Machine learning-based radiomics score improves prognostic prediction accuracy of stage II/III gastric cancer: A multi-cohort study.

BACKGROUND: Although accurately evaluating the overall survival (OS) of gastric cancer patients remains difficult, radiomics is considered an important option for studying prognosis. AIM: To develop a robust and unbiased biomarker for predicting OS using machine learning and computed tomography (CT) image radiomics. METHODS: This study included 181 stage II/III gastric cancer patients, 141 from Lichuan People's Hospital, and 40 from the Cancer Imaging Archive (TCIA). Primary tumors in the preoperative unenhanced CT images were outlined as regions of interest (ROI), and approximately 1700 radiomics features were extracted from each ROI. The skeletal muscle index (SMI) and skeletal muscle density (SMD) were measured using CT images from the lower margin of the third lumbar vertebra. Using the least absolute shrinkage and selection operator regression with 5-fold cross-validation, 36 radiomics features were identified as important predictors, and the OS-associated CT image radiomics score (OACRS) was calculated for each patient using these important predictors. RESULTS: Patients with a high OACRS had a poorer prognosis than those with a low OACRS score (P < 0.05) and those in the TCIA cohort. Univariate and multivariate analyses revealed that OACRS was a risk factor [RR = 3.023 (1.896-4.365), P < 0.001] independent of SMI, SMD, and pathological features. Moreover, OACRS outperformed SMI and SMD and could improve OS prediction (P < 0.05). CONCLUSION: A novel biomarker based on machine learning and radiomics was developed that exhibited exceptional OS discrimination potential.

[Response of plant leaf traits to microhabitat change in a subalpine meadow on the eastern edge of Qinghai-Tibetan Plateau, China]. / 青藏高原东缘亚高寒草甸植物叶性状对微生境变化的响应.

The response of plant leaf traits in five different slope aspects to microhabitat changes was studied on subalpine meadow in Gannan. The number of plant species was 25, 36, 42, 47 and 49 in the sunny slope, half-sunny slope, west slope, half-negative slope and shady slope, respectively. Soil water content, soil nutrients, soil temperature, illumination and soil pH were significantly different among the different slope aspects. Soil water content had the maximum value in the half-shady slope, and daylight illumination, daily soil temperature and soil pH had the maximum value in the sunny slope. Specific leaf area, leaf dry matter, leaf water content, net photosynthetic rate, transpiration rate and water use efficiency were significantly different among the different slope aspects, indicating plant photosynthesis strategy was different in the different microhabitats. Kobresia humilis and Taraxacum mongolicum were dominant species in the sunny slope. Kobresia humilis, Taraxacum mongolicum, Gueldenstaedtia verna and Polygonum macrophyllum were dominant species in the half-sunny slope and west slope. Potentilla fruticosa, Polygonum macrophyllum and Stellera chamaejasme were dominant species in the half-shady slope and shady slope. Habitat quality led to low biomass in the sunny slope and high biomass in the half-shady slope. Biodiversity and abundance were relatively low in the sunny slope and half-shady slope. Biomass was medium, and biodiversity and abundance were the highest in the west slope.