Objective:To investigate the differences in clinical characteristics and prognosis between early- and late-onset rectal cancer (EORC and LORC, respectively), and to analyze the adverse factors affecting outcomes in EORC patients.Methods:This retrospective cohort and propensity score matching (PSM) study examined 904 rectal cancer patients who underwent radical resection at Peking University Shougang Hospital between 2017 and 2022. Prior to comparison, patients in the EORC group (<50 years old) and LORC group (≥50 years old) were matched at a 1:2 ratio to control for the following confounders: sex; neoadjuvant therapy; T, N, and M stage; and adjuvant treatment. Cox regression was used to identify independent risk factors for poor overall and progression-free survival (OS and PFS, respectively). Restricted cubic splines were used to analyze the association between age and clinical outcome.Results:A total of 199 EORC and 705 LORC patients were included for analysis. Prior to PSM, the proportions of patients with stage T4 [27.6%(55/199) vs.12.9%(91/705),χ 2=30.12, P<0.001] and M1 disease [24.6%(49/199) vs. 15.7% (111/705),χ 2=8.40, P=0.004], and the proportions of patients who received neoadjuvant [79.9% (159/199) vs. 62.3%(439/705), χ 2=21.54, P<0.001] and adjuvant therapy [62.8%(125/199) vs. 50.8% (358/705), χ 2=9.03, P=0.003] were significantly higher in the EORC group. Mean OS (57.8 vs. 51.9 months; P=0.011) and PFS (53.6 vs. 44.5 months; P=0.001) were also significantly longer in the LORC group. However, after PSM, the intergroup differences in OS and PFS were not significant ( P=0.450 and 0.180, respectively). Multivariate Cox regression in the EORC cohort identified carcinoembryonic antigen concentration ≥5 μg/L [hazard ratio (HR), 3.79; 95% confidence interval (CI), 1.34-10.69; P=0.012] and presence of perineural invasion (HR, 7.27; 95%CI, 1.77-29.88; P=0.006) as independent risk factors for overall mortality; the only independent risk factor for cancer progression was carcinoembryonic antigen concentration ≥5 μg/L (HR, 2.56; 95%CI, 1.06-6.17; P=0.037). Restricted cubic spline analysis showed a U-shaped relationship between age and clinical outcome. After PSM, OS and PFS did not show a significant association with age in the < 60 years old group. Conclusion:Compared with LORC, EORC is more likely to be diagnosed at a later stage and has a worse outcome. Early diagnosis and timely treatment improve outcome in EORC patients.

Objective:To investigate the differences in clinical characteristics and prognosis between early- and late-onset rectal cancer (EORC and LORC, respectively), and to analyze the adverse factors affecting outcomes in EORC patients.Methods:This retrospective cohort and propensity score matching (PSM) study examined 904 rectal cancer patients who underwent radical resection at Peking University Shougang Hospital between 2017 and 2022. Prior to comparison, patients in the EORC group (<50 years old) and LORC group (≥50 years old) were matched at a 1:2 ratio to control for the following confounders: sex; neoadjuvant therapy; T, N, and M stage; and adjuvant treatment. Cox regression was used to identify independent risk factors for poor overall and progression-free survival (OS and PFS, respectively). Restricted cubic splines were used to analyze the association between age and clinical outcome.Results:A total of 199 EORC and 705 LORC patients were included for analysis. Prior to PSM, the proportions of patients with stage T4 [27.6%(55/199) vs.12.9%(91/705),χ 2=30.12, P<0.001] and M1 disease [24.6%(49/199) vs. 15.7% (111/705),χ 2=8.40, P=0.004], and the proportions of patients who received neoadjuvant [79.9% (159/199) vs. 62.3%(439/705), χ 2=21.54, P<0.001] and adjuvant therapy [62.8%(125/199) vs. 50.8% (358/705), χ 2=9.03, P=0.003] were significantly higher in the EORC group. Mean OS (57.8 vs. 51.9 months; P=0.011) and PFS (53.6 vs. 44.5 months; P=0.001) were also significantly longer in the LORC group. However, after PSM, the intergroup differences in OS and PFS were not significant ( P=0.450 and 0.180, respectively). Multivariate Cox regression in the EORC cohort identified carcinoembryonic antigen concentration ≥5 μg/L [hazard ratio (HR), 3.79; 95% confidence interval (CI), 1.34-10.69; P=0.012] and presence of perineural invasion (HR, 7.27; 95%CI, 1.77-29.88; P=0.006) as independent risk factors for overall mortality; the only independent risk factor for cancer progression was carcinoembryonic antigen concentration ≥5 μg/L (HR, 2.56; 95%CI, 1.06-6.17; P=0.037). Restricted cubic spline analysis showed a U-shaped relationship between age and clinical outcome. After PSM, OS and PFS did not show a significant association with age in the < 60 years old group. Conclusion:Compared with LORC, EORC is more likely to be diagnosed at a later stage and has a worse outcome. Early diagnosis and timely treatment improve outcome in EORC patients.

BACKGROUND:MRI is important for the diagnosis of early knee osteoarthritis.MRI image recognition and intelligent segmentation of knee osteoarthritis using deep learning method is a hot topic in image diagnosis of artificial intelligence. OBJECTIVE:Through deep learning of MRI images of knee osteoarthritis,the segmentation of femur,tibia,patella,cartilage,meniscus,ligaments,muscles and effusion of knee can be automatically divided,and then volume of knee fluid and muscle content were measured. METHODS:100 normal knee joints and 100 knee osteoarthritis patients were selected and randomly divided into training dataset(n=160),validation dataset(n=20),and test dataset(n=20)according to the ratio of 8:1:1.The Coarse-to-Fine sequential training method was used to train the 3D-UNET network deep learning model.A Coarse MRI segmentation model of the knee sagittal plane was trained first,and the rough segmentation results were used as a mask,and then the fine segmentation model was trained.The T1WI and T2WI images of the sagittal surface of the knee joint and the marking files of each structure were input,and DeepLab v3 was used to segment bone,cartilage,ligament,meniscus,muscle,and effusion of knee,and 3D reconstruction was finally displayed and automatic measurement results(muscle content and volume of knee fluid)were displayed to complete the deep learning application program.The MRI data of 26 normal subjects and 38 patients with knee osteoarthritis were screened for validation. RESULTS AND CONCLUSION:(1)The 26 normal subjects were selected,including 13 females and 13 males,with a mean age of(34.88±11.75)years old.The mean muscle content of the knee joint was(1 051 322.94±2 007 249.00)mL,the mean median was 631 165.21 mL,and the mean volume of effusion was(291.85±559.59)mL.The mean median was 0 mL.(2)There were 38 patients with knee osteoarthritis,including 30 females and 8 males.The mean age was(68.53±9.87)years old.The mean muscle content was(782 409.18±331 392.56)mL,the mean median was 689 105.66 mL,and the mean volume of effusion was(1 625.23±5 014.03)mL.The mean median was 178.72 mL.(3)There was no significant difference in muscle content between normal people and knee osteoarthritis patients.The volume of effusion in patients with knee osteoarthritis was higher than that in normal subjects,and the difference was significant(P<0.05).(4)It is indicated that the intelligent segmentation of MRI images by deep learning can discard the defects of manual segmentation in the past.The more accuracy evaluation of knee osteoarthritis was necessary,and the image segmentation was processed more precisely in the future to improve the accuracy of the results.