Supplemental Material for Unveiling the Impact: Childhood Sexual Abuse and Dissociation Among Lithuanian Young Adults

MRI is the reference standard modality for evaluation of perianal fistulas, providing high-spatial-resolution mapping of the tract origin, course, and secondary extensions to aid classification and treatment planning. Cryptoglandular fistulas most commonly follow intersphincteric or transsphincteric pathways and may be complicated by formation of abscesses. In comparison, fistulas related to perianal fistulizing Crohn disease (PFCD) often arise near the anorectal junction, frequently extending beyond conventional classifications due to branching, high-origin involvement, and associated proctitis. In patients with PFCD, MRI facilitates serial evaluation, allowing stable tracts to be distinguished from new branching or progressive fibrosis. MRI allows differentiation of fluid-persistent tracts, which carry a higher risk of symptom relapse, from fibrosis-predominant tracts, which reflect a favorable response to therapy and a reduced risk of recurrence. Although most perianal fistulas are benign, chronic tracts can undergo malignant transformation. In PFCD, prolonged inflammation and nonhealing fistulas increase the risk of mucinous adenocarcinoma. The authors present an MRI-based approach to interpretation of fistulas, focusing on tract origin and escape pathways, and using the Parks and St James classification systems to improve radiologic assessment. Core components include defining tract trajectory, identifying internal and external openings, and recognizing secondary extensions such as abscesses or supralevator spreading. Radiologists should also assess for proctitis and monitor changes in fistula composition over time. High-spatial-resolution MRI, combined with mapping of the escape pathway and evaluation of the serial tract, improves characterization of fistulas and supports long-term monitoring in patients with cryptoglandular and PFCD fistulas. © The Author(s) 2026. Published by the Radiological Society of North America under a CC BY 4.0 license. Supplemental material is available for this article.

Whole-body (WB) diffusion-weighted (DW) (WB-DW) MRI and fluorine 18-fluorodeoxyglucose (FDG) PET/CT have increasing roles in the evaluation of treatment response in the setting of multiple myeloma (MM), complementing molecular and clinical criteria. The authors clarify the use of WB-DW MRI and FDG PET/CT in the follow-up of MM, emphasize the advantages and limitations of these examinations, and outline the use of the response criteria for each modality. The Myeloma Response Assessment and Diagnosis System is a consensus guideline that provides recommendations for the acquisition and interpretation of WB-DW MR images of MM for diagnosis and follow-up. It is used to categorize treatment responses into five response assessment categories (RACs): RAC 1 (highly likely to be responding), RAC 2 (likely to be responding), RAC 3 (no change), RAC 4 (likely to be progressing), and RAC 5 (highly likely to be progressing). Conversely, the Italian Myeloma Criteria for PET Use is used to standardize FDG PET/CT image interpretation. The five-point Deauville score (DS) scale is used to grade FDG uptake visually according to metabolic response: Complete response is defined as uptake lower than that in the liver (DS 1-3); partial response, as persistent lesions, with a decreased number of lesions or reduced uptake (DS 4-5 ); stable disease, as no change; and progressive disease, as new lesions or increased uptake. WB-DW MRI and FDG PET/CT provide valuable information for posttreatment MM assessment. FDG PET/CT is the reference standard for detecting extramedullary disease, while WB-DW MRI is more sensitive, particularly for diffuse disease. For comprehensive evaluation, the morphologic changes in MM lesions should be correlated with DW imaging and apparent diffusion coefficient values at MRI and with uptake values at FDG PET/CT. ©RSNA, 2026 Supplemental material is available for this article.

