Artificial intelligence-based models enabling accurate diagnosis of ovarian cancer using laboratory tests in China: a multicentre, retrospective cohort study.

BACKGROUND: Ovarian cancer is the most lethal gynecological malignancy. Timely diagnosis of ovarian cancer is difficult due to the lack of effective biomarkers. Laboratory tests are widely applied in clinical practice, and some have shown diagnostic and prognostic relevance to ovarian cancer. We aimed to systematically evaluate the value of routine laboratory tests on the prediction of ovarian cancer, and develop a robust and generalisable ensemble artificial intelligence (AI) model to assist in identifying patients with ovarian cancer. METHODS: In this multicentre, retrospective cohort study, we collected 98 laboratory tests and clinical features of women with or without ovarian cancer admitted to three hospitals in China during Jan 1, 2012 and April 4, 2021. A multi-criteria decision making-based classification fusion (MCF) risk prediction framework was used to make a model that combined estimations from 20 AI classification models to reach an integrated prediction tool developed for ovarian cancer diagnosis. It was evaluated on an internal validation set (3007 individuals) and two external validation sets (5641 and 2344 individuals). The primary outcome was the prediction accuracy of the model in identifying ovarian cancer. FINDINGS: Based on 52 features (51 laboratory tests and age), the MCF achieved an area under the receiver-operating characteristic curve (AUC) of 0·949 (95% CI 0·948-0·950) in the internal validation set, and AUCs of 0·882 (0·880-0·885) and 0·884 (0·882-0·887) in the two external validation sets. The model showed higher AUC and sensitivity compared with CA125 and HE4 in identifying ovarian cancer, especially in patients with early-stage ovarian cancer. The MCF also yielded acceptable prediction accuracy with the exclusion of highly ranked laboratory tests that indicate ovarian cancer, such as CA125 and other tumour markers, and outperformed state-of-the-art models in ovarian cancer prediction. The MCF was wrapped as an ovarian cancer prediction tool, and made publicly available to provide estimated probability of ovarian cancer with input laboratory test values. INTERPRETATION: The MCF model consistently achieved satisfactory performance in ovarian cancer prediction when using laboratory tests from the three validation sets. This model offers a low-cost, easily accessible, and accurate diagnostic tool for ovarian cancer. The included laboratory tests, not only CA125 which was the highest ranked laboratory test in importance of diagnostic assistance, contributed to the characterisation of patients with ovarian cancer. FUNDING: Ministry of Science and Technology of China; National Natural Science Foundation of China; Natural Science Foundation of Guangdong Province, China; and Science and Technology Project of Guangzhou, China. TRANSLATION: For the Chinese translation of the abstract see Supplementary Materials section.

Progress in Research on Stem Cells in Neonatal Refractory Diseases.

With the development and progress of medical technology, the survival rate of premature and low-birth-weight infants has increased, as has the incidence of a variety of neonatal diseases, such as hypoxic-ischemic encephalopathy, intraventricular hemorrhage, bronchopulmonary dysplasia, necrotizing enterocolitis, and retinopathy of prematurity. These diseases cause severe health conditions with poor prognoses, and existing control methods are ineffective for such diseases. Stem cells are a special type of cells with self-renewal and differentiation potential, and their mechanisms mainly include anti-inflammatory and anti-apoptotic properties, reducing oxidative stress, and boosting regeneration. Their paracrine effects can affect the microenvironment in which they survive, thereby affecting the biological characteristics of other cells. Due to their unique abilities, stem cells have been used in treating various diseases. Therefore, stem cell therapy may open up the possibility of treating such neonatal diseases. This review summarizes the research progress on stem cells and exosomes derived from stem cells in neonatal refractory diseases to provide new insights for most researchers and clinicians regarding future treatments. In addition, the current challenges and perspectives in stem cell therapy are discussed.

Rectum Protection by Rectal Gel Injection in Cervical Cancer Brachytherapy: A Dosimetric Study via Deformable Surface Dose Accumulation and Machine-Learning-Based Discriminative Modeling.

