Clear cell renal cell carcinoma (ccRCC) is a heterogeneous disease with features that vary by ethnicity. A systematic characterization of the genomic landscape of Chinese ccRCC is lacking, and features of ccRCC associated with tumor thrombus (ccRCC-TT) remain poorly understood. Here, we applied whole-exome sequencing on 110 normal-tumor pairs and 42 normal-tumor-thrombus triples, and transcriptome sequencing on 61 tumor-normal pairs and 30 primary-thrombus pairs from 152 Chinese patients with ccRCC. Our analysis reveals that a mutational signature associated with aristolochic acid (AA) exposure is widespread in Chinese ccRCC. Tumors from patients with ccRCC-TT show a higher mutational burden and genomic instability; in addition, mutations in BAP1 and SETD2 are highly enriched in patients with ccRCC-TT. Moreover, patients with/without TT show distinct molecular characteristics. We reported the integrative genomic sequencing of Chinese ccRCC and identified the features associated with tumor thrombus, which may facilitate ccRCC diagnosis, prognosis and treatment.
The intraoperative activity of a surgeon has substantial impact on postoperative outcomes. However, for most surgical procedures, the details of intraoperative surgical actions, which can vary widely, are not well understood. Here we report a machine learning system leveraging a vision transformer and supervised contrastive learning for the decoding of elements of intraoperative surgical activity from videos commonly collected during robotic surgeries. The system accurately identified surgical steps, actions performed by the surgeon, the quality of these actions and the relative contribution of individual video frames to the decoding of the actions. Through extensive testing on data from three different hospitals located in two different continents, we show that the system generalizes across videos, surgeons, hospitals and surgical procedures, and that it can provide information on surgical gestures and skills from unannotated videos. Decoding intraoperative activity via accurate machine learning systems could be used to provide surgeons with feedback on their operating skills, and may allow for the identification of optimal surgical behaviour and for the study of relationships between intraoperative factors and postoperative outcomes.
Purpose:Automated performance metrics provide a novel approach to the assessment of surgical performance. Herein, we present a construct validation of automated performance metrics during robotic assisted partial nephrectomy.Materials and Methods:Automated performance metrics (instrument motion tracking/system events) and synchronized surgical videos from da Vinci® Si systems during robotic assisted partial nephrectomy were recorded using a system data recorder. Each case was segmented into 7 steps: colon mobilization, ureteral identification/dissection, hilar dissection, exposure of tumor within Gerota’s fascia, intraoperative ultrasound/tumor scoring, tumor excision, and renorrhaphy. Automated performance metrics from each step were compared between expert (≥150 cases) and trainee (<150 cases) surgeons by Mann-Whitney U test (continuous variables) and Pearson’s chi-squared test (categorical variables). Clinical outcomes were collected prospectively and correlated to automated performance metrics and R.E.N.A.L. (radius, exophytic/endophytic, nearness of tumor to collecting system, anterior/posterior, location relative to polar line) nephrometry score by Spearman’s correlation coefficients (r).Results:A total of 50 robotic assisted partial nephrectomy cases were included for analysis, performed by 7 expert and 10 trainee surgeons. Automated performance metric profiles significantly differed between experts and novices in the initial 5 steps (p <0.05). Specifically, experts exhibited faster dominant instrument movement and greater dominant instrument usage (bimanual dexterity) than trainees in select steps (p ≤0.045). Automated performance metrics during tumor excision and renorrhaphy were significantly correlated with R.E.N.A.L. score (r ≥0.364; p ≤0.041). These included metrics related to instrument efficiency, task duration, and dominant instrument use.Conclusions:Experts are more efficient and directed in their movement during robotic assisted partial nephrectomy. Automated performance metrics during key steps correlate with objective measures of tumor complexity and may serve as predictors of clinical outcomes. These data help establish a standardized metric for surgeon assessment and training during robotic assisted partial nephrectomy.
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