Purpose To compare gallium 68 (68Ga) fibroblast activation protein inhibitor (FAPI) PET/CT with plasma Epstein-Barr virus (EBV)-DNA load measurement, contrast-enhanced MRI/CT, and fluorine 18 (18F) fluorodeoxyglucose (FDG) PET/CT for nasopharyngeal carcinoma (NPC) initial assessment and recurrence surveillance. Materials and Methods Participants with NPC who underwent both 18F-FDG and 68Ga-FAPI PET/CT examinations within 1 week for initial assessment (treatment-naive cohort) and recurrence surveillance (posttreatment cohort) were enrolled in this prospective study conducted between July 2020 and November 2023 (Chinese Clinical Trial Registry identifier no. ChiCTR2100054163). The data of plasma EBV DNA load and contrast-enhanced MRI/CT were analyzed. Diagnostic performance was compared using the McNemar test. The relationships between plasma EBV DNA and the two tracers were assessed by using simple linear regression analysis. Results Sixty-five participants (median age, 50 years [IQR, 41.50-61 years]; 50 male) were included, with 31 in the treatment-naive cohort and 34 in the posttreatment cohort. In participant-level analysis, 68Ga-FAPI PET/CT demonstrated the highest accuracy rate in identifying the participants with de novo (100% [31 of 31] vs 84% [26 of 31], 77% [24 of 31], and 84% [26 of 31], P = .06, .02, and .06, respectively) and recurrent (94% [32 of 34] vs 53% [18 of 34], 68% [23 of 34], and 85% [29 of 34], P = .001, .02, and .25, respectively) NPC, compared with plasma EBV DNA, contrast-enhanced MRI/CT, and 18F-FDG PET/CT. Furthermore, compared with contrast-enhanced MRI/CT and 18F-FDG PET/CT, 68Ga-FAPI PET/CT led to upstaging in five of 31 (16%) and downstaging in four of 31 (13%) treatment-naive participants. Lesion uptake at 68Ga-FAPI PET/CT did not correlate with plasma EBV DNA load (P > .05). Conclusion 68Ga-FAPI PET/CT outperformed plasma EBV DNA level, contrast-enhanced MRI/CT, and 18F-FDG PET/CT in initial assessment and recurrence surveillance of NPC. Keywords: Head/Neck, PET/CT, Nasopharyngeal Carcinoma, Epstein-Barr Virus, Fibroblast Activation Protein Inhibitor, FDG Chinese Clinical Trial Registry identifier no. ChiCTR2100054163 Supplemental material is available for this article. © RSNA, 2026.

Contrast-enhanced US (CEUS) is increasingly used in the characterization of indeterminate focal renal masses. In addition to the safety profile, portability, and relatively low cost of performing a CEUS examination, CEUS may be better tolerated than CT or MRI. US contrast agents (UCAs) are not nephrotoxic, making CEUS the contrast-enhanced examination of choice to safeguard renal function or in patients with kidney failure. CEUS may face technical limitations, including in patients with deep lesions or lesions larger than the field of view, acoustic windows limited by bone or gas, or respiratory motion; technique modification may be needed for a successful examination. As purely intravascular agents, UCAs offer accurate characterization of lesion vascularity. The high temporal resolution of CEUS enables frame-by-frame analysis for detailed assessment of lesion perfusion and can mitigate the effects of patient or respiratory motion. With high contrast and spatial resolution and exquisite sensitivity to even minimal blood flow, CEUS can detect enhancement in hair-thin septa and subcentimeter nodules, making CEUS ideally suited for the characterization and surveillance of cystic renal masses. The high sensitivity of CEUS for intralesional enhancement and its negative predictive value of 100% in excluding flow enable confident discrimination of benign avascular cysts, which require no further follow-up, from solid renal masses, which warrant surveillance or tissue diagnosis. Overall, CEUS performs equally to contrast-enhanced CT or MRI, may be preferred for certain patients or indications, and is appropriate for first-line imaging of indeterminate focal renal masses. ©RSNA, 2026 Supplemental material is available for this article. See the invited commentary by Fetzer in this issue.

