The optimal threshold of PD-L1 combined positive score to predict the benefit of PD-1 antibody plus chemotherapy for patients with HER2-negative gastric adenocarcinoma: a meta-analysis.

BACKGROUND: Immune checkpoint inhibitors (ICIs) combined with chemotherapy have become the first-line treatment of metastatic gastric and gastroesophageal adenocarcinomas (GEACs). This study aims to figure out the optimal combined positive score (CPS) cutoff value. METHODS: We searched for randomized phase III trials to investigate the efficacy of ICIs plus chemotherapy for metastatic GEACs compared with chemotherapy alone. Pooled analyses of hazard ratios (HRs) based on PD-L1 expression were performed. RESULTS: A total of six trials (KEYNOTE-062, KEYNOTE-590, KEYNOTE-859, ATTRACTION-04, CheckMate 649, and ORIENT-16) were included, comprising 5,242 patients. ICIs plus chemotherapy significantly improved OS (HR: 0.79, 95% CI 0.72-0.86 in global patients; HR: 0.75, 95% CI 0.57-0.98 in Asian patients) and PFS (HR: 0.74, 95% CI 0.68-0.82 in global patients; HR: 0.64, 95% CI 0.56-0.73 in Asian patients) compared with chemotherapy alone. The differences in OS (ratio of HR: 1.05, 95% CI 0.79-1.40; predictive value: - 5.1%) and PFS (ratio of HR: 1.16, 95% CI 0.98-1.36; predictive value: - 13.5%) were not statistically significant between the global and Asian patients. Subgroup analyses indicated that the optimal CPS threshold was at ≥ 5 for OS and ≥ 10 for PFS with the highest predictive values. CONCLUSIONS: The benefit derived from ICIs plus chemotherapy is similar between Asian and global GEAC patients. However, those with a PD-L1 CPS < 5 or CPS < 10 may not have significant benefits from ICIs therapy. Therefore, it is advisable to routinely assess PD-L1 expression in GEAC patients considered for ICIs treatment.

A cost-effectiveness analysis of capecitabine maintenance therapy versus routine follow-up for early-stage triple-negative breast cancer patients after standard treatment from a perspective of Chinese society.

BACKGROUND: Capecitabine maintenance therapy is safe and efficacious for early-stage triple-negative breast cancer (TNBC) patients, but the cost-effectiveness of its long-term use has not been investigated. Here, we evaluated the cost-effectiveness of capecitabine maintenance therapy, compared with routine follow-up, in early-stage TNBC patients after standard treatment from a perspective of Chinese society. METHODS: A three-state Markov model based on the data from the SYSUCC-001 trial was constructed to estimate the cost-effectiveness of capecitabine maintenance therapy in a month cycle over a period of 30-year time horizon. A 5% annual discount rate was set for all costs and benefits. One-way and probabilistic sensitivity analyses were performed to explore the model uncertainties. The main outcomes include quality-adjusted life years (QALYs), incremental cost-effectiveness ratio (ICER), and the number needed to treat (NNT) to prevent one additional event. RESULTS: Compared with routine follow-up, 1-year capecitabine maintenance therapy yielded an additional 1.29 quality-adjusted life years (QALYs) at an additional cost of $3391.70, with an ICER of $2630.53 (95% CI: $1159.81-$5090.12) per QALY gained. The ICER was considerably lower than the recommended willingness-to-pay (WTP) threshold (i.e., $28,130.00 per QALY). The results were sensitive to the discount rate, drug cost, and treatment cost after relapse. Further, the NNT to prevent one additional relapse case was 29.2 (95% CI: 13.2-196.6), 16.7 (95% CI: 8.4-111.6), and 12.0 (95% CI: 5.7-82.6) at 1, 2, and 5 years, respectively. CONCLUSIONS: One-year capecitabine maintenance therapy for early-stage TNBC after standard treatment, compared with routine follow-up, was found to be highly cost-effective with promising clinical benefits and acceptable increased costs. Real-world studies are warranted to validate our findings in the future.

Toripalimab plus capecitabine in the treatment of patients with residual nasopharyngeal carcinoma: a single-arm phase 2 trial.

Patients with residual nasopharyngeal carcinoma after receiving definitive treatment have poor prognoses. Although immune checkpoint therapies have achieved breakthroughs for treating recurrent and metastatic nasopharyngeal carcinoma, none of these strategies have been assessed for treating residual nasopharyngeal carcinoma. In this single-arm, phase 2 trial, we aimed to evaluate the antitumor efficacy and safety of toripalimab (anti-PD1 antibody) plus capecitabine in patients with residual nasopharyngeal carcinoma after definitive treatment (ChiCTR1900023710). Primary endpoint of this trial was the objective response rate assessed according to RECIST (version 1.1). Secondary endpoints included complete response rate, disease control rate, duration of response, progression-free survival, safety profile, and treatment compliance. Between June 1, 2020, and May 31, 2021, 23 patients were recruited and received six cycles of toripalimab plus capecitabine every 3 weeks. In efficacy analyses, 13 patients (56.5%) had complete response, and 9 patients (39.1%) had partial response, with an objective response rate of 95.7% (95% CI 78.1-99.9). The trial met its prespecified primary endpoint. In safety analyses, 21 of (91.3%) 23 patients had treatment-related adverse events. The most frequently reported adverse event was hand-foot syndrome (11 patients [47.8%]). The most common grade 3 adverse event was hand-foot syndrome (two patients [8.7%]). No grades 4-5 treatment-related adverse events were recorded. This phase 2 trial shows that combining toripalimab with capecitabine has promising antitumour activity and a manageable safety profile for patients with residual nasopharyngeal carcinoma.

