Radiology. Imaging cancer最新文献

{"title":"Intraprocedural Diffusion-weighted Imaging for Predicting Ablation Zone during MRI-guided Focused Ultrasound of Prostate Cancer.","authors":"Rachel R Bitton, Wei Shao, Yosef Chodakeiwitz, Ryan L Brunsing, Geoffery Sonn, Mirabela Rusu, Pejman Ghanouni","doi":"10.1148/rycan.240009","DOIUrl":"10.1148/rycan.240009","url":null,"abstract":"<p><p>Purpose To compare diffusion-weighted imaging (DWI) with thermal dosimetry as a noncontrast method to predict ablation margins in individuals with prostate cancer treated with MRI-guided focused ultrasound (MRgFUS) ablation. Materials and Methods This secondary analysis of a prospective trial (ClinicalTrials.gov no. NCT01657942) included 17 participants (mean age, 64 years ± 6 [SD]; all male) who were treated for prostate cancer using MRgFUS in whom DWI was performed immediately after treatment. Ablation contours from computed thermal dosimetry and DWI as drawn by two blinded radiologists were compared against the reference standard of ablation assessment, posttreatment contrast-enhanced nonperfused volume (NPV) contours. The ability of each method to predict the ablation zone was analyzed quantitively using Dice similarity coefficients (DSCs) and mean Hausdorff distances (mHDs). Results DWI revealed a hyperintense rim at the margin of the ablation zone. While DWI accurately helped predict treatment margins, thermal dose contours underestimated the extent of the ablation zone compared with the T1-weighted NPV imaging reference standard. Quantitatively, contour assessment between methods showed that DWI-drawn contours matched postcontrast NPV contours (mean DSC = 0.84 ± 0.05 for DWI, mHD = 0.27 mm ± 0.13) better than the thermal dose contours did (mean DSC = 0.64 ± 0.12, mHD = 1.53 mm ± 1.20) (<i>P</i> < .001). Conclusion This study demonstrates that DWI, which can visualize the ablation zone directly, is a promising noncontrast method that is robust to treatment-related bulk motion compared with thermal dosimetry and correlates better than thermal dosimetry with the reference standard T1-weighted NPV. <b>Keywords:</b> Interventional-Body, Ultrasound-High-Intensity Focused (HIFU), Genital/Reproductive, Prostate, Oncology, Imaging Sequences, MRI-guided Focused Ultrasound, MR Thermometry, Diffusionweighted Imaging, Prostate Cancer ClinicalTrials.gov Identifier no. NCT01657942 <i>Supplemental material is available for this article.</i> © RSNA, 2024.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 5","pages":"e240009"},"PeriodicalIF":5.6,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11449221/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142111365","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient Characteristics Impact False Positives in AI Interpretation of True-Negative Screening Breast Tomosynthesis Examinations.","authors":"Nour Homsi, Maggie Chung","doi":"10.1148/rycan.249015","DOIUrl":"10.1148/rycan.249015","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 5","pages":"e249015"},"PeriodicalIF":5.6,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11443469/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141988735","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rosai-Dorfman Disease Mimicking Metastatic Disease.","authors":"Sofia Velasco, Santiago Aristizábal-Ortiz, Angela Guarnizo","doi":"10.1148/rycan.240109","DOIUrl":"10.1148/rycan.240109","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 5","pages":"e240109"},"PeriodicalIF":5.6,"publicationDate":"2024-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11443498/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142036772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-enhanced Mammography With Digital Breast Tomosynthesis for Breast Cancer Detection: Clinical Value and Comparison With Human Performance.","authors":"Daphne Resch, Roberto Lo Gullo, Jonas Teuwen, Friedrich Semturs, Johann Hummel, Alexandra Resch, Katja Pinker","doi":"10.1148/rycan.230149","DOIUrl":"10.1148/rycan.230149","url":null,"abstract":"<p><p>Purpose To compare two deep learning-based commercially available artificial intelligence (AI) systems for mammography with digital breast tomosynthesis (DBT) and benchmark them against the performance of radiologists. Materials and Methods This retrospective study included consecutive asymptomatic patients who underwent mammography with DBT (2019-2020). Two AI systems (Transpara 1.7.0 and ProFound AI 3.0) were used to evaluate the DBT examinations. The systems were compared using receiver operating characteristic (ROC) analysis to calculate the area under the ROC curve (AUC) for detecting malignancy overall and within subgroups based on mammographic breast density. Breast Imaging Reporting and Data System results obtained from standard-of-care human double-reading were compared against AI results with use of the DeLong test. Results Of 419 female patients (median age, 60 years [IQR, 52-70 years]) included, 58 had histologically proven breast cancer. The AUC was 0.86 (95% CI: 0.85, 0.91), 0.93 (95% CI: 0.90, 0.95), and 0.98 (95% CI: 0.96, 0.99) for Transpara, ProFound AI, and human double-reading, respectively. For Transpara, a rule-out criterion of score 7 or lower yielded 100% (95% CI: 94.2, 100.0) sensitivity and 60.9% (95% CI: 55.7, 66.0) specificity. The rule-in criterion of higher than score 9 yielded 96.6% sensitivity (95% CI: 88.1, 99.6) and 78.1% specificity (95% CI: 73.8, 82.5). For ProFound AI, a rule-out criterion of lower than score 51 yielded 100% sensitivity (95% CI: 93.8, 100) and 67.0% specificity (95% CI: 62.2, 72.1). The rule-in criterion of higher than score 69 yielded 93.1% (95% CI: 83.3, 98.1) sensitivity and 82.0% (95% CI: 77.9, 86.1) specificity. Conclusion Both AI systems showed high performance in breast cancer detection but lower performance compared with human double-reading. <b>Keywords:</b> Mammography, Breast, Oncology, Artificial Intelligence, Deep Learning, Digital Breast Tomosynthesis © RSNA, 2024.