Purpose To evaluate the performance of deep learning models integrating multimodal data for predicting microvascular invasion (MVI) in hepatocellular carcinoma and to investigate the impact of different manual annotation methods on performance. Materials and Methods Patients with hepatocellular carcinoma from three institutions were included in this retrospective study; postoperative histopathology served as the reference standard for MVI. Patients from center A were divided into training and internal test sets; patients from centers B and C formed the external test set. Two manual annotations (voxel-level masks, bounding boxes) were performed on MRI scans. Deep learning models were developed using multimodal data. Model performance was evaluated using the receiver operating characteristic, calibration, and decision curve analysis, with area under the receiver operating characteristic curve (AUC) differences tested by the DeLong test. Results A total of 281 patients were included in this study (mean age, 59.05 years ± 11.92 [SD]; 238 male). Single-sequence models achieved internal test AUCs of 0.57-0.76. Multisequence models reached AUCs of 0.86 (95% CI: 0.77, 0.95) with masks and 0.83 (95% CI: 0.73, 0.94) with bounding boxes. Multimodal fusion improved performance (mask: AUC, 0.88 [95% CI: 0.80, 0.96] vs bounding box: AUC, 0.85 [95% CI: 0.75, 0.94]; P = .50), with external test AUCs of 0.77 (95% CI: 0.66, 0.89) and 0.76 (95% CI: 0.64, 0.88), respectively (P = .40). Bounding box reduced time by 53% (mask = 3.24 minutes; bounding box = 1.52 minutes; P < .001). Conclusion Multimodal fusion models improved predictive performance for MVI. Bounding box annotation achieved statistically comparable overall AUC to that of voxel-level masks while improving annotation efficiency. Keywords: Hepatocellular Carcinoma, Microvascular Invasion, MRI, Deep Learning, Annotation Efficiency, Model Visualization Supplemental material is available for this article. © The Author(s) 2026. Published by the Radiological Society of North America under a CC BY 4.0 license.

Cowden syndrome (CS) is a rare autosomal-dominant inherited variant of PTEN (phosphatase and tensin homolog gene) hamartoma tumor syndrome caused by mutations to the PTEN tumor suppressor gene, leading to activation of the PI3K/AKT/mTOR intracellular signaling pathway. Patients with this syndrome present with multisystem hamartomatous lesions and are particularly predisposed to developing malignancies, including breast, thyroid, renal, endometrial, and colorectal malignancies. Mucocutaneous lesions, especially trichilemmomas, and macrocephaly are commonly seen in the 2nd decade of life and usually manifest before the development of malignancies. In children, clinical manifestations such as intellectual disability and gastrointestinal polyps may arise and may prompt early investigation and diagnosis. Major features of CS include Lhermitte-Duclos disease, breast cancer, endometrial cancer, thyroid carcinoma, macrocephaly, and gastrointestinal hamartomas. Other features that are considered part of the minor clinical diagnostic criteria include colorectal cancer, esophageal glycogenic acanthosis, lipomas, renal cell carcinoma, testicular lipomatosis, and vascular anomalies. Comprehensive evaluation including clinical and genetic assessment and imaging is essential for early diagnosis and management. Given the increased risk of developing malignancies, surveillance strategies have been routinely updated and recommended by the National Comprehensive Cancer Network. Important cancer surveillance imaging strategies include mammography, MRI of the breast, and US of the thyroid and kidneys, although other manifestations are routinely assessed with CT and MRI. In this article, the genetics and molecular pathogenesis, clinical manifestations, imaging features, and most up-to-date surveillance recommendations of CS are reviewed. ©RSNA, 2026 Supplemental material is available for this article.

Background Methylmalonic acid (MMA) accumulates due to mitochondrial dysfunction or enzymatic deficiencies. Methylmalonic acidemia causes central nervous system damage. In vivo detection of MMA using conventional proton (1H) MR spectroscopy is hindered by overlap with lactate and lipids at 1.33 ppm. Purpose To evaluate the feasibility of an optimized J-editing 1H MR spectroscopy protocol to selectively detect MMA and lactate signals in both phantoms and individuals with methylmalonic acidemia. Materials and Methods In this prospective pediatric case-control study, individuals with genetically confirmed methylmalonic acidemia and age-matched control participants underwent brain J-editing 1H MR spectroscopy. The primary endpoint was the feasibility of selective in vivo detection of cerebral MMA using J-editing 1H MR spectroscopy. Phantoms were prepared with different MMA to lactate ratios. Correlations between cerebral MMA signals and biochemical markers (blood propionylcarnitine to acetylcarnitine [C3/C2] ratio and urinary MMA levels) were assessed using Spearman correlation in individuals diagnosed with methylmalonic acidemia. The diagnostic performance of MR spectroscopy and urinary MMA measurement was evaluated against genetic confirmation with use of sensitivity, specificity, and receiver operating characteristic curves. Results A total of 42 participants were included: 24 with methylmalonic acidemia (mean age, 8.7 years ± 5.2 [SD]) and 18 control participants (mean age, 8.9 years ± 4.0 [SD]). The J-editing 1H MR spectroscopy protocol effectively separated MMA and lactate signals in the brains of individuals with methylmalonic acidemia, consistent with phantom results. No abnormal MMA peaks were observed in control participants. The intensity of cerebral MMA signals correlated with blood C3/C2 ratio (Spearman ρ = 0.53 [95% CI: 0.15, 0.78]; P = .008) and urinary MMA levels (ρ = 0.66 [95% CI: 0.33, 0.84]; P < .001). Conclusion J-editing 1H MR spectroscopy reliably and noninvasively detected cerebral MMA in vivo, distinguishing it from overlapping lactate signals. © RSNA, 2026 Supplemental material is available for this article.

