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Background: There is a scarcity of evidence regarding the potential relationship between the size and location of necrotic lesions, which must be understood to provide optimal joint-preserving treatment. The purpose of this study was to characterize the distribution patterns of necrotic lesions of varying sizes in early-stage osteonecrosis of femoral head (ONFH) with the use of three-dimensional mapping. Methods: We retrospectively evaluated clinical CT images of the hips that were performed in the Third Hospital of Hebei Medical University from January 2018 to December 2022 and collected all CT images diagnosed with stage I and II ONFH. Three-dimensional structures that included both necrotic lesions and normal areas of the femoral heads were reconstructed and divided into eight regions to record their size and location. CT images for all lesions were superimposed onto a standard template, and three-dimensional mapping was created to determine the presence of concentrated areas of lesions. Results: In a cohort of 143 patients with stage I and II ONFH, a total of 150 hips were reviewed. For lesions with less than 15% of the femoral head volume, necrotic lesions predominantly involve regions I, III, and V, with region I showing concentration. For lesions with volumes ranging from 15 to 30%, necrotic lesions exhibited a wider distribution across regions I, II, III, IV, V, and VII, with significant concentrations in regions I, III, and V. For lesions exceeding 30% of the femoral head volume, the necrotic lesions were extensively distributed across nearly the entire femoral head, with a notable expansion of the concentrated necrotic areas. Conclusions: The distribution of necrotic lesions varies with lesion size, with smaller lesions primarily concentrated in the anterior and medial regions of the femoral head, particularly in the anterosuperior region, while larger lesions expand to the lateral and inferior regions. These findings enhance existing classification systems and provide crucial insights for guiding hip-preserving surgical planning and approaches. [ABSTRACT FROM AUTHOR]

Objectives: To develop an interactive, non-invasive artificial intelligence (AI) system for malignancy risk prediction in cystic renal lesions (CRLs). Methods: In this retrospective, multicenter diagnostic study, we evaluated 715 patients. An interactive geodesic-based 3D segmentation model was created for CRLs segmentation. A CRLs classification model was developed using spatial encoder temporal decoder (SETD) architecture. The classification model combines a 3D-ResNet50 network for extracting spatial features and a gated recurrent unit (GRU) network for decoding temporal features from multi-phase CT images. We assessed the segmentation model using sensitivity (SEN), specificity (SPE), intersection over union (IOU), and dice similarity (Dice) metrics. The classification model's performance was evaluated using the area under the receiver operator characteristic curve (AUC), accuracy score (ACC), and decision curve analysis (DCA). Results: From 2012 to 2023, we included 477 CRLs (median age, 57 [IQR: 48–65]; 173 men) in the training cohort, 226 CRLs (median age, 60 [IQR: 52–69]; 77 men) in the validation cohort, and 239 CRLs (median age, 59 [IQR: 53–69]; 95 men) in the testing cohort (external validation cohort 1, cohort 2, and cohort 3). The segmentation model and SETD classifier exhibited excellent performance in both validation (AUC = 0.973, ACC = 0.916, Dice = 0.847, IOU = 0.743, SEN = 0.840, SPE = 1.000) and testing datasets (AUC = 0.998, ACC = 0.988, Dice = 0.861, IOU = 0.762, SEN = 0.876, SPE = 1.000). Conclusion: The AI system demonstrated excellent benign-malignant discriminatory ability across both validation and testing datasets and illustrated improved clinical decision-making utility. Critical relevance statement: In this era when incidental CRLs are prevalent, this interactive, non-invasive AI system will facilitate accurate diagnosis of CRLs, reducing excessive follow-up and overtreatment. Key Points: The rising prevalence of CRLs necessitates better malignancy prediction strategies. The AI system demonstrated excellent diagnostic performance in identifying malignant CRL. The AI system illustrated improved clinical decision-making utility. [ABSTRACT FROM AUTHOR]