Parallel CNN-Deep Learning Clinical-Imaging Signature for Assessing Pathologic Grade and Prognosis of Soft Tissue Sarcoma Patients.

BACKGROUND: Traditional biopsies pose risks and may not accurately reflect soft tissue sarcoma (STS) heterogeneity. MRI provides a noninvasive, comprehensive alternative. PURPOSE: To assess the diagnostic accuracy of histological grading and prognosis in STS patients when integrating clinical-imaging parameters with deep learning (DL) features from preoperative MR images. STUDY TYPE: Retrospective/prospective. POPULATION: 354 pathologically confirmed STS patients (226 low-grade, 128 high-grade) from three hospitals and the Cancer Imaging Archive (TCIA), divided into training (n = 185), external test (n = 125), and TCIA cohorts (n = 44). 12 patients (6 low-grade, 6 high-grade) were enrolled into prospective validation cohort. FIELD STRENGTH/SEQUENCE: 1.5 T and 3.0 T/Unenhanced T1-weighted and fat-suppressed-T2-weighted. ASSESSMENT: DL features were extracted from MR images using a parallel ResNet-18 model to construct DL signature. Clinical-imaging characteristics included age, gender, tumor-node-metastasis stage and MRI semantic features (depth, number, heterogeneity at T1WI/FS-T2WI, necrosis, and peritumoral edema). Logistic regression analysis identified significant risk factors for the clinical model. A DL clinical-imaging signature (DLCS) was constructed by incorporating DL signature with risk factors, evaluated for risk stratification, and assessed for progression-free survival (PFS) in retrospective cohorts, with an average follow-up of 23 ± 22 months. STATISTICAL TESTS: Logistic regression, Cox regression, Kaplan-Meier curves, log-rank test, area under the receiver operating characteristic curve (AUC)，and decision curve analysis. A P-value <0.05 was considered significant. RESULTS: The AUC values for DLCS in the external test, TCIA, and prospective test cohorts (0.834, 0.838, 0.819) were superior to clinical model (0.662, 0.685, 0.694). Decision curve analysis showed that the DLCS model provided greater clinical net benefit over the DL and clinical models. Also, the DLCS model was able to risk-stratify patients and assess PFS. DATA CONCLUSION: The DLCS exhibited strong capabilities in histological grading and prognosis assessment for STS patients, and may have potential to aid in the formulation of personalized treatment plans. TECHNICAL EFFICACY: Stage 2.

An unusually high prevalence of allergic rhinitis at high altitudes in 6-7 year old children - An epidemiological study.

Objectives: To compare the epidemiology and disease patterns of allergic rhinitis (AR) at 2 different altitudes in children aged 6-7 years, and subsequently to compare with and augment data from international studies. Materials and methods: This is a multistage, clustered and stratified random sample study. The study area comprises 2 distinct areas within Yunnan Province, China. Low altitude was represented by Xishuangbanna Prefecture (XB), while high altitude was represented by Diqing Prefecture (DiQ). Each study area was subdivided into 3 sub-areas, and children aged 6-7 years were randomly sampled based on proportion-weighted sampling. The area studied includes the well-known area of Shangri-La city. Questionnaires were distributed and jointly completed by study participants and their parents or guardians, under the guidance of professional medical staff. Results: 2796 valid questionnaires out of 2933 distributed were obtained (survey response rate 95.3%). The prevalence of AR is statistically significantly higher at high altitude (DiQ, 36.0%, 95%CI 33.2-38.8) as compared to low altitude (XB, 19.7%, 95%CI 17.8-21.6) (p < 0.001). Both areas studied had a greater prevalence of AR compared to international data. In both XB and DiQ, male gender, history of early antibiotic use, urban place of birth and place of residence, presence of smokers within the same household, family history of allergic diseases (such as atopic dermatitis), as well as higher parental educational level were all associated with a higher prevalence of AR (p < 0.05). In DiQ, the prevalence of AR in Han ethnicity was greater than that of ethnic minorities (p < 0.05). In XB, being a single child was associated with an increased prevalence of AR compared to those who had siblings (p < 0.05). Conclusion: Our study found that the prevalence of AR is relatively greater at higher altitudes. Genetic and environmental factors both play an important role in the pathogenesis of AR. While altitude may be an important environmental factor, confounding factors may include humidity, temperature and distribution pattern of common aeroallergens.