Purpose To evaluate the generalizability of adult-trained models for hepatoblastoma segmentation to pediatric patients and to develop two deep learning (DL) models, and , specifically trained on pediatric contrast-enhanced CT and T2-weighted MRI scans, respectively. Materials and Methods Imaging data from the multicenter Children's Oncology Group AHEP0731 trial (NCT00980460; May 2008-July 2018) were analyzed. DL models employing the three-dimensional U-Net architecture were trained using DCT-Train and DMRI-Train. These models were evaluated on DCT-Val and DMRI-Val using the Dice similarity coefficient (DSC), and model segmentations were compared with manual segmentations from three annotators (R1, R2, and R3), their consensus (Rc), and adult-trained model ( ) segmentations. Volume percentage error analysis was performed to evaluate segmentation precision. Results A total of 104 participants (mean age ± SD, 28.2 months ± 30.5; 64 male; DCT-Train = 56, DCT-Val = 48) were included in the CT dataset and 123 (31.5 months ± 38.4; 87 male; DMRI-Train = 50, DMRI-Val = 73) in the MRI dataset. achieved good agreement with consensus segmentation (DSC = 0.86 [95% CI: 0.80, 0.91]) and exhibited higher agreement than with R1 (0.83 vs 0.55), R2 (0.85 vs 0.55), R3 (0.84 vs 0.54), and Rc (0.86 vs 0.55) segmentations. Volume percentage error analysis revealed that achieved segmentation results on par with or better than those of a novice annotator (R3) in high-precision scenarios. also achieved a DSC of 0.86, demonstrating good agreement with Rc. Conclusion The pediatric-trained DL-based models outperformed adult-trained models for accurate segmentation of pediatric hepatoblastoma. Keywords: Pediatrics, Deep Learning, Liver, MR-Imaging, Abdomen/GI, Algorithm Development ClinicalTrials.gov NCT00980460 Supplemental material is available for this article. © The Author(s) 2026. Published by the Radiological Society of North America under a CC BY 4.0 license.

Purpose To evaluate the performance of a deep learning algorithm (DLA) for detecting liver metastases (LM) in patients with colorectal cancer (CRC) across diverse clinical contexts and compare its accuracy with that of radiologists. Materials and Methods This retrospective, bicentric study included patients with CRC who underwent contrast-enhanced abdominal CT between January 2019 and December 2021. The DLA accuracy was assessed at the per-nodule and per-patient levels and compared with that of a senior (R1) and an in-training (R2) radiologist blinded to each other's results. The LM detection and false detection rates and interreader agreement were determined. Results Among 181 patients with CRC (mean age, 64 years ± 13 [SD]; 102 male), 95 had LM and 86 had no LM. In the per-nodule analysis, the DLA LM detection rate was 81% (227 of 280; 95% CI: 76.1, 85.2), with no difference compared with R1 (79%; 222 of 280; 95% CI: 74.2, 83.6; P = .49) or R2 (76%; 214 of 280; 95% CI: 71.1, 81.0; P = .19). Detection rates of DLA increased with lesion size: less than 10 mm, 55% (51 of 93; 95% CI: 44.7, 64.6); 10-19 mm, 91% (96 of 106; 95% CI: 83.5, 94.8); and 20 mm or more, 99% (80 of 81; 95% CI: 93.3, 99.8). Detection of subcapsular LM was comparable across readers (DLA, 90% [113 of 125; 95% CI: 84.0, 94.4]; R1, 91% [114 of 125; 95% CI: 84.9, 95.0]; R2, 89% [111 of 125; 95% CI: 82.1, 93.2]). False detection rates were low (DLA, 22% [39 of 181; 95% CI: 16.2, 28.1]; R1, 20% [37 of 181; 95% CI: 15.2, 26.9]; R2, 26% [47 of 181; 95% CI: 20.1, 32.8]; DLA vs R1, P = .004; DLA vs R2, P = .01). DLA false positives were mainly biliary dilatations (n = 14) and diaphragmatic indentations (n = 12). Interreader agreement was moderate (κ = 0.63-0.75). Conclusion DLA performance did not differ from radiologists in detecting LM, with consistent results across lesion sizes and locations. Keywords: Imaging Modality, Abdomen, Gastrointestinal, Liver, Oncology, Comparative Studies, Segmentation, Diagnosis, Deep Learning Supplemental material is available for this article. © RSNA, 2026.

