Tumor response assessment by measuring the single largest lesion per organ in advanced non-small cell lung cancer patients treated with PD-1/PD-L1 inhibitor.

Background: For Response Evaluation Criteria in Solid Tumors version 1.1 (RECIST1.1), measuring up to two target lesions per organ is an arbitrary criterion. Objectives: We sought to compare response assessment using RECIST1.1 and modified RECIST1.1 (mRECIST1.1, measuring the single largest lesion per organ) in advanced non-small cell lung cancer (aNSCLC) patients undergoing anti-PD-1/PD-L1 monotherapy. Methods: Concordance of radiologic response categorization between RECIST1.1 and mRECIST1.1 was compared using the Kappa statistics. C-index was calculated to evaluate prognostic accuracy of radiologic response by the two criteria. The Kaplan-Meier method and Cox regression analysis were conducted for progression-free survival (PFS) and overall survival (OS). Results: Eighty-seven patients who had at least two target lesions in any organ per the RECIST1.1 were eligible for comparison analysis. Tumor response showed excellent concordance when measured using the RECIST1.1 and mRECIST1.1 (Kappa = 0.961). C-index by these two criteria was similar for PFS (0.784 versus 0.785) and OS (0.649 versus 0.652). Responders had significantly longer PFS and OS versus non-responders (p < 0.05), whichever criterion adopted. Radiologic response remained a significant predictor of PFS and OS in multivariate analysis (p < 0.05). Conclusion: The mRECIST1.1 was comparable to RECIST1.1 in response assessment among aNSCLC patients who received single-agent PD-1/PD-L1 inhibitor. The mRECIST1.1, with reduced number of lesions to be measured, may be sufficient and more convenient to assess antitumor activity in clinical practice.

Automated Multi-View Multi-Modal Assessment of COVID-19 Patients Using Reciprocal Attention and Biomedical Transform.

Automated severity assessment of coronavirus disease 2019 (COVID-19) patients can help rationally allocate medical resources and improve patients' survival rates. The existing methods conduct severity assessment tasks mainly on a unitary modal and single view, which is appropriate to exclude potential interactive information. To tackle the problem, in this paper, we propose a multi-view multi-modal model to automatically assess the severity of COVID-19 patients based on deep learning. The proposed model receives multi-view ultrasound images and biomedical indices of patients and generates comprehensive features for assessment tasks. Also, we propose a reciprocal attention module to acquire the underlying interactions between multi-view ultrasound data. Moreover, we propose biomedical transform module to integrate biomedical data with ultrasound data to produce multi-modal features. The proposed model is trained and tested on compound datasets, and it yields 92.75% for accuracy and 80.95% for recall, which is the best performance compared to other state-of-the-art methods. Further ablation experiments and discussions conformably indicate the feasibility and advancement of the proposed model.

Cause-specific death assessment of patients with stage I small-cell lung cancer: a competing risk analysis.

Aim: Stage I small-cell lung cancer (SCLC) is a potentially curable disease that needs timely and multidisciplinary management. The aim of this study was to evaluate the probability of cause-specific mortality for patients with stage I SCLC. Material & methods: We identified patients in the SEER database and constructed a proportional subdistribution hazard model to evaluate cancer-specific mortality. A nomogram was built based on Fine and Gray competing risk regression model. Results: A total of 864 stage I SCLC patients were identified. The 5-year cumulative incidence of SCLC-specific mortality was 56.2%, while that for other causes of death was 17.3%. The c-index for the prognostic prediction model was 0.66. Besides, the nomogram was well calibrated. Conclusion: Our nomogram might serve as a reference for clinicians when evaluating the prognosis of stage I SCLC.

Network meta-analysis of erlotinib, gefitinib, afatinib and icotinib in patients with advanced non-small-cell lung cancer harboring EGFR mutations.

BACKGROUND: Several EGFR-tyrosine kinase inhibitors (EGFR-TKIs) including erlotinib, gefitinib, afatinib and icotinib are currently available as treatment for patients with advanced non-small-cell lung cancer (NSCLC) who harbor EGFR mutations. However, no head to head trials between these TKIs in mutated populations have been reported, which provides room for indirect and integrated comparisons. METHODS: We searched electronic databases for eligible literatures. Pooled data on objective response rate (ORR), progression free survival (PFS), overall survival (OS) were calculated. Appropriate networks for different outcomes were established to incorporate all evidences. Multiple-treatments comparisons (MTCs) based on Bayesian network integrated the efficacy and specific toxicities of all included treatments. RESULTS: Twelve phase III RCTs that investigated EGFR-TKIs involving 1821 participants with EGFR mutation were included. For mutant patients, the weighted pooled ORR and 1-year PFS of EGFR-TKIs were significant superior to that of standard chemotherapy (ORR: 66.6% vs. 30.9%, OR 5.46, 95%CI 3.59 to 8.30, P<0.00001; 1-year PFS: 42.9% vs. 9.7%, OR 7.83, 95%CI 4.50 to 13.61; P<0.00001) through direct meta-analysis. In the network meta-analyses, no statistically significant differences in efficacy were found between these four TKIs with respect to all outcome measures. Trend analyses of rank probabilities revealed that the cumulative probabilities of being the most efficacious treatments were (ORR, 1-year PFS, 1-year OS, 2-year OS): erlotinib (51%, 38%, 14%, 19%), gefitinib (1%, 6%, 5%, 16%), afatinib (29%, 27%, 30%, 27%) and icotinib (19%, 29%, NA, NA), respectively. However, afatinib and erlotinib showed significant severer rash and diarrhea compared with gefitinib and icotinib. CONCLUSIONS: The current study indicated that erlotinib, gefitinib, afatinib and icotinib shared equivalent efficacy but presented different efficacy-toxicity pattern for EGFR-mutated patients. Erlotinib and afatinib revealed potentially better efficacy but significant higher toxicities compared with gefitinib and icotinib.

[The cost-effectiveness analysis of gefitinib or erlotinib in the treatment of advanced EGFR mutant non-small cell lung cancer patients].

BACKGROUND: Targeted therapy in non-small cell lung cancer (NSCLC) had become a research hotspot. Both of gefitinib and erlotinib had already been recommended as first line treatment in epidermal growth factor receptor (EGFR) mutant advanced NSCLC patients. The study aimed to compare the effectiveness and prognosis of advanced NSCLC with gefitinib or erlotinib, as well as the cost-effectiveness ratio of the two drugs. METHODS: Data of 66 EGFR mutant NSCLC patients who were included in Guangzhou medical insurance were analyzed. The efficacy and adverse reactions were evaluated. All the patients were followed-up regularly and the cost of the treatment was recorded. RESULTS: The median progression free survival (PFS) of all patients was 15.0 months. 49 patients received gefintib and 17 patients had erlotinib. The PFS for the two groups of patients was 17.5 month and 13 months, respectively (P=0.459). 31 (62.3%) patients had rash in gefitinib group, 16 (94.1%) in erlotinib group. Cost-effectiveness ratio (CER) in gefitinib group was 3,027 RMB per month, while 6,800 RMB in erlotinib group. The incremental cost-effectiveness ratio (ICEA) of erlotinib was 2.25 times of gefitinib. CONCLUSIONS: For EGFR mutant advanced NSCLC patients, equal efficacy and survival benefit were observed in patients with gefitinib and erlotinib. The adverse reaction was milder in gefitinib group than that of erlotinib group. And with Guangzhou medical insurance, gefitinib had a superior cost-effectiveness ratio.