RESUMO
Radiology-structured reports (SR) have many advantages over free text (FT), but the wide implementation of SR is still lagging. A powerful tool such as GPT-4 can address this issue. We aim to employ a web-based reporting tool powered by GPT-4 capable of converting FT to SR and then evaluate its impact on reporting time and report quality. Thirty abdominopelvic CT scans were reported by two radiologists across two sessions (15 scans each): a control session using traditional reporting methods and an AI-assisted session employing a GPT-4-powered web application to structure free text into structured reports. For each radiologist, the output included 15 control finalized reports, 15 AI-assisted pre-edits, and 15 post-edit finalized reports. Reporting turnaround times were assessed, including total reporting time (TRT) and case reporting time (TATc). Quality assessments were conducted by two blinded radiologists. TRT and TATc have decreased with the use of the AI-assisted reporting tool, although statistically not significant (p-value > 0.05). Mean TATc for RAD-1 decreased from 00:20:08 to 00:16:30 (hours:minutes:seconds) and TRT decreased from 05:02:00 to 04:08:00. Mean TATc for RAD-2 decreased from 00:12:04 to 00:10:04 and TRT decreased from 03:01:00 to 02:31:00. Quality scores of the finalized reports with and without AI-assistance were comparable with no significant differences. Adjusting the AI-assisted TATc by removing the editing time showed statistically significant results compared to the control for both radiologists (p-value < 0.05). The AI-assisted reporting tool can generate SR while reducing TRT and TATc without sacrificing report quality. Editing time is a potential area for further improvement.
RESUMO
Background Obesity is a known risk factor of colorectal cancer (CRC); however, the relationship between obesity and clinicopathologic characteristics and prognosis of CRC remains unclear. This study aimed to investigate the relationship between body mass index (BMI) and clinicopathological and prognostic factors of CRC in Saudi Arabia. Method This was a retrospective cross-sectional study of patients with CRC diagnosed between 2014 and 2018 at King Abdulaziz University Hospital in Jeddah, Saudi Arabia. BMI was calculated by dividing the patient's weight in kilograms by height in meter squared and was classified according to the World Health Organization criteria. Statistical tests, including analysis of variance and chi-square tests, were used to investigate the relationship of each BMI category with clinicopathologic (histological type, degree of differentiation, tumor location, and medical comorbidities) and prognostic variables (TNM stage, lymph nodes involvement, and lymph nodes yield). Results Of 233 patients who were included, 60.1% were male and 39.9% were female patients, with a mean age (standard deviation) of 58.8 ± 13.7 (range: 26-99) years. The median BMI was 26.5 kg/m2. Overall, 3%, 34.3%, 33.0%, and 29.6% patients were classified as underweight, normal weight, overweight, and obese, respectively. Furthermore, 57.1% (4/7), 39.2% (31/80), 38.7% (29/77), and 25.8% (17/69) of underweight, normal, overweight, and obese patients had Stage IV disease (p = 0.20). Of 16 patients with transverse colon cancer, 8 (50%) were obese (p = 0.38), and 1 (6%), 5 (31%), and 2 (13%) were underweight, normal weight, and overweight, respectively. Conclusion Underweight patients are more likely to present with metastatic CRC, while obese patients are more likely to present at earlier stages, although the difference was not statistically significant. BMI is not related to lymph node yield, histological type, or the degree of differentiation.