPEs (p = 0.0049 and 0.0038, respectively). Multivariable analyses showed that intratumoral T2 dark signal (p = 0.0439) and focal (p = 0.0029) and diffuse enhancement patterns (p = 0.0398) were independent factors. The prediction model showed an AUC of 0.9204 (95% CI 0.8532-0.9876) and the average AUC for internal validation was 0.9210 (95% CI 0.9160-0.9270). MRI provides useful data for differentiating spinal MPEs from schwannomas. The prediction model developed based on the MRI features demonstrated excellent discriminatory performance.

Use of individualized 3D-printed models of pancreatic cancer to improve surgeons' anatomic understanding and surgical planning.

OBJECTIVES: Three-dimensional (3D) printing has been increasingly used to create accurate patient-specific 3D-printed models from medical imaging data. We aimed to evaluate the utility of 3D-printed models in the localization and understanding of pancreatic cancer for surgeons before pancreatic surgery. METHODS: Between March and September 2021, we prospectively enrolled 10 patients with suspected pancreatic cancer who were scheduled for surgery. We created an individualized 3D-printed model from preoperative CT images. Six surgeons (three staff and three residents) evaluated the CT images before and after the presentation of the 3D-printed model using a 7-item questionnaire (understanding of anatomy and pancreatic cancer [Q1-4], preoperative planning [Q5], and education for trainees or patients [Q6-7]) on a 5-point scale. Survey scores on Q1-5 before and after the presentation of the 3D-printed model were compared. Q6-7 assessed the 3D-printed model's effects on education compared to CT. Subgroup analysis was performed between staff and residents. RESULTS: After the 3D-printed model presentation, survey scores improved in all five questions (before 3.90 vs. after 4.56, p < 0.001), with a mean improvement of 0.57‒0.93. Staff and resident scores improved after a 3D-printed model presentation (p < 0.05), except for Q4 in the resident group. The mean difference was higher among the staff than among the residents (staff: 0.50‒0.97 vs. residents: 0.27‒0.90). The scores of the 3D-printed model for education were high (trainees: 4.47 vs. patients: 4.60) compared to CT. CONCLUSION: The 3D-printed model of pancreatic cancer improved surgeons' understanding of individual patients' pancreatic cancer and surgical planning. CLINICAL RELEVANCE STATEMENT: The 3D-printed model of pancreatic cancer can be created using a preoperative CT image, which not only assists surgeons in surgical planning but also serves as a valuable educational resource for patients and students. KEY POINTS: • A personalized 3D-printed pancreatic cancer model provides more intuitive information than CT, allowing surgeons to better visualize the tumor's location and relationship to neighboring organs. • In particular, the survey score was higher among staff who performed the surgery than among residents. • Individual patient pancreatic cancer models have the potential to be used for personalized patient education as well as resident education.

Evaluation of Response to Immune Checkpoint Inhibitors Using a Radiomics, Lesion-Level Approach.

Conventional methods to determine the response to immune checkpoint inhibitors (ICIs) are limited by the unique responses to an ICI. We performed a radiomics approach for all measurable lesions to identify radiomic variables that could distinguish hyperprogressive disease (HPD) on baseline CT scans and classify a dissociated response (DR). One hundred and ninety-six patients with advanced lung cancer, treated with ICI monotherapy, who underwent at least three CT scans, were retrospectively enrolled. For all 621 measurable lesions, HPDv was determined from baseline CT scans using the tumor growth kinetics (TGK) ratio, and radiomics features were extracted. Multivariable logistic regression analysis of radiomics features was performed to discriminate DR. Radiomics features that significantly discriminated HPDv on baseline CT differed according to organ. Of the 196 patients, 54 (27.6%) had a DR and 142 (72.4%) did not have a DR. Overall survival in the group with a DR was significantly inferior to that in the group without a DR (log rank test, p = 0.04). Our study shows that lesion-level analysis using radiomics features has great potential for discriminating HPDv and understanding heterogeneous tumor progression, including a DR, after ICI treatment.

Prostate cancer: diagnostic yield of modified transrectal ultrasound-guided twelve-core combined biopsy (targeted plus systematic biopsies) using prebiopsy magnetic resonance imaging.

PURPOSE: This study aimed to analyze the diagnostic yield of modified transrectal ultrasound (TRUS)-guided 12-core combined biopsy (CB) using prebiopsy magnetic resonance imaging (MRI) for detecting clinically significant prostate cancer (csPCa). METHODS: This retrospective study included 130 consecutive patients who underwent modified TRUS-guided 12-core CB using cognitive fusion for lesions of Prostate Imaging-Reporting and Data System (PI-RADS) category ≥ 3. The 12-core CB comprised 3-6-core targeted biopsy (TB) and systematic biopsy (SB). For SB, tissue sampling in TB regions was omitted, and 3-core sampling (i.e., apex, mid, and base) in the contralateral peripheral zone of TB was mandatory. csPCa was defined as International Society of Urological Pathology (ISUP) grade ≥ 2 cancer. The per-patient cancer detection rates (CDRs) according to biopsy type or PI-RADS category were investigated. RESULTS: The CDRs of TB, SB, and CB for csPCa were 47.7% (62/130 patients), 29.2% (38/130), and 52.3% (68/130), respectively. For csPCa, the CDRs of TB and CB according to PI-RADS categories of 3, 4, or 5 were 25.0% (8/32) and 31.3% (10/32), 41.2% (28/68) and 45.6% (31/68), or 86.7% (26/30) and 90.0% (27/30), respectively. In 6 (4.6%) patients, csPCa was detected only by SB. In 18 (13.8%) patients, SB detected PCa of a higher ISUP grade than TB. In 11 (8.5%) patients, SB detected csPCa at contralateral peripheral zone of TB. CONCLUSION: Modified TRUS-guided 12-core CB using prebiopsy MRI seems to be feasible. It may reduce total biopsy cores in patients who are suitable for CB based on prebiopsy MRI findings.