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 4","pages":"e230149"},"PeriodicalIF":5.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11287230/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141591225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Calcified Primary Signet Ring Cell Carcinoma of the Colon with Metastases.","authors":"Sanil Garg, Amit Gupta, Krithika Rangarajan, Rajni Yadav, Mukesh Kumar","doi":"10.1148/rycan.240079","DOIUrl":"10.1148/rycan.240079","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 4","pages":"e240079"},"PeriodicalIF":5.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11287222/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141591226","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Innovative Advances in Molecular Breast Imaging Biopsy.","authors":"Amy M Fowler","doi":"10.1148/rycan.240135","DOIUrl":"10.1148/rycan.240135","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 4","pages":"e240135"},"PeriodicalIF":5.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11287228/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141470403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital Breast Tomosynthesis for Nonimplant-displaced Views May Be Safely Omitted at Screening Mammography.","authors":"Brandon K K Fields, Bonnie N Joe","doi":"10.1148/rycan.249014","DOIUrl":"10.1148/rycan.249014","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 4","pages":"e249014"},"PeriodicalIF":5.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11287225/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141470401","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mean Apparent Propagator MRI: Quantitative Assessment of Tumor-Stroma Ratio in Invasive Ductal Breast Carcinoma.","authors":"Xiang Zhang, Ya Qiu, Wei Jiang, Zehong Yang, Mengzhu Wang, Qin Li, Yeqing Liu, Xu Yan, Guang Yang, Jun Shen","doi":"10.1148/rycan.230165","DOIUrl":"10.1148/rycan.230165","url":null,"abstract":"<p><p>Purpose To determine whether metrics from mean apparent propagator (MAP) MRI perform better than apparent diffusion coefficient (ADC) value in assessing the tumor-stroma ratio (TSR) status in breast carcinoma. Materials and Methods From August 2021 to October 2022, 271 participants were prospectively enrolled (ClinicalTrials.gov identifier: NCT05159323) and underwent breast diffusion spectral imaging and diffusion-weighted imaging. MAP MRI metrics and ADC were derived from the diffusion MRI data. All participants were divided into high-TSR (stromal component < 50%) and low-TSR (stromal component ≥ 50%) groups based on pathologic examination. Clinicopathologic characteristics were collected, and MRI findings were assessed. Logistic regression was used to determine the independent variables for distinguishing TSR status. The area under the receiver operating characteristic curve (AUC) and sensitivity, specificity, and accuracy were compared between the MAP MRI metrics, either alone or combined with clinicopathologic characteristics, and ADC, using the DeLong and McNemar test. Results A total of 181 female participants (mean age, 49 years ± 10 [SD]) were included. All diffusion MRI metrics differed between the high-TSR and low-TSR groups (<i>P</i> < .001 to <i>P</i> = .01). Radial non-Gaussianity from MAP MRI and lymphovascular invasion were significant independent variables for discriminating the two groups, with a higher AUC (0.81 [95% CI: 0.74, 0.87] vs 0.61 [95% CI: 0.53, 0.68], <i>P</i> < .001) and accuracy (138 of 181 [76%] vs 106 of 181 [59%], <i>P</i> < .001) than that of the ADC. Conclusion MAP MRI may serve as a better approach than conventional diffusion-weighted imaging in evaluating the TSR of breast carcinoma. <b>Keywords:</b> MR Diffusion-weighted Imaging, MR Imaging, Breast, Oncology ClinicalTrials.gov Identifier: NCT05159323 <i>Supplemental material is available for this article.</i> © RSNA, 2024.</p>","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 4","pages":"e230165"},"PeriodicalIF":5.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11287226/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141318136","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI Systems for Mammography with Digital Breast Tomosynthesis: Expectations and Challenges.","authors":"Masako Kataoka, Takayoshi Uematsu","doi":"10.1148/rycan.240171","DOIUrl":"10.1148/rycan.240171","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 4","pages":"e240171"},"PeriodicalIF":5.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11287227/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141760545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Patient Positioning by Online Adaptive Radiation Therapy.","authors":"Paolo Farace","doi":"10.1148/rycan.240120","DOIUrl":"10.1148/rycan.240120","url":null,"abstract":"","PeriodicalId":20786,"journal":{"name":"Radiology. Imaging cancer","volume":"6 4","pages":"e240120"},"PeriodicalIF":5.6,"publicationDate":"2024-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11289739/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141591227","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}