Insights into Imaging最新文献

{"title":"Classification and segmentation of hip fractures in x-rays: highlighting fracture regions for interpretable diagnosis.","authors":"Germán González, Joaquín Galant, José María Salinas, Emilia Benítez, Maria Dolores Sánchez-Valverde, Jorge Calbo, Nicolás Cerrolaza","doi":"10.1186/s13244-025-01958-y","DOIUrl":"https://doi.org/10.1186/s13244-025-01958-y","url":null,"abstract":"<p><strong>Objective: </strong>To develop an artificial intelligence (AI) system capable of classifying and segmenting femoral fractures. To compare its performance against existing state-of-the-art methods.</p><p><strong>Methods: </strong>This Institutional Review Board (IRB)-approved retrospective study did not require informed consent. 10,308 hip x-rays from 2618 patients were retrieved from the hospital PACS. 986 were randomly selected for annotation and randomly split into training, validation, and test sets at the patient level. Two radiologists segmented and classified femoral fractures based on their location (femoral neck, pertrochanteric region, or subtrochanteric region) and grade, using the Evans and Garden scales for neck and pertrochanteric regions, respectively. A YOLOv8 segmentation convolutional neural network (CNN) was trained to generate fracture masks and indicate their class and grade. Classification CNNs were trained in the same dataset for method comparison.</p><p><strong>Results: </strong>On the test set, YOLOv8 achieved a Dice coefficient of 0.77 (95% CI: 0.56-0.98) for segmenting fractures, an accuracy of 86.2% (95% CI: 80.77-90.55) for classification and grading, and an AUC of 0.981 (95% CI: 0.965-0.997) for fracture detection. These metrics are on par with or exceed those of previously published AI methods, demonstrating the efficacy of our approach.</p><p><strong>Conclusions: </strong>The high accuracy and AUC values demonstrate the potential of the proposed neural network as a reliable tool in clinical settings. Further, it is the first to provide a precise segmentation of femoral fractures, as indicated by the Dice scores, which may enhance interpretability. A formal evaluation is planned to further assess its clinical applicability.</p><p><strong>Critical relevance statement: </strong>The proposed system offers high granularity in fracture classification and is the first to segment femoral fractures, ensuring interpretability.</p><p><strong>Key points: </strong>We present the first AI method that segments and grades femoral fractures. The method classifies fractures with fracture location and type. High accuracy and interpretability promise utility in clinical practice.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"86"},"PeriodicalIF":4.1,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12000489/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144021768","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction: Fluoroscopy-guided aspiration of the acutely dislocated total hip arthroplasty: a feasible, high-yield, and safe procedure.","authors":"Dyan V Flores, Abdullah Felemban, Taryn Hodgdon, Paul Beaulé, George Grammatopoulos, Kawan S Rakhra","doi":"10.1186/s13244-025-01963-1","DOIUrl":"https://doi.org/10.1186/s13244-025-01963-1","url":null,"abstract":"","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"85"},"PeriodicalIF":4.1,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11996738/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143985417","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AI-based automatic estimation of single-kidney glomerular filtration rate and split renal function using non-contrast CT.","authors":"Yiwei Wang, Feng Xu, Qiuyue Han, Daoying Geng, Xin Gao, Bin Xu, Wei Xia","doi":"10.1186/s13244-025-01959-x","DOIUrl":"10.1186/s13244-025-01959-x","url":null,"abstract":"<p><strong>Objectives: </strong>To address SPECT's radioactivity, complexity, and costliness in measuring renal function, this study employs artificial intelligence (AI) with non-contrast CT to estimate single-kidney glomerular filtration rate (GFR) and split renal function (SRF).</p><p><strong>Methods: </strong>245 patients with atrophic kidney or hydronephrosis were included from two centers (Training set: 128 patients from Center I; Test set: 117 patients from Center II). The renal parenchyma and hydronephrosis regions in non-contrast CT were automatically segmented by deep learning. Radiomic features were extracted and combined with clinical characteristics using multivariable linear regression (MLR) to obtain a radiomics-clinical-estimated GFR (rcGFR). The relative contribution of single-kidney rcGFR to overall rcGFR, the percent renal parenchymal volume, and the percent renal hydronephrosis volume were combined by MLR to generate the estimation of SRF (rcphSRF). The Pearson correlation coefficient (r), mean absolute error (MAE), and Lin's concordance coefficient (CCC) were calculated to evaluate the correlations, differences, and agreements between estimations and SPECT-based measurements, respectively.</p><p><strong>Results: </strong>Compared to manual segmentation, deep learning-based automatic segmentation could reduce the average segmentation time by 434.6 times to 3.4 s. Compared to single-kidney GFR measured by SPECT, the rcGFR had a significant correlation of r = 0.75 (p < 0.001), MAE of 10.66 mL/min/1.73 m², and CCC of 0.70. Compared to SRF measured by SPECT, the rcphSRF had a significant correlation of r = 0.92 (p < 0.001), MAE of 7.87%, and CCC of 0.88.