Citations

Objective: Reverse hybrid coronary revascularization (RHCR) is the integration of percutaneous coronary intervention (PCI) followed by sternal-sparing coronary artery bypass grafting in patients with multivessel coronary artery disease (CAD). We sought to review our RHCR experience over a 10-year period using PCI first followed by robotic totally endoscopic coronary artery bypass (TECAB). Methods: We reviewed the indications and outcomes in patients who underwent RHCR (PCI with drug-eluting stents first, followed by TECAB). Results: From July 2013 to August 2024, 882 robotic TECAB procedures were performed at our institution. Of these, 60 patients underwent RHCR. The mean age of the patients was 66.7 ± 10 years, and 74% were male patients. The target vessel stented was the right coronary artery in 52 patients (87%), circumflex coronary artery in 10 patients (17%), and diagonal in 4 patients (7%). The average time from PCI to TECAB was 3.8 ± 1.64 months. A total of 35 patients (58%) underwent multivessel grafting, with 74% bilateral internal thoracic artery (ITA) use. The mean operative time was 253 ± 88 min, and the mean hospital length of stay was 2 ± 0.76 days. There were no conversions, perioperative strokes, or myocardial infarctions. At mean follow-up of 34 ± 27 months, cardiac-related mortality occurred in 1 patient. Freedom from major adverse cardiac or cerebrovascular events including repeat revascularization was 93%. Conclusions: RHCR is safe and feasible in selected patients with multivessel CAD. In experienced hands, stenting first followed by robotic TECAB with left ITA or bilateral ITA grafts resulted in excellent early and midterm outcomes. Further studies are warranted.

Listing and transplanting patients with acute liver failure (ALF) is challenging, requiring rapid assessment of the likelihood of recovery. The availability of a large number of ALF liver explants, along with their clinical data, afforded an opportunity to retrospectively evaluate liver transplantation (LT) decision-making. We hypothesized that, with the benefit of hindsight, a small number of patients might have recovered without LT. Three hundred liver explants from ALF patients of varying etiologies who had undergone LT over 22 years in the Acute Liver Failure Study Group registry were reviewed for histopathological features and degree of necrosis by a committee of 8 hepatopathologists. A second committee of 8 experienced transplant hepatologists independently reviewed clinical data on these same 300 patients and retrospectively scored the likelihood that each patient required transplantation. Only 3 (1%) of those reviewed by the Clinical Committee were considered to “Definitely” not require liver grafting, while an additional 10 (3.3%) were deemed to “Possibly” not require transplantation (Not Likely Group, N=13). There was no difference in the degree of necrosis in explants in these groups, when compared to the remaining Likely Group, N=287 (95.6%), except in acetaminophen (APAP)-associated ALF, where the degree of necrosis was more uniformly graded as severe, correlating with the need for transplantation. Virtually every patient undergoing LT for ALF retrospectively appeared to need their transplant, based on expert clinical consensus review. Overall, there was poor correlation between clinical severity and apparent degree of necrosis seen in the 300 liver explants, except perhaps for APAP subjects.

Objective To develop and validate classification criteria for pediatric chronic nonbacterial osteomyelitis (CNO) jointly supported by EULAR and the American College of Rheumatology (ACR). Methods This international initiative had 4 phases: (1) candidate items were proposed in a survey of pediatric rheumatologists, (2) criteria definition and reduction by Delphi and nominal group technique exercises, (3) criteria weighting using multicriteria decision analysis, and (4) refinement of weights and threshold score in a development cohort of 441 patients and validation in another cohort of 514 patients. Results The new EULAR/ACR classification criteria for CNO require typical radiographic or magnetic resonance imaging findings and bone pain as an obligatory entry criterion and exclusion criteria of malignancy, infection, vitamin C deficiency, and hypophosphatasia, followed by additive weighted criteria in 5 clinical (site of bone lesions, pattern of bone lesions, age at onset, coexisting conditions, fever) and 4 pathology/laboratory domains (bone biopsy findings if done, anemia, C‐reactive protein level, and erythrocyte sedimentation rate). A total score ≥55 is required for classification as CNO. The new criteria had a sensitivity of 82% and specificity of 98% in the validation cohort. Conclusion These new classification criteria for pediatric CNO developed with international input reflect current views about CNO, have high specificity and good sensitivity, and provide a key foundation for future CNO research.