Purpose To develop a self-supervised chest CT foundation model and evaluate its performance in lung cancer clinical tasks. Materials and Methods In this retrospective multicenter study, the authors developed the Unified CT-Based Lung Cancer Imaging Foundation (UCLIF) model using self-supervised learning on 33 901 three-dimensional chest CT scans acquired between June 1958 and February 2019. The model was pretrained with a contrastive masked image modeling task and then fine-tuned for lung cancer histologic subtype classification, cancer staging, survival, and recurrence prediction using multicenter patient datasets. Histopathology, TNM stage, and follow-up outcomes served as reference standards. UCLIF was compared with mainstream deep learning and machine learning algorithms, and model performance was assessed by accuracy, sensitivity, specificity, and area under the receiver operating characteristic curve (AUC); superiority was tested using the DeLong test. Results A total of 656 patients were included for downstream evaluation (mean age, 68.55 years ± 10.01 [SD]; 450 males). Compared with self-supervised pretraining on natural images or single tumor regions, UCLIF achieved superior performance (DeLong test, P < .001) and provided high AUCs for histologic subtype (AUC, 0.96 [95% CI: 0.88, 1.00]; AUC, 0.82 [95% CI: 0.60, 0.98]; and AUC, 0.93 [95% CI: 0.80, 0.99] for adenocarcinoma, large cell lung cancer, and squamous cell carcinoma, respectively), cancer staging (AUC, 0.95 [95% CI: 0.79, 1.00]; AUC, 0.99 [95% CI: 0.96, 1.00]; AUC, 0.92 [95% CI: 0.74, 1.00]; and AUC, 0.91 [95% CI: 0.78, 1.00] for stages I-IV, respectively), survival (AUC, 0.97 [95% CI: 0.92, 1.00]; AUC, 0.90 [95% CI: 0.72, 0.98]; and AUC, 0.90 [95% CI: 0.77, 1.00] for 1-, 3-, and 5-year survival, respectively), and recurrence (AUC, 0.95; 95% CI: 0.88, 0.99). Conclusion The UCLIF model accurately predicted lung cancer histologic subtype, stage, survival, and recurrence. Keywords: Lung Cancer, CT, Foundation Model, Diagnosis, Classification Supplemental material is available for this article. © RSNA 2026.

Fibromuscular dysplasia (FMD) is a rare idiopathic, noninflammatory, nonatherosclerotic arteriopathy that leads to wall abnormalities in medium- and small-caliber arteries, typically resulting in alternating areas of stenosis and dilatation, or "beading" at angiography. Stenosis, dissection, and aneurysm can also be seen, and clinical manifestations depend on the vascular territory affected by FMD, with the renal and carotid arteries being most frequently involved. The diagnosis of FMD is made based on imaging features of FMD classified according to the angiographic appearance as focal FMD or multifocal FMD. Early diagnosis is often challenging, as mimics of FMD must first be excluded. The presence of aneurysm, dissection, or tortuosity alone is inadequate to establish the diagnosis, as these are variant manifestations; however, if a patient has focal or multifocal manifestations such as stenosis or stenosis and dilatation in one vascular bed, establishing the diagnosis of FMD, the presence of aneurysm, dissection, or tortuosity in additional vascular beds is considered multivessel involvement of all affected vascular beds. The cerebrovascular system, coronary arteries, renal and mesenteric arteries, and extremities should be evaluated for FMD involvement. The most serious potential complications of FMD are hypoperfusion secondary to aneurysm, dissection, or arterial occlusion, which can lead to hypertension, stroke, or myocardial infarction, as well as subarachnoid hemorrhage. Prompt intervention, blood pressure management, and revascularization are necessary to avoid devastating complications. Radiologists play an important role in timely identification of diverse FMD-associated morbidities and thus may contribute to early diagnosis and treatment of FMD. ©RSNA, 2026 Supplemental material is available for this article.