</p><p><strong>Conclusions: </strong>The non-contrast CT and AI methods are feasible to estimate single-kidney GFR and SRF in patients with atrophic kidney or hydronephrosis.</p><p><strong>Critical relevance statement: </strong>For patients with an atrophic kidney or hydronephrosis, non-contrast CT and artificial intelligence methods can be used to estimate single-kidney glomerular filtration rate and split renal function, which may minimize the radiation risk, enhance diagnostic efficiency, and reduce costs.</p><p><strong>Key points: </strong>Renal function can be assessed using non-contrast CT and AI. Estimated renal function significantly correlated with the SPECT-based measurements. The efficiency of renal function estimation can be refined by the proposed method.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"84"},"PeriodicalIF":4.1,"publicationDate":"2025-04-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11977085/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143795076","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Reply to the Letter to the Editor: Should all trainees \"do research\"?","authors":"Luis Martí-Bonmatí","doi":"10.1186/s13244-025-01954-2","DOIUrl":"10.1186/s13244-025-01954-2","url":null,"abstract":"","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"80"},"PeriodicalIF":4.1,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11971094/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143788296","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Renal angiomyolipoma-investigating radiological signs indicative of risk for bleeding.","authors":"Jesper Swärd, Karl Bohlin, Olof Henrikson, Sven Lundstam, Ralph Peeker, Anna Grenabo Bergdahl","doi":"10.1186/s13244-025-01957-z","DOIUrl":"10.1186/s13244-025-01957-z","url":null,"abstract":"<p><strong>Objectives: </strong>To compare imaging differences between bleeding and non-bleeding angiomyolipoma with respect to the proportion and attenuation of the angiomyogenic component and the occurrence and size of aneurysms.</p><p><strong>Materials and methods: </strong>CT scans and angiographies preceding 58 consecutive embolisations at two institutions from 1999 to 2018 were analysed retrospectively. Tumour volume was measured by contouring the angiomyolipoma on CT scans. The partial volume of the angiomyogenic component (blood vessels and smooth muscle relative to fatty tissue) was derived using attenuation threshold values measured in Hounsfield Units.</p><p><strong>Results: </strong>Bleeding angiomyolipoma exhibited a significantly higher proportion of angiomyogenic component (23%) than non-bleeding angiomyolipoma (8%) (p = 0.042). Angiomyolipoma with 0-5% angiomyogenic component had a lower risk of bleeding compared to those with ≥ 5% angiomyogenic component (13% vs 42%). Mean attenuation values of angiomyogenic components did not differ between bleeders and non-bleeders. Aneurysms were observed in 24% of angiomyolipoma during angiography. No statistically significant association was found between the occurrence of aneurysms and bleeding, neither when all aneurysms were included nor when only aneurysms ≥ 5 mm were considered. Tuberous sclerosis patients had larger tumours (11.4 cm vs 6.0 cm), but no significant difference in bleeding was observed (p = 0.53).</p><p><strong>Conclusions: </strong>A higher proportion of the angiomyogenic component in bleeding renal angiomyolipoma suggests a possible association with bleeding. Angiomyolipoma with less than 5% angiomyogenic components may represent a subgroup with a reduced risk of bleeding. Our findings do not confirm the widely accepted assumption that aneurysms significantly increase the risk of bleeding.</p><p><strong>Critical relevance statement: </strong>Measuring the angiomyogenic component in renal angiomyolipoma could help address current knowledge gaps and aid in the more efficient selection of patients for therapeutic interventions.</p><p><strong>Key points: </strong>Identifying risk factors for bleeding beyond tumour size is important. Very low angiomyogenic component tumours may have reduced bleeding risk. The presence of aneurysms may not significantly increase bleeding risk. Reporting angiomyogenic proportion on CT may aid in treatment decisions.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"83"},"PeriodicalIF":4.1,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11972250/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143788273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quality of prostate MRI in early diagnosis-a national survey and reading evaluation.","authors":"Linda C P Thijssen, Jasper J Twilt, Tristan Barrett, Francesco Giganti, Ivo G Schoots, Rianne R M Engels, Mireille J M Broeders, Jelle O Barentsz, Maarten de Rooij","doi":"10.1186/s13244-025-01960-4","DOIUrl":"10.1186/s13244-025-01960-4","url":null,"abstract":"<p><strong>Objectives: </strong>The reliability of image-based recommendations in the prostate cancer pathway is partially dependent on prostate MRI image quality. We evaluated the current compliance with PI-RADSv2.1 technical recommendations and the prostate MRI image quality in the Netherlands. To aid image quality improvement, we identified factors that possibly influence image quality.