Correlation between spleen density and prognostic outcomes in patients with colorectal cancer after curative resection.

OBJECTIVE: The objective of this study was to investigate the correlation between spleen density and the prognostic outcomes of patients who underwent curative resection for colorectal cancer (CRC). METHODS: The clinical data of patients who were diagnosed with CRC and underwent radical resection were retrospectively analyzed. Spleen density was determined using computed tomography. Analysis of spleen density in relation to overall survival (OS) and disease-free survival (DFS) utilizing the Kaplan-Meier method. Univariate and multivariate Cox regression models were used to screen for independent prognostic factors, and a nomogram was constructed to predict OS and DFS. Moreover, internally validated using a bootstrap resamplling method. RESULTS: Two hundred twelve patients were included, of whom 23 (10.85%) were defined as having a diffuse reduction of spleen density (DROSD) based on diagnostic cutoff values (spleen density≦37.00HU). Kaplan-Meier analysis indicated that patients with DROSD had worse OS and DFS than those non-DROSD (P < 0.05). Multivariate Cox regression analysis revealed that DROSD, carbohydrate antigen 199 (CA199) > 37 U/mL, tumor node metastasis (TNM) stage III-IV, laparoscopy-assisted operation and American Society of Anesthesiology (ASA) score were independent risk factors for 3-year DFS. DROSD, CA199 > 37 U/mL, TNM stage III-IV, hypoalbuminemia, laparoscopy-assisted operation and ASA score were chosen as predictors of for 3-year OS. Nomograms showed satisfactory accuracy in predicting OS and DFS using calibration curves, decision curve analysis and bootstrap resamplling method. CONCLUSION: Patients with DROSD who underwent curative resection have worse 3-year DFS and OS. The nomogram demonstrated good performance, particularly in predicting 3-year DFS with a net clinical benefit superior to well-established risk calculator.

The psoas muscle density as a predictor of postoperative complications in elderly patients undergoing rectal cancer resection.

Background: Muscle depletion that impairs normal physiological function in elderly patients leads to poor prognosis. This study aimed to evaluate the association between total abdominal muscle area (TAMA), total psoas area (TPA), psoas muscle density (PMD), and short-term postoperative complications in elderly patients with rectal cancer. Methods: All elderly patients underwent rectal cancer resection with perioperative abdominal computed tomography (CT). Complications were assessed according to the Clavien-Dindo classification. Severe complications were defined as grade III-V following the Clavien-Dindo classification. Univariate and multivariate analyses were performed to evaluate risk factors of short-term severe postoperative complications. Results: The cohort consisted of 191 patients with a mean age of 73.60 ± 8.81 years. Among them, 138 (72.25%) patients had Clavien-Dindo 0- II, 53 (27.75%) patients had severe postoperative complications (Clavien-Dindo III-V), and 1(0.52%) patient died within 30 days of surgery. PMD was significantly higher in the Clavien-Dindo 0-II cohort compared to the Clavien-Dindo III-V cohort (p=0.004). Nevertheless, TAMA and TPA failed to exhibit significant differences. Moreover, the multivariate regression analysis implied that advanced age [OR 1.07 95%CI (1.02-1.13) p=0.013], male [OR 5.03 95%CI (1.76-14.41) p=0.003], high charlson comorbidity index (CCI) score [OR 3.60 95%CI (1.44-9.00) p=0.006], and low PMD [OR 0.94 95%CI (0.88-0.99) p=0.04] were independent risk factors of Clavien-Dindo III-V. Conclusion: Preoperative assessment of the PMD on CT can be a simple and practical method for identifying elderly patients with rectal cancer at risk for severe postoperative complications.

An Illustrated Guide to the Imaging Evolution of COVID in Non-Epidemic Areas of Southeast China.