Dual-energy CT (DECT) has increased the diagnostic capabilities of CT in abdominal imaging, overcoming many of the limitations of conventional CT by leveraging information about radiation beam energy and material composition to generate multiple datasets. Exploiting DECT-derived energy- and material-specific datasets has improved CT image quality and expanded its clinical applications in abdominal imaging. DECT makes it possible to noninvasively detect, characterize, and quantify clinically relevant materials, improving tissue characterization and providing multiple quantitative parameters that expand the role of CT in abdominal imaging and support development of imaging biomarkers. Nevertheless, implementing these advanced CT applications and quantitative parameters in clinical practice remains challenging. The authors provide an overview of various applications of quantitative clinical DECT parameters in abdominal imaging. First, they describe the basic principles underlying DECT, reviewing the fundamentals and challenges of DECT material characterization and derived quantitative parameters. They then examine current applications of DECT-based quantitative imaging in abdominal disease, discussing their strengths and limitations. Finally, they explore potential applications of DECT in abdominal imaging, including artificial intelligence and DECT-derived radiomics and imaging biomarkers. ©RSNA, 2026 Supplemental material is available for this article.

Purpose To develop a deep learning-based, computer-aided diagnosis (CADx) model for preoperative classification of ovarian tumors (OTs) on CT scans and to compare its performance with current US models and radiologist assessments. Materials and Methods This retrospective multicenter study (January 2021-November 2023) included patients with indeterminate OTs. The dataset comprised training, internal (n = 360), and external test (n = 27) sets. Final histopathology served as the reference standard. The CADx model was trained using self-supervised learning on public and institutional CT datasets. Performance of the CADx model was compared with that of two current US-based models (Risk of Malignancy [RMI] and Assessment of Different NEoplasias in the adneXa [ADNEX] models) and with radiologist reports. Metrics included the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values, with comparisons assessed using 95% CI overlap. Results The dataset contained 387 OT images from 344 patients (226 benign and 118 malignant OTs). The model achieved a median AUC of 0.84 (95% CI: 0.65, 0.92) on the internal test set and 0.61 (95% CI: 0.59, 0.65) on the external test set. The CADx model performed comparably with the two US models and radiologists. On the internal test set, AUCs for RMI, ADNEX, and radiologists were 0.77 (95% CI: 0.72, 0.83), 0.68 (95% CI: 0.51, 0.84), and 0.76 (95% CI: 0.69, 0.83), respectively. On the external test set, corresponding AUCs were 0.66 (95% CI: 0.44, 0.88), 0.86 (95% CI: 0.60, >0.99), and 0.67 (95% CI: 0.26, >0.99), respectively. The CADx model yielded the highest sensitivity (94.7%). Conclusion Despite disease and data variability, this CT-based deep learning model for preoperative OT classification achieved comparable performances to US models and radiologists on internal and external test sets, but further refinement is needed before clinical implementation. Keywords: Ovarian Tumor Classification, Ovarian Cancer, Computer-aided Diagnostics, Multicenter Trial Clinical trial registration no. NTC05174377 Supplemental material is available for this article. © RSNA, 2026.

