Artificial intelligence to investigate predictors and prognostic impact of time to surgery in colon cancer

Abstract

Background and Objectives The role of time to surgery (TTS) for long‐term outcomes in colon cancer (CC) remains ill‐defined. We sought to utilize artificial intelligence (AI) to characterize the drivers of TTS and its prognostic impact. Methods The National Cancer Database was utilized to identify patients diagnosed with non‐metastatic CC between 2004 and 2018. AI models were employed to rank the importance of several sociodemographic, facility, and tumor characteristics in determining TTS, and postoperative survival. Results Among 518 983 patients, 137 902 (26.6%) received intraoperative diagnosis of CC (TTS = 0), while 381 081 (74.4%) underwent elective surgery (TTS > 0) with median TTS of 19.0 days (interquartile range [IQR]: 7.0−33.0). An AI model, identified tumor stage, receipt of adequate lymphadenectomy, histologic grade, lymphovascular invasion, and insurance status as the most important variables associated with TTS = 0. Conversely, the type and location of treating facility and receipt of adjuvant therapy were among the most important variables for TTS > 0. Notably, TTS was among the most important variables associated with survival, and TTS > 3 weeks was associated with an incremental increase in mortality risk. Conclusions The identification of factors associated with TTS can help stratify patients most likely to suffer poor outcomes due to prolonged TTS, as well as guide quality improvement initiatives related to timely surgical care.

A novel high accuracy model for automatic surgical workflow recognition using artificial intelligence in laparoscopic totally extraperitoneal inguinal hernia repair (TEP)

“…Artificial intelligence (AI) is increasingly being adopted in several medical specialties. It has proven to be an effective supporting tool in various fields and at different levels of healthcare management, from diagnosis to treatment and follow up [ 1 , 2 ]. Since its first introduction, surgery mediated by a video interface has evolved from a hardly conceivable concept to an efficient tool that can be harnessed to achieve various goals [ 3 ].…”

Avaliação do Escore Plaqueta-Albumina (PAL) como preditor de risco e sobrevida pós-operatória em pacientes com carcinoma hepatocelular submetidos a ressecção hepática

“…Tendo em vista a gravidade da PHLF no pós-operatório de hepatectomias por CHC, muitos autores buscaram criar ferramentas para predizer com maior assertividade seu risco. A correlação entre ALBI e PHLF, já comentada anteriormente, foi corroborada por vários autores (29,90,95,96,112) .…”

“…Reportaram, ainda, melhor desempenho em predizer PHLF do que o escore ALBI. Alaimo et al (112) utilizaram o CCI, o "tumor burden score" e o ALBI para desenvolver um novo escore que teve melhor desempenho do que a classificação de Child-Pugh ou o método de BCLC para predizer PHLF.…”