BackgroundIn the era of intensity-modulated radiotherapy (IMRT), the role of neoadjuvant chemotherapy (NAC) for locoregionally advanced nasopharyngeal carcinoma (NPC) is under-evaluated. The aim of this study was to compare the efficacy of NAC plus IMRT and concurrent chemoradiotherapy (CCRT) plus adjuvant chemotherapy (AC) on locoregionally advanced NPC.MethodsBetween January 2004 and December 2008, 240 cases of locoregionally advanced NPC confirmed by pathologic assessment in Sun Yat-sen University Cancer Center were reviewed. Of the 240 patients, 117 received NAC followed by IMRT, and 123 were treated with CCRT plus AC. The NAC + IMRT group received a regimen that included cisplatin and 5-fluorouracil (5-FU). The CCRT + AC group received cisplatin concurrently with radiotherapy, and subsequently received adjuvant cisplatin and 5-FU. The survival rates were assessed by Kaplan–Meier analysis, and the survival curves were compared using a log-rank test. Multivariate analysis was conducted using the Cox proportional hazard regression model.ResultsThe 5-year overall survival (OS), locoregional relapse-free survival (LRRFS), distant metastasis-free survival (DMFS), and disease-free survival (DFS) were 78.0, 87.9, 79.0, and 69.8%, respectively, for the NAC + IMRT group and 78.7, 84.8, 76.2, and 65.6%, respectively, for the CCRT + AC group. There were no significant differences in survival between the two groups. In multivariate analysis, age (<50 years vs. ≥50 years) and overall stage (III vs. IV) were found to be independent predictors for OS and DFS; furthermore, the overall stage was a significant prognostic factor for DMFS. Compared with the CCRT + AC protocol, the NAC + IMRT protocol significantly reduced the occurrence rates of grade 3–4 nausea–vomiting (6.5 vs. 1.5%, P = 0.023) and leukopenia (9.7 vs. 0.8%, P = 0.006).ConclusionsThe treatment outcomes of the NAC + IMRT and CCRT + AC groups were similar. Distant metastasis remained the predominant mode of treatment failure.
Objective To develop and test computer software to detect, quantify, and monitor progression of pneumonia associated with COVID-19 using chest CT scans. Methods One hundred twenty chest CT scans from subjects with lung infiltrates were used for training deep learning algorithms to segment lung regions and vessels. Seventy-two serial scans from 24 COVID-19 subjects were used to develop and test algorithms to detect and quantify the presence and progression of infiltrates associated with COVID-19. The algorithm included (1) automated lung boundary and vessel segmentation, (2) registration of the lung boundary between serial scans, (3) computerized identification of the pneumonitis regions, and (4) assessment of disease progression. Agreement between radiologist manually delineated regions and computer-detected regions was assessed using the Dice coefficient. Serial scans were registered and used to generate a heatmap visualizing the change between scans. Two radiologists, using a five-point Likert scale, subjectively rated heatmap accuracy in representing progression. Results There was strong agreement between computer detection and the manual delineation of pneumonic regions with a Dice coefficient of 81% (CI 76-86%). In detecting large pneumonia regions (> 200 mm 3), the algorithm had a sensitivity of 95% (CI 94-97%) and specificity of 84% (CI 81-86%). Radiologists rated 95% (CI 72 to 99) of heatmaps at least "acceptable" for representing disease progression. Conclusion The preliminary results suggested the feasibility of using computer software to detect and quantify pneumonic regions associated with COVID-19 and to generate heatmaps that can be used to visualize and assess progression. Key Points • Both computer vision and deep learning technology were used to develop computer software to quantify the presence and progression of pneumonia associated with COVID-19 depicted on CT images. • The computer software was tested using both quantitative experiments and subjective assessment. • The computer software has the potential to assist in the detection of the pneumonic regions, monitor disease progression, and assess treatment efficacy related to COVID-19.
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