Purpose To compare published CT-based systems for small solid renal mass (SoRM) assessment, propose modifications that may increase specificity and interreader agreement, and validate the revised system. Materials and Methods Our retrospective study included patients with histologically confirmed SoRMs measuring ≤4 cm who underwent CT imaging (single-institution internal dataset, n = 194; external dataset from The Cancer Imaging Archive, n = 55). Two blinded radiologists (readers 1 [R1] and 2 [R2]) compared four CT systems (CT score, modified CT score, abbreviated CT score, and UCLA CT score) for diagnostic accuracy in clear cell renal cell carcinoma (ccRCC) and papillary RCC (pRCC) and for interreader agreement (Gwet agreement coefficient [AC1]). We also evaluated the addition of two decision rules to the best-performing algorithm (noncontrast CT [NCCT] attenuation ≤ 20 HU and corticomedullary phase-NCCT attenuation at two thresholds, ≤20 HU and ≤30 HU) to create a modified algorithm (CT-Score version 2.0). Results The abbreviated CT score had the best combination of accuracy for ccRCC (R1: 85% [95% CI: 79, 89], R2: 72% [95% CI: 65, 78]) and pRCC (R1: 86% [95% CI: 80, 91], R2: 86% [95% CI: 80, 91]) and interreader agreement (Gwet AC1 = 0.53). CT-Score version 2.0 (derived by adding decision rules to the abbreviated CT score) demonstrated substantial agreement (Gwet AC1 = 0.63). Specificity of CT-Score version 2.0 was higher for ccRCC (R1: 99% [95% CI: 94, 100], R2: 99% [95% CI: 94, 100] vs R1: 92% [95% CI: 84, 96], R2: 81% [95% CI: 72, 89]; P = .02, P < .001) and pRCC (R1: 100% [95% CI: 98, 100], R2: 100% [95% CI: 98, 100] vs R1: 93% [95% CI: 87, 96], R2: 93% [95% CI: 87, 96]; P = .003, P = .003) when compared with the abbreviated CT score. Validation in the external dataset showed similar results: Gwet AC1 = 0.53; specificity for ccRCC (R1: 100% [95% CI: 83, 100], R2: 100% [95% CI: 83, 100]); and specificity for pRCC (R1: 100% [95% CI: 82, 100], R2: 100% [95% CI: 92, 100]). Conclusion Application of CT-Score version 2.0 resulted in modest improvements in interreader agreement and high specificity for ccRCC and pRCC diagnosis. Keywords: CT, Kidney, Urinary, Oncology, Renal Mass, Algorithm, Clear Cell RCC, Papillary RCC Supplemental material is available for this article. © RSNA, 2026.

Endobronchial lesions in children are uncommon and have various causes and symptoms can be vague and nonspecific, thus making early and accurate diagnosis challenging. Infants may only present with segmental hyperinflation, persistent lobar consolidation, or respiratory distress. Children with endobronchial obstruction usually present with wheezing, persistent cough, recurrent fever, and hemoptysis. Obstructive symptoms are seen when the mass occludes more than one-half of the endobronchial lumen. Unilateral lobar consolidation on chest radiographs is frequently diagnosed as pneumonia, although it could be secondary to a clinically occult endobronchial lesion. Foreign body aspiration may occur at any age but should be excluded if a child younger than 4 years presents with choking, a cough, or dyspnea. The radiolucent nature of many foreign bodies, including organic material and plastics, increases the complexity of diagnosis. Endobronchial tumors become more common in older children but are most often benign. Although they are less common than they are in adults, primary malignant salivary gland tumors and carcinoid tumors do occur in children, although most are benign or low grade with a good prognosis. Endobronchial abnormalities should be considered in the differential diagnosis in children with respiratory symptoms refractory to standard medical treatment. Bronchoscopy plays a vital role in assessing endobronchial lesions with direct visualization and biopsy. Tissue diagnosis assists in determining the optimal treatment plan. Benign and malignant endobronchial pathologic processes in children are reviewed and the multidisciplinary approach to these often challenging diagnoses is discussed. ©RSNA, 2026 Supplemental material is available for this article.

Fertility preservation is a critical consideration for young women undergoing cancer treatment, particularly in cases of gynecologic malignancies. Various options are available and include fertility-sparing treatment (FST) procedures and fertility-preserving treatment (FPT) options such as oocyte or embryo cryopreservation, ovarian tissue cryopreservation, and surgical transposition of the ovaries or uterus. In cases of endometrial cancer, FST entails hysteroscopic resection or dilation and curettage, followed by high-dose progestin therapy for early-stage low-risk tumors. To be suitable for FST, at MRI the tumor must be confined to the endometrium, without myometrial or cervical stromal invasion and without evidence of metastasis. For cervical cancer, FST such as conization or trachelectomy, in combination with lymph node assessment, may be considered if the tumor is confined to the cervix and is 2 cm or smaller in diameter. MRI is pivotal for assessing the tumor extent, largest tumor size, depth of cervical stromal invasion, and proximity of the tumor to the internal os. For ovarian cancer, FST is feasible in select cases, including those of borderline tumors, stage IA low-grade epithelial cancers, and germ cell or sex cord stromal tumors. It often is associated with high survival rates that exceed 90% in cases of early-stage disease, although the risk of recurrence varies based on histologic factors. MRI is essential for characterizing and evaluating the extent of these tumors. A comprehensive approach with imaging, histopathologic analysis, and patient counseling combined is essential for developing personalized FST and suitable FPT protocols. This strategy ensures a balance between oncologic safety and effectiveness while respecting the patient's autonomy in making decisions about future reproductive choices. ©RSNA, 2026 Supplemental material is available for this article.