PURPOSE: This retrospective study aimed to evaluate the dosimetric effects of a rectal insertion of Kushen Ningjiao on rectal protection using deformable dose accumulation and machine learning-based discriminative modelling. MATERIALS AND METHODS: Sixty-two patients with cervical cancer enrolled in a clinical trial, who received a Kushen Ningjiao injection of 20 g into their rectum for rectal protection via high-dose rate brachytherapy (HDR-BT, 6 Gy/f), were studied. The cumulative equivalent 2-Gy fractional rectal surface dose was deformably summed using an in-house-developed topography-preserved point-matching deformable image registration method. The cumulative three-dimensional (3D) dose was flattened and mapped to a two-dimensional (2D) plane to obtain the rectal surface dose map (RSDM). For analysis, the rectal dose (RD) was further subdivided as follows: whole, anterior, and posterior 3D-RD and 2D-RSDM. The dose-volume parameters (DVPs) were extracted from the 3D-RD, while the dose geometric parameters (DGPs) and textures were extracted from the 2D-RSDM. These features were fed into 192 classification models (built with 8 classifiers and 24 feature selection methods) for discriminating the dose distributions between pre-Kushen Ningjiao and pro-Kushen Ningjiao. RESULTS: The rectal insertion of Kushen Ningjiao dialated the rectum in the ambilateral direction, with the rectal column increased from pre-KN 15 cm3 to post-KN 18 cm3 (P < 0.001). The characteristics of DGPs accounted for the largest portions of the top-ranked features. The top-ranked dosimetric features extracted from the posterior rectum were more reliable indicators of the dosimetric effects/changes introduced by the rectal insertion of Kushen Ningjiao. A significant dosimetric impact was found on the dose-volume parameters D1.0cc-D2.5cc extracted on the posterior rectal wall. CONCLUSIONS: The rectal insertion of Kushen Ningjiao incurs significant dosimetric changes on the posterior rectal wall. Whether this effect is eventually translated into clinical gains requires further long-term follow-up and more clinical data for confirmation.

Human epicardial adipose tissue-derived and circulating secreted frizzled-related protein 4 (SFRP4) levels are increased in patients with coronary artery disease.

BACKGROUND: Previous studies have demonstrated that secreted frizzled-related protein 4 (SFRP4) is associated with impaired glucose and triglyceride metabolism in patients with stable coronary artery disease. In the present study, we investigated human epicardial adipose tissue (EAT)-derived and circulating SFRP4 levels in patients with coronary artery disease (CAD). METHODS: Plasma samples and adipose biopsies from EAT and subcutaneous adipose tissue (SAT) were collected from patients with CAD (n = 40) and without CAD (non-CAD, n = 30) during elective cardiac surgery. The presence of CAD was identified by coronary angiography. SFRP4 mRNA and protein expression levels in adipose tissue were detected by quantitative real-time PCR and immunohistochemistry, respectively. Plasma SFRP4 concentrations were measured by an enzyme-linked immunosorbent assay (ELISA). Correlation analysis and multivariate linear regression analysis were used to determine the association of SFRP4 expression with atherosclerosis as well as clinical risk factors. RESULTS: SFRP4 mRNA and protein expression levels were significantly lower in EAT than in paired SAT in patients with and without CAD (all P < 0.05). Compared to non-CAD patients, CAD patients had higher SFRP4 expression levels in EAT (both mRNA and protein levels) and in plasma. Multivariate linear regression analysis showed that CAD was an independent predictor of SFRP4 expression levels in EAT (beta = 0.442, 95% CI 0.030-0.814; P = 0.036) and in plasma (beta = 0.300, 95% CI 0.056-0.545; P = 0.017). SAT-derived SFRP4 mRNA levels were independently associated with fasting insulin levels (beta = 0.382, 95% CI 0.008-0.756; P = 0.045). In addition, plasma SFRP4 levels were positively correlated with BMI (r = 0.259, P = 0.030), fasting insulin levels (r = 0.306, P = 0.010) and homeostasis model assessment of insulin resistance (HOMA-IR) values (r = 0.331, P = 0.005). CONCLUSIONS: EAT-derived and circulating SFRP4 expression levels were increased in patients with CAD. EAT SFRP4 mRNA levels and plasma SFRP4 concentrations were independently associated with the presence of CAD.