Purpose: By analyzing the CT manifestations and evolution of COVID in non-epidemic areas of southeast China, analyzing the developmental abnormalities and accompanying signs in the early and late stages of the disease, providing imaging evidence for clinical diagnosis and identification, and assisting in judging disease progression and monitoring prognosis. Methods: This retrospective and multicenter study included 1,648 chest CT examinations from 693 patients with laboratory-confirmed COVID-19 infection from 16 hospitals of southeast China between January 19 and March 27, 2020. Six trained radiologists analyzed and recorded the distribution and location of the lesions in the CT images of these patients. The accompanying signs include crazy-paving sign, bronchial wall thickening, microvascular thickening, bronchogram sign, fibrous lesions, halo and reverse-halo signs, nodules, atelectasis, and pleural effusion, and at the same time, they analyze the evolution of the abovementioned manifestations over time. Result: There were 1,500 positive findings in 1,648 CT examinations of 693 patients; the average age of the patients was 46 years, including 13 children; the proportion of women was 49%. Early CT manifestations are single or multiple nodular, patchy, or flaky ground-glass-like density shadows. The frequency of occurrence of ground-glass shadows (47.27%), fibrous lesions (42.60%), and microvascular thickening (40.60%) was significantly higher than that of other signs. Ground-glass shadows increase and expand 3-7 days after the onset of symptoms. The distribution and location of lesions were not significantly related to the appearance time. Ground-glass shadow is the most common lesion, with an average absorption time of 6.2 days, followed by consolidation, with an absorption time of about 6.3 days. It takes about 8 days for pure ground-glass lesions to absorb. Consolidation change into ground glass or pure ground glass takes 10-14 days. For ground-glass opacity to evolve into pure ground-glass lesions, it takes an average of 17 days. For ground-glass lesions to evolve into consolidation, it takes 7 days, pure ground-glass lesions need 8 days to evolve into ground-glass lesions. The average time for CT signs to improve is 10-15 days, and the first to improve is the crazy-paving sign and nodules; while the progression of the disease is 6-12 days, the earliest signs of progression are air bronchogram signs, bronchial wall thickening, and bronchiectasis. There is no severe patient in this study. Conclusion: This study depicts the CT manifestation and evolution of COVID in non-epidemic origin areas, and provides valuable first-hand information for clinical diagnosis and judgment of patient's disease evolution and prediction.

Temporal changes of COVID-19 pneumonia by mass evaluation using CT: a retrospective multi-center study.

BACKGROUND: Coronavirus disease 2019 (COVID-19) has widely spread worldwide and caused a pandemic. Chest CT has been found to play an important role in the diagnosis and management of COVID-19. However, quantitatively assessing temporal changes of COVID-19 pneumonia over time using CT has still not been fully elucidated. The purpose of this study was to perform a longitudinal study to quantitatively assess temporal changes of COVID-19 pneumonia. METHODS: This retrospective and multi-center study included patients with laboratory-confirmed COVID-19 infection from 16 hospitals between January 19 and March 27, 2020. Mass was used as an approach to quantitatively measure dynamic changes of pulmonary involvement in patients with COVID-19. Artificial intelligence (AI) was employed as image segmentation and analysis tool for calculating the mass of pulmonary involvement. RESULTS: A total of 581 confirmed patients with 1,309 chest CT examinations were included in this study. The median age was 46 years (IQR, 35-55; range, 4-87 years), and 311 (53.5%) patients were male. The mass of pulmonary involvement peaked on day 10 after the onset of initial symptoms. Furthermore, the mass of pulmonary involvement of older patients (>45 years) was significantly severer (P<0.001) and peaked later (day 11 vs. day 8) than that of younger patients (≤45 years). In addition, there were no significant differences in the peak time (day 10 vs. day 10) and median mass (P=0.679) of pulmonary involvement between male and female. CONCLUSIONS: Pulmonary involvement peaked on day 10 after the onset of initial symptoms in patients with COVID-19. Further, pulmonary involvement of older patients was severer and peaked later than that of younger patients. These findings suggest that AI-based quantitative mass evaluation of COVID-19 pneumonia hold great potential for monitoring the disease progression.