Variant vascular pathways often present a challenge to a radiologist interpreting routine musculoskeletal imaging studies. In this article, the authors focus on the prevalence, anatomic landmarks for both normal and variant anatomy, clinical significance of the aberrant vasculature and anatomic associations, and potential risks of surgical complications. In the upper extremity, the persistent median artery and high division of the radial artery, known as the brachioradial artery, threaten median neuropathy or upper extremity ischemia. Pelvic trauma cases should be scrutinized for corona mortis, an anastomotic vessel draping around the superior pubic ramus, that threatens severe, even fatal, hemorrhage if lacerated from superior pubic rami fractures or during acetabular fixation. In the posterior thigh, the persistent sciatic artery can be the supplemental or dominant vascular supply to the leg, which when thrombosed risks limb-threatening arterial insufficiency. The relatively fixed course of the aberrant anterior tibial artery between the popliteus muscle and posterior tibial cortex places it at risk in several orthopedic procedures, predominately those involving anterior to posterior instrumentation, and can have severe clinical repercussions including compartment syndrome, limb ischemia necessitating amputation, and hemorrhage. The peroneal arteria magna, referring to a dominant peroneal artery providing the sole vascular supply to the foot, is at risk for traumatic or iatrogenic aneurysm or pseudoaneurysm formation or ischemia if lacerated. Both common and uncommon variants are important to recognize for potential immediate or remote surgical intervention and treatment. ©RSNA, 2026 Supplemental material is available for this article.

This study evaluates the effect of generative artificial intelligence (AI) on software developer productivity via randomized controlled trials at Microsoft, Accenture, and an anonymous Fortune 100 company. These field experiments, run by the companies as part of their ordinary course of business, provided a random subset of developers with access to an AI-based coding assistant suggesting intelligent code completions. Although each experiment is noisy and results vary across experiments, when data are combined across three experiments and 4,867 developers, our analysis reveals a 26.08% increase (standard error: 10.3%) in completed tasks among developers using the AI tool. Notably, less experienced developers had higher adoption rates and greater productivity gains. This paper was accepted by Jean-Pierre Dube, marketing. Funding: M. Demirer and T. Salz thank the MIT GenAI Initiative for funding. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2025.00535 .

A growing number of organizations hire employees with visible physical disabilities into jobs where they can perform as effectively as their nondisabled counterparts. Many practitioners expect such inclusion to positively impact nondisabled coworkers. Yet, the consequences for nondisabled coworkers’ productivity remain unclear. We focus on an underexplored mechanism: frontline managers’ discretionary allocation of supervisory support. Drawing on research on benevolent ableism and finite managerial capacity, we theorize that adding an employee with a visible physical disability—even one fully capable of performing the task—can shift supervisory support toward the disabled employee, reducing the support available to nondisabled coworkers. We test this theory using a natural experiment in a teleservice company that randomly assigned recruits, including individuals with visible physical disabilities, to project teams. We analyze 40,334 employee-month productivity bonus records, a standardized, nondiscretionary measure consolidating multiple performance indicators and net of supervisor evaluations. Supplemented with interviews with 15 frontline managers and 28 subordinate agents, our analyses show that managers allocated more supervisory support to disabled employees and that this reallocation dampened nondisabled coworkers’ productivity. Our study advances understanding of how disability inclusion shapes supervisory support patterns and its implications for coworker performance. Funding: X. Zhou gratefully acknowledges financial support from the National Natural Science Foundation of China [Grant 72102145] and Shanghai Oriental Talents Program. Supplemental Material: The online appendix is available at https://doi.org/10.1287/orsc.2024.19036 .