Lutronic (South Korea)

Recently, anti-programmed cell death-1/anti-programmed cell death ligand-1 (anti-PD1/L1) immunotherapy has been demonstrated for its efficacy when combined with cytotoxic chemotherapy in randomized phase 3 trials for advanced biliary tract cancer (BTC). However, no biomarker predictive of benefit has been established for anti-PD1/L1 in BTC. Here, we evaluated tumor-infiltrating lymphocytes (TIL) using artificial intelligence-powered immune phenotype (AI-IP) analysis in advanced BTC treated with anti-PD1. Pretreatment hematoxylin and eosin (H&E)-stained whole-slide images from 339 patients with advanced BTC who received anti-PD1 as second-line treatment or beyond, were employed for AI-IP analysis and correlative analysis between AI-IP and efficacy outcomes with anti-PD1. Next, data and images of the BTC cohort from The Cancer Genome Atlas (TCGA) were additionally analyzed to evaluate the transcriptomic and mutational characteristics of various AI-IP in BTC. Overall, AI-IP were classified as inflamed [high intratumoral TIL (iTIL)] in 40 patients (11.8%), immune-excluded (low iTIL and high stromal TIL) in 167 patients (49.3%), and immune-desert (low TIL overall) in 132 patients (38.9%). The inflamed IP group showed a substantially higher overall response rate compared with the noninflamed IP groups (27.5% vs. 7.7%, P < 0.001). Median overall survival and progression-free survival were significantly longer in the inflamed IP group than in the noninflamed IP group (OS, 12.6 vs. 5.1 months; P = 0.002; PFS, 4.5 vs. 1.9 months; P < 0.001). In the TCGA cohort analysis, the inflamed IP showed increased cytolytic activity scores and IFNγ signature compared with the noninflamed IP. AI-IP based on spatial TIL analysis was effective in predicting the efficacy outcomes in patients with BTC treated with anti-PD1 therapy. Further validation is necessary in the context of anti-PD1/L1 plus gemcitabine-cisplatin.

[This corrects the article on p. 3735 in vol. 8, PMID: 28856046.].

Tuberculosis (TB) is one of the major global health threats especially in developing countries. Although newly diagnosed TB patients can be recovered with high cure rate, many curable TB patients in the developing countries are obliged to die because of delayed diagnosis, partly by the lack of radiography and radiologists. Therefore, developing a computer-aided diagnosis (CAD) system for TB screening can contribute to early diagnosis of TB, which results in prevention of deaths from TB. Currently, most CAD algorithms adopt carefully designed morphological features distinguishing different lesion types to improve screening performances. However, such engineered features cannot be guaranteed to be the best descriptors for TB screening. Deep learning has become a majority in machine learning society. Especially in computer vision fields, it has been verified that deep convolutional neural networks (CNN) is a very promising algorithm for various visual tasks. Since deep CNN enables end-to-end training from feature extraction to classification, it does not require objective-specific manual feature engineering. In this work, we designed CAD system based on deep CNN for automatic TB screening. Based on large-scale chest X-rays (CXRs), we achieved viable TB screening performance of 0.96, 0.93 and 0.88 in terms of AUC for three real field datasets, respectively, by exploiting the effect of transfer learning.

Surgeons use sophisticated but imperfect clinical algorithms to analyze visible skin conditions for precise decision making. Algorithmic approaches, current limitations and future ideas will be discussed.

Enlarged facial pores remain one of the major cosmetic concerns among Asian females. This study attempted to assess and compare the efficacy of a combination of the Q-switched and quasi long-pulsed (micropulsed) Nd:YAG laser to reduce the size of the enlarged pores with and without an exogenous photoenhancer. In twenty five female subjects mean age 34.04 yr and skin type II-IV, a carbon lotion as a photoenhancer was applied on one side of the face (Method 1) and the other side was used as the control (Method 2). The entire face was then treated with a single pass of the 1064 nm Nd:YAG laser in the micropulsed mode, pulse fluence and width of 2.3 J/cm(2) and 300 µsec, respectively. Multiple passes were then delivered in the Q-switched mode (2.5 J/cm(2) and 5 nsec). Three weeks after the final treatment, 75% of the subjects showed improvement with method 1 whereas 67% showed improvement with method 2. No adverse side effects were reported with either method. Although histological confirmation was not performed, we were able to prove both subjectively and objectively that the use of the combination of the micropulsed and Q-switched modes of the Nd:YAG laser was useful in reducing pore size, and that the photoenhancer improved the efficacy.