Feng Li

Background: The responses of cancer patients to immune checkpoint inhibitors (ICIs) vary in success. CD8+ tumor infiltrating lymphocytes (TILs) play a key role in killing tumor cells. This study aims to evaluate the prognostic role of CD8+ TILs in cancer patients treated with ICIs. Methods: We systematically searched all publications from PubMed, EMBASE, and Cochrane Library until 12 Jul 2021 without any restriction of language or article types. Studies assessing high versus low CD8+ TILs in predicting efficacy and survival of various cancer patients were included. The outcomes included overall survival (OS), progression-free survival (PFS), and objective response rate (ORR). The study protocol is prospectively registered on PROSPERO (registration number CRD42021233654). Findings: Findings: A total of 33 studies consisting of 2559 cancer patients were included. The result showed that high CD8+ TILs were significantly associated with better OS (HR, 0.52; 95% confidence interval: 0.410.67; p < 0.001), PFS (HR, 0.52; 95% confidence interval: 0.400.67; p < 0.001) and ORR (OR, 4.08; 95% confidence interval: 2.736.10; p < 0.001) in patients treated with ICIs. Subgroup analyses suggested that patients with high CD8+ TILs had a better clinical benefit, regardless of different treatments (ICI mono therapy, or combination therapy), cancer types (NSCLC, melanoma and others), and CD8+ T cells locations (intratumor, stroma, and invasive margin). The higher baseline circulating CD8+ T cells from peripheral blood did not contribute to the improved OS (HR, 0.93; 95% confidence interval: 0.671.29; p = 0.67) and PFS (HR, 0.89; 95% confidence interval: 0.601.32; p = 0.56) compared with the low baseline. Interpretation: Interpretation: Our results suggested that high intra-tumoral, stromal, or invasive marginal, but not circulating CD8+ T cells, can predict treatment outcomes in patients with ICIs therapy across different cancers, in either single-agent ICIs or combination with other therapies.

Low-dose helical computed tomography (LDCT) is being applied as a modality for lung cancer screening. It may be difficult, however, for radiologists to distinguish malignant from benign nodules in LDCT. Our purpose in this study was to develop a computer-aided diagnostic (CAD) scheme for distinction between benign and malignant nodules in LDCT scans by use of a massive training artificial neural network (MTANN). The MTANN is a trainable, highly nonlinear filter based on an artificial neural network. To distinguish malignant nodules from six different types of benign nodules, we developed multiple MTANNs (multi-MTANN) consisting of six expert MTANNs that are arranged in parallel. Each of the MTANNs was trained by use of input CT images and teaching images containing the estimate of the distribution for the "likelihood of being a malignant nodule," i.e., the teaching image for a malignant nodule contains a two-dimensional Gaussian distribution and that for a benign nodule contains zero. Each MTANN was trained independently with ten typical malignant nodules and ten benign nodules from each of the six types. The outputs of the six MTANNs were combined by use of an integration ANN such that the six types of benign nodules could be distinguished from malignant nodules. After training of the integration ANN, our scheme provided a value related to the "likelihood of malignancy" of a nodule, i.e., a higher value indicates a malignant nodule, and a lower value indicates a benign nodule. Our database consisted of 76 primary lung cancers in 73 patients and 413 benign nodules in 342 patients, which were obtained from a lung cancer screening program on 7847 screenees with LDCT for three years in Nagano, Japan. The performance of our scheme for distinction between benign and malignant nodules was evaluated by use of receiver operating characteristic (ROC) analysis. Our scheme achieved an Az (area under the ROC curve) value of 0.882 in a round-robin test. Our scheme correctly identified 100% (76/76) of malignant nodules as malignant, whereas 48% (200/413) of benign nodules were identified correctly as benign. Therefore, our scheme may be useful in assisting radiologists in the diagnosis of lung nodules in LDCT.

PURPOSE: To determine whether liver function correlating with indocyanine green (ICG) clearance could be estimated quantitatively from gadoxetate disodium-enhanced magnetic resonance (MR) images. MATERIALS AND METHODS: This retrospective study was approved by the institutional review board, and the requirement for informed consent was waived. Twenty-three consecutive patients who underwent an ICG clearance test and gadoxetate disodium-enhanced MR imaging with the same parameters as were used for a preoperative examination were chosen. The hepatocellular uptake index (HUI) from liver volume (V(L))and mean signal intensity of the liver on contrast-enhanced T1-weighted images with fat suppression (L(20)) and mean signal intensity of the spleen on contrast-enhanced T1-weighted images with fat suppression (S(20)) on 3D gradient-echo T1-weighted images with fat suppression obtained at 20 minutes after gadoxetate disodium (0.025 mmol per kilogram of body weight) administration was determined with the following equation: V(L)[(L(20)/S(20)) - 1]. The correlation of the plasma disappearance rate of ICG (ICG-PDR) and various factors derived from MR imaging, including HUI, iron and fat deposition in the liver and spleen, and spleen volume (V(S)), were evaluated with stepwise multiple regression analysis. The difference between the ratio of the remnant HUI to the HUI of the total liver (rHUI/HUI) and ratio of the liver remnant V(L) to the total V(L) (rV(L)/V(L)) was evaluated in four patients who had segmental heterogeneity of liver function. RESULTS: HUI and V(S) were the factors significantly correlated with ICG-PDR (R = 0.87). The mean value and its 95% confidence interval were 0.18 and 0.01 to 0.34, respectively, for the following calculation: (rHUI/HUI) - (rV(L)/V(L)). CONCLUSION: The liver function correlating with ICG-PDR can be estimated quantitatively from the signal intensities and the volumes of the liver and spleen on gadoxetate disodium-enhanced MR images, which may improve the estimation of segmental liver function.

BACKGROUND: HPV has been found repeatedly in esophageal carcinoma tissues. However, reported detection rates of HPV DNA in these tumors have varied markedly. Differences in detection methods, sample types, and geographic regions of sample origin have been suggested as potential causes of this discrepancy. METHODS: HPV L1 DNA and HPV genotypes were evaluated in 435 esophageal carcinoma specimens collected from four geographic regions with different ethnicities including Anyang in north China, Shantou in south China, Xinjiang in west China, and the United States. The HPV L1 fragment was detected using SPF1/GP6+ primers. HPV genotyping was performed using genotype specific PCR. RESULTS: Two hundred and forty four of 435 samples (56.1%) tested positive for HPV L1. Significant differences in detection rate were observed neither among the three areas of China nor between China and the US. HPV6, 16, 18, 26, 45, 56, 57, and 58 were identified in L1 positive samples. HPV16 and 57 were the most common types in all regions, followed by HPV26 and HPV18. CONCLUSIONS: HPV infection is common in esophageal carcinoma independent of region and ethnic group of origin. Findings in this study raise the possibility that HPV is involved in esophageal carcinogenesis. Further investigation with a larger sample size over broader geographic areas may be warranted.