</p><p><strong>Materials and methods: </strong>A survey was sent to 68 Dutch medical centres to acquire information on prostate MRI acquisition. The responding medical centres were requested to provide anonymised prostate MRI examinations of biopsy-naive men suspected of prostate cancer. The images were evaluated for quality by three expert prostate radiologists. The compliance with PI-RADSv2.1 technical recommendations and the PI-QUALv2 score was calculated. Relationships between hardware, education of personnel, technical parameters, and/or patient preparation and both compliance and image quality were analysed using Pearson correlation, Mann-Whitney U-test, or Student's t-test where appropriate.</p><p><strong>Results: </strong>Forty-four medical centres submitted their compliance with PI-RADSv2.1 technical recommendations, and 26 medical centres completed the full survey. Thirteen hospitals provided 252 usable images. The mean compliance with technical recommendations was 79%. Inadequate PI-QUALv2 scores were given in 30.9% and 50.6% of the mp-MRI and bp-MRI examinations, respectively. Multiple factors with a possible relationship with image quality were identified.</p><p><strong>Conclusion: </strong>In the Netherlands, the average compliance with PI-RADSv2.1 technical recommendations is high. Prostate MRI image quality was inadequate in 30-50% of the provided examinations. Many factors not covered in the PI-RADSv2.1 technical recommendations can influence image quality. Improvement of prostate MRI image quality is needed.</p><p><strong>Critical relevance statement: </strong>It is essential to improve the image quality of prostate MRIs, which can be achieved by addressing factors not covered in the PI-RADSv2.1 technical recommendations.</p><p><strong>Key points: </strong>Prostate MRI image quality influences the diagnostic accuracy of image-based decisions. Thirty to fifty percent of Dutch prostate MRI examinations were of inadequate image quality. We identified multiple factors with possible influence on image quality.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"82"},"PeriodicalIF":4.1,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11972232/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143788173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"CT-based radiomics deep learning signatures for non-invasive prediction of metastatic potential in pheochromocytoma and paraganglioma: a multicohort study.","authors":"Yongjie Zhou, Yuan Zhan, Jinhong Zhao, Linhua Zhong, Fei Zou, Xuechao Zhu, Qiao Zeng, Jiayu Nan, Lianggeng Gong, Yongming Tan, Lan Liu","doi":"10.1186/s13244-025-01952-4","DOIUrl":"10.1186/s13244-025-01952-4","url":null,"abstract":"<p><strong>Objectives: </strong>This study aimed to develop and validate CT-based radiomics deep learning signatures for the non-invasive prediction of metastatic potential in pheochromocytomas and paragangliomas (PPGLs).</p><p><strong>Methods: </strong>We conducted a retrospective analysis of 249 PPGL patients from three institutions, dividing them into training (n = 138), test1 (n = 71), and test2 (n = 40) sets. Based on the grading system for adrenal pheochromocytoma and paraganglioma (GAPP), patients were classified into low-risk (GAPP < 3) and high-risk (GAPP ≥ 3) groups. Radiomic features were extracted from CT venous phase images and modeled using six machine learning algorithms. The maximum 2D sections and 3D images of each tumor were input into four ResNet models to obtain predictive probabilities. Optimal models were selected based on receiver operating characteristic analysis and integrated with radiological features to develop a combined model, which was evaluated on external datasets, and explored prognostic information.</p><p><strong>Results: </strong>The support vector machine radiomics and 2D ResNet-50 models demonstrated good performance. By integrating these two models with intratumoral necrosis features, we constructed a combined model that achieved high accuracy, with area under the curve (AUC) values of 0.90 for the training, 0.86 for the test1, and 0.88 for the test2 sets. This model effectively stratified patients based on metastasis-free survival (p = 0.003). Its predictive ability remains robust below the 6 cm threshold, with AUC values exceeding 0.87 across all datasets.</p><p><strong>Conclusions: </strong>The combined model can predict the metastatic potential of PPGL in the preoperative stage, providing a precise surgical strategy for pheochromocytoma regarding the 6 cm surgical threshold.</p><p><strong>Critical relevance statement: </strong>The combined model, established based on radiomic and deep learning signatures, shows potential for early preoperative prediction of metastatic potential in PPGL.</p><p><strong>Key points: </strong>Metastatic potential of PPGL affects surgical approaches and prognosis. CT-based radiomics deep learning signatures can predict the metastatic potential in PPGL.3. The combined model's predictive ability remains robust below the 6-cm threshold. The combined model's predictive ability remains robust below the 6-cm threshold.