Add-on individualizing prediction of nasopharyngeal carcinoma using deep-learning based on MRI: A multicentre, validation study.

In nasopharyngeal carcinoma, deep-learning extracted signatures on MR images might be correlated with survival. In this study, we sought to develop an individualizing model using deep-learning MRI signatures and clinical data to predict survival and to estimate the benefit of induction chemotherapy on survivals of patients with nasopharyngeal carcinoma. Two thousand ninety-seven patients from three independent hospitals were identified and randomly assigned. When the deep-learning signatures of the primary tumor and clinically involved gross cervical lymph nodes extracted from MR images were added to the clinical data and TNM staging for the progression-free survival prediction model, the combined model achieved better prediction performance. Its application is among patients deciding on treatment regimens. Under the same conditions, with the increasing MRI signatures, the survival benefits achieved by induction chemotherapy are increased. In nasopharyngeal carcinoma, these prediction models are the first to provide an individualized estimation of survivals and model the benefit of induction chemotherapy on survivals.

Factors associated with positive predictive value of preliminary screening in a two-step screening strategy for colorectal neoplasms in China.

BACKGROUND: The positive predictive value (PPV) of high risk factor questionnaire (HRFQ) plus fecal immunochemical test (FIT) as preliminary screening strategy for colorectal-related neoplasia is relatively low. We aim to explore independent factors associated with PPVs of HRFQ combined FIT for selecting high risk individuals for colonoscopy. METHODS: A total of 6971 residents were enrolled in a community-based screening program. Participants who had positive results of HRFQ and/or FIT and subsequently received colonoscopy were involved. The associations of socio-demographic factors, lifestyle behaviors, and high risk factors of colorectal cancer with PPVs of HRFQ, FIT, and their combination were evaluated by multivariable logistic regression models. RESULTS: Among 572 involved cases, 249 (43.5%) colorectal neoplasms were detected by colonoscopy, including 71 advanced adenoma (12.4%) and 9 colorectal cancer (CRC) (1.6%). The PPVs of preliminary screening were 43.5% for total colorectal neoplasms, 14.0% for advanced neoplasm, and 1.6% for CRC. Adding positive HRFQ to FIT could improve the PPV from 3.5 to 8.0% for detecting CRC. Preliminarily screened positive individuals who were males [adjusted odds ratio (AOR): 1.95, 95% CI 1.31, 2.90; p < 0.001], elders (> 60 years) (AOR: 1.70, 95% CI 1.17, 2.46; p = 0.005), or ex-/current smokers (AOR: 3.04, 95% CI 1.31, 7.09; p = 0.10) had higher odds of PPVs of detecting colorectal neoplasms. CONCLUSIONS: Combining HRFQ and FIT could largely improve PPVs for screening advanced neoplasm and CRC. Gender and age-specific FIT cut-off values as well as initiating ages for CRC screening might be recommended to improve the accuracy and effectiveness of current screening algorithm.

A Robust Discriminant Framework Based on Functional Biomarkers of EEG and Its Potential for Diagnosis of Alzheimer's Disease.

(1) Background: Growing evidence suggests that electroencephalography (EEG), recording the brain's electrical activity, can be a promising diagnostic tool for Alzheimer's disease (AD). The diagnostic biomarkers based on quantitative EEG (qEEG) have been extensively explored, but few of them helped clinicians in their everyday practice, and reliable qEEG markers are still lacking. The study aims to find robust EEG biomarkers and propose a systematic discrimination framework based on signal processing and computer-aided techniques to distinguish AD patients from normal elderly controls (NC). (2) Methods: In the proposed study, EEG signals were preprocessed firstly and Maximal overlap discrete wavelet transform (MODWT) was applied to the preprocessed signals. Variance, Pearson correlation coefficient, interquartile range, Hoeffding's D measure, and Permutation entropy were extracted as the input of the candidate classifiers. The AD vs. NC discriminant performance of each model was evaluated and an automatic diagnostic framework was eventually developed. (3) Results: A classification procedure based on the extracted EEG features and linear discriminant analysis based classifier achieved the accuracy of 93.18 ± 3.65 (%), the AUC of 97.92 ± 1.66 (%), the F-measure of 94.06 ± 4.04 (%), separately. (4) Conclusions: The developed discrimination framework can identify AD from NC with high performance in a systematic routine.