Objectives: Identifying patients’ advantageous radiotherapy modalities prior to CT simulation is challenging. This study aimed to develop a workflow using deep learning (DL)-predicted synthetic CT (sCT) for treatment modality comparison based solely on a diagnostic CT (dCT). Methods: A DL network, U-Net, was trained utilizing 46 thoracic cases from a public database to generate sCT images predicting planning CT (pCT) scans based on the latest dCT, and tested on 15 institutional patients. The sCT accuracy was evaluated against the corresponding pCT and a commercial algorithm deformed CT (MdCT) based on Mean Absolute Error (MAE) and Universal Quality Index (UQI). To determine advantageous treatment modality, clinical dose-volume histogram (DVH) metrics and Normal Tissue Complication Probability (NTCP) differences between proton and photon treatment plans were analyzed on the sCTs via concordance correlation coefficient (CCC). Results: The AI-generated sCTs closely resembled those of the commercial deformation algorithm in the tested cases. The differences in MAE and UQI values between the sCT-vs-pCT and MdCT-vs-pCT were 19.38 HU and 0.06, respectively. The mean absolute NTCP deviation between sCT and pCT was 1.54%, 0.21%, and 2.36% for esophagus perforation, lung pneumonitis, and heart pericarditis, respectively. The CCC between sCT and pCT was 0.90 for DVH metrics and 0.97 for NTCP, indicating moderate agreement for DVH metrics and substantial agreement. Conclusions: Radiation oncologists can potentially utilize this personalized sCT based approach as a clinical support tool to rapidly compare the treatment modality benefit during patient consultation and facilitate in-depth discussion on potential toxicities at a patient-specific level.

Abstract Background Central sleep apnea (CSA) is characterized by recurrent central apneas during sleep and obstructive sleep apnea (OSA) is characterized by recurrent apneas during sleep due to upper airway obstruction. While OSA may be treated surgically, soft tissue surgery is not considered for treatment of CSA. Case Presentation We present the case of a patient with severe CSA with an Apnea/Hypopnea Index (AHI) of 122 and a central apnea index (CAI) of 74 who experienced complete resolution of CSA following tonsillectomy and expansion pharyngoplasty. Conclusions Although there is significant overlap between OSA and CSA, this is the first report of resolution of CSA following soft tissue surgical intervention in an adult. This suggests an unusual underlying pathophysiology, possibly post-arousal in nature.

Abstract Introduction: We previously developed a urine liquid biopsy assay to measure urine tumor DNA (utDNA) levels in bladder cancer (BC) patients. Here, we extended this approach significantly by layering in cell-free DNA fragmentomic and cell-free RNA analysis, and extending the method to the full spectrum of genitourinary (GU) cancers, to enable both GU cancer detection and tumor tissue of origin prediction. Methods: A total of 155 GU cancer patients and 34 healthy adults were enrolled in this prospective study. Preoperative urine was collected from 94 bladder cancer (BC) patients, including 65 with muscle-invasive disease, all undergoing cystectomy. Additionally, urine samples were collected from 18 renal cell carcinoma (RCC) patients undergoing nephrectomy and 42 metastatic prostate cancer (mPC) patients. Urine cfDNA and cfRNA were isolated and sequenced, with cfRNA sequenced to 100 million reads per sample on an Illumina NovaSeq X and cfDNA sequenced at 5x genome-wide coverage on a Novoseq S4 flow cell. To identify malignant tissue of origin, we analyzed somatic copy number alterations (CNAs), genome-wide fragment length ratios, and 4-mer end motifs. Tumor fraction (TFx) was estimated using ichorCNA, while DELFI was employed for fragment length ratios in 5 megabase bins across the genome and chromosome arm-level z-scores. Individual logistic regression models were developed based on each fragmentomic feature, and validated using a training and held-out testing approach. Outputs from these models were combined into a single comprehensive model, also trained and held-out tested using the same approach. Urine cfRNA transcript isoform abundance was quantified using Salmon and normalized to Transcripts Per Million (TPM) to account for sequencing depth variations. Results: Bladder cancer (BC) showed the highest mean tumor fraction (TFx, 10.5%), followed by RCC (3.8%) and mPC (3.4%). Among 94 BC patients, 42% achieved a pCR, while 58% had residual disease and did not. Patients without pCR had significantly higher urine TFx compared to those with pCR (14.7% vs. 4.7%, p=0.0008). A machine learning model based on genome-wide fragment length ratios achieved an ROC AUC of 0.74, while models using genome-wide CNAs and end motifs achieved AUCs of 0.81 and 0.88, respectively. Combining all features into a single model yielded an AUC of 0.92, with classification accuracies of 89% for bladder cancer, 80% for RCC, and 60% for prostate cancer. Additionally, urine cfRNA analysis using the top 1, 000 coding transcripts achieved an AUC of 0.95, with classification accuracies of 100% for mPC, 87% for BC, and 78% for RCC. Conclusion: Fragmentomic features from urine cfDNA and transcriptomic features from urine cfRNA demonstrate strong potential for predicting tumor tissue-of-origin in GU cancers, underscoring the promise of non-invasive diagnosis across the spectrum of GU malignancies through urine liquid biopsy. Citation Format: Pradeep Singh Chauhan, Irfan Alahi, Jessica Linford, Arpit Panda, Nathan Colon, Ryan Mueller, Faridi Qaium, Peter K. Harris, John Sheng, Eric H. Kim, Melissa A. Reimers, Zachary L. Smith, Woodson W. Smelser, Aadel A. Chaudhuri. Predicting genitourinary cancer tissue-of-origin using urine cell-free DNA fragmentomics and cell-free RNA [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3258.