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"81"},"PeriodicalIF":4.1,"publicationDate":"2025-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11971077/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143788112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ischiofemoral impingement in joint preserving hip surgery: prevalence and imaging predictors.","authors":"Alexander F Heimann, Moritz Wagner, Peter Vavron, Alexander Brunner, Till D Lerch, Ehrenfried Schmaranzer, Joseph M Schwab, Simon D Steppacher, Moritz Tannast, Reto Sutter, Florian Schmaranzer","doi":"10.1186/s13244-025-01946-2","DOIUrl":"10.1186/s13244-025-01946-2","url":null,"abstract":"<p><strong>Objectives: </strong>To determine the prevalence of ischiofemoral impingement (IFI) in young patients evaluated for joint-preserving hip surgery and investigate its associations with osseous deformities and intra-articular pathologies.</p><p><strong>Methods: </strong>Retrospective study of 256 hips (224 patients, mean age 34 years) that were examined with radiographs and MR arthrography for hip pain. Quadratus femoris muscle edema was used to indicate IFI and measurements of ischiofemoral space were performed. Imaging analysis assessed cam deformity, femoral torsion, neck-shaft angle, ischial angle, acetabular coverage-/ version, and chondro-labral pathology. Prevalence of MRI findings consistent with IFI was calculated and univariate- and multivariate logistic regression identified associations between IFI and hip deformities.</p><p><strong>Results: </strong>Quadratus femoris muscle edema consistent with IFI was present in 9% (23/256 hips) with narrowing of the ischiofemoral distance (1.7 ± 0.6 cm vs 2.8 ± 0.7 cm in the control group, p < 0.001) and a higher prevalence in females (89% vs 45%, p < 0.001). Multiple regression identified female sex (OR 12.5, 95% CI: 1.6-98.2, p = 0.017), high femoral torsion (OR 3.9, 1.4-10.4, p = 0.008), and ischial angle > 127° (OR 5.9, 1.3-27.1, p = 0.023) as independent predictors of IFI. Labral tears were highly prevalent in both IFI and control groups (87% vs 89%, p = 0.732); cartilage lesions were less common in the IFI group (26% vs 52%, p = 0.027).</p><p><strong>Conclusion: </strong>IFI was present in 9% of young patients evaluated for joint-preserving surgery, associated with female sex, high femoral torsion and increased ischial angle. The comparable prevalence of labral lesions but lower prevalence of cartilage damage suggests complex relationships between extra- and intra-articular pathologies.</p><p><strong>Critical relevance statement: </strong>Recognizing IFI and its link to hip deformities and chondrolabral damage is crucial for clinicians, as it represents an important differential diagnosis, directly impacting joint-preserving treatment strategies in young adults with hip pain.</p><p><strong>Key points: </strong>The prevalence and imaging predictors of IFI in young patients remain unknown. IFI occurred in 9%, with predictors including female sex, high femoral torsion, and an increased ischial angle. IFI is an important differential diagnosis in joint-preserving hip surgery.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"78"},"PeriodicalIF":4.1,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11971088/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143788117","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Should all trainees \"do research\"?","authors":"Steve Halligan, Stuart Taylor","doi":"10.1186/s13244-025-01940-8","DOIUrl":"10.1186/s13244-025-01940-8","url":null,"abstract":"","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"79"},"PeriodicalIF":4.1,"publicationDate":"2025-04-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11971067/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143788280","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The clinical implications and interpretability of computational medical imaging (radiomics) in brain tumors.","authors":"Yixin Wang, Zongtao Hu, Hongzhi Wang","doi":"10.1186/s13244-025-01950-6","DOIUrl":"10.1186/s13244-025-01950-6","url":null,"abstract":"<p><p>Radiomics has widespread applications in the field of brain tumor research. However, radiomic analyses often function as a 'black box' due to their use of complex algorithms, which hinders the translation of brain tumor radiomics into clinical applications. In this review, we will elaborate extensively on the application of radiomics in brain tumors. Additionally, we will address the interpretability of handcrafted-feature radiomics and deep learning-based radiomics by integrating biological domain knowledge of brain tumors with interpretability methods. Furthermore, we will discuss the current challenges and prospects concerning the interpretability of brain tumor radiomics. Enhancing the interpretability of radiomics may make it more understandable for physicians, ultimately facilitating its translation into clinical practice. CRITICAL RELEVANCE STATEMENT: The interpretability of brain tumor radiomics empowers neuro-oncologists to make well-informed decisions from radiomic models. KEY POINTS: Radiomics makes a significant impact on the management of brain tumors in several key clinical areas. Transparent models, habitat analysis, and feature attribute explanations can enhance the interpretability of traditional handcrafted-feature radiomics in brain tumors. Various interpretability methods have been applied to explain deep learning-based models; however, there is a lack of biological mechanisms underlying these models.</p>","PeriodicalId":13639,"journal":{"name":"Insights into Imaging","volume":"16 1","pages":"77"},"PeriodicalIF":4.1,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11955438/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143752421","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}