Predicting progression-free survival in patients with epithelial ovarian cancer using an interpretable random forest model.

Prognostic models play a crucial role in providing personalised risk assessment, guiding treatment decisions, and facilitating the counselling of patients with cancer. However, previous imaging-based artificial intelligence models of epithelial ovarian cancer lacked interpretability. In this study, we aimed to develop an interpretable machine-learning model to predict progression-free survival in patients with epithelial ovarian cancer using clinical variables and radiomics features. A total of 102 patients with epithelial ovarian cancer who underwent contrast-enhanced computed tomography scans were enrolled in this retrospective study. Pre-surgery clinical data, including age, performance status, body mass index, tumour stage, venous blood cancer antigen-125 (CA125) level, white blood cell count, neutrophil count, red blood cell count, haemoglobin level, and platelet count, were obtained from medical records. The volume of interest for each tumour was manually delineated slice-by-slice along the boundary. A total of 2074 radiomic features were extracted from the pre- and post-contrast computed tomography images. Optimal radiomic features were selected using the Least Absolute Shrinkage and Selection Operator logistic regression. Multivariate Cox analysis was performed to identify independent predictors of three-year progression-free survival. The random forest algorithm developed radiomic and combined models using four-fold cross-validation. Finally, the Shapley additive explanation algorithm was applied to interpret the predictions of the combined model. Multivariate Cox analysis identified CA-125 levels (P = 0.015), tumour stage (P = 0.019), and Radscore (P < 0.001) as independent predictors of progression-free survival. The combined model based on these factors achieved an area under the curve of 0.812 (95 % confidence interval: 0.802-0.822) in the training cohort and 0.772 (95 % confidence interval: 0.727-0.817) in the validation cohort. The most impactful features on the model output were Radscore, followed by tumour stage and CA-125. In conclusion, the Shapley additive explanation-based interpretation of the prognostic model enables clinicians to understand the reasoning behind predictions better.

Screening parameters for diagnosing primary aldosteronism in patients with moderate to severe obstructive sleep apnea hypopnea syndrome and resistant hypertension.

Background: Patients with obstructive sleep apnea hypopnea syndrome (OSAHS) combined with resistant hypertension (RH) have a high risk of developing primary aldosteronism (PA). This study investigated the aldosterone-renin ratio (ARR), plasma aldosterone concentration (PAC), and plasma renin activity (PRA) to determine the optimal cutoff values for PA diagnosis in patients with OSAHS combined with RH. Methods: Patients diagnosed with moderate and severe OSAHS combined with RH were recruited from the inpatient clinic of the Department of Endocrinology at Ji'an Central Hospital between October 2020 and April 2023. The included patients were divided into PA and no-PA groups. Diagnostic accuracy measures were calculated for each group, and receiver operating characteristic (ROC) curves were generated. Results: A total of 241 patients were included, of which 103 had positive ARR screening results in the diagnostic accuracy analysis and 66 were diagnosed with PA. PAC and ARR showed moderate predictive capacity for PA, with area under the curve (AUC) values of 0.66 [95% confidence interval (CI): 0.55-0.77] and 0.72 (95% CI: 0.63-0.82), respectively, while PRA exhibited a limited predictive capacity (AUC = 0.51, 95% CI: 0.40-0.63). Using 45 as the optimal cutoff value for ARR, the sensitivity was 86% and the specificity was 52%. The optimal cutoff value for PAC was 17, with a sensitivity of 78% and a specificity of 55%. Notably, in patients with severe OSAHS, ARR at screening demonstrated significant predictive value for PA, with an AUC of 0.84 (95% CI: 0.72-0.96), a sensitivity of 85%, and a specificity of 76%. Conversely, in patients with moderate OSAHS, only ARR demonstrated significant predictive value for PA diagnosis, while PAC did not demonstrate notable diagnostic value. Conclusion: ARR and PAC are initial screening tools for PA, facilitating early detection, particularly in low-resource settings. In patients with OSAHS and RH, the ARR and PAC thresholds for PA diagnosis may require more stringent adjustment.

Absorption, distribution, metabolism and excretion of linaprazan glurate in rats.

Linaprazan (AZD0865, TX07) is one of potassium-competitive acid blockers. However, linaprazan is rapidly excreted from the body, shortening its acid inhibition property. Linaprazan glurate (X842) is a prodrug of linaprazan with a prolonged inhibitory effect on gastric acid secretion. Linaprazan glurate has entered clinical trials, but few studies have reported its metabolism in non-clinical and clinical settings. In this study, we studied the pharmacokinetics, tissue distribution, mass balance, and metabolism of linaprazan glurate in rats after a single oral dose of 2.4 mg/kg (100 µCi/kg) [14C]linaprazan glurate. The results demonstrated that linaprazan glurate was mainly excreted via feces in rats with 70.48% of the dose over 168 h. The plasma AUC0-∞ of linaprazan glurate in female rats was 2 times higher than that in male rats. Drug-related substances were mainly concentrated in the stomach, eyes, liver, small intestine, and large intestine after administration. In blood, drug-related substances were mostly distributed into plasma instead of hemocytes. In total, 13 metabolites were detected in rat plasma, urine, feces, and bile. M150 (2,6-dimethylbenzoic acid) was the predominant metabolite in plasma, accounting for 80.65% and 67.65% of AUC0-24h in male and female rats, respectively. Based on the structures, linaprazan glurate was mainly hydrolyzed into linaprazan, followed by a series of oxidation, dehydrogenation, and glucuronidation in rats. Besides, CES2 is the main metabolic enzyme involved in the hydrolysis of linaprazan glurate to linaprazan.

Precisional detection of lymph node metastasis using tFCM in colorectal cancer.

The detection of colorectal cancer (CRC) lymph node (LN) metastases significantly influences treatment choices, yet identifying them in samples is time-consuming and error-prone. To enhance efficiency, we have established a LN metastasis detection method utilizing triple-parameter flow cytometry (tFCM) and have conducted a comparative assessment of its accuracy and cost-effectiveness in contrast to conventional pathological examinations. This technique utilized biomarkers cytokeratin 20 (CK20), epithelial cell adhesion molecules (EpCAM), and Pan-CK. tFCM's sensitivity was validated by analyzing known cell line concentrations (SW480 and SW620) in peripheral blood mononuclear cells (PBMCs), with CK20, EpCAM, and Pan-CK showing significant expression in CRC cell lines but not in PBMCs. A strong linear correlation was observed in the mixed leukocyte environment (R 2 = 0.9988). Subsequently, tFCM and pathological sections were employed to analyze LNs from CRC patients, enabling comparison of detection accuracy. Within the 36 LNs studied, tFCM successfully identified tumor cells with varying metastasis degrees, including micro-metastasis and isolated tumor cell clusters. Notably, relying solely on pathological sections led to a potential 25% misdiagnosis rate for LNs. In contrast, tFCM effectively minimized this risk. In summary, compared to traditional pathological sections, tFCM is a more advantageous method for detecting nodal metastasis in CRC patients, offering a more precise prognosis for these patients.

Efficacy of adoptive immunocyte infusion combined with immunodeprivation in the treatment of castration resistant prostate cancer / 中国肿瘤生物治疗杂志

@#[Abstract] Objective: To observe the clinical effect of adoptive immunocyte infusion combined with immunodeprivation in the treatment of castration-resistant prostate cancer. Methods: The information of 35 patients with castration resistant prostate cancer, who were treated in the Affiliated Guizhou Provincial Cancer Hospital of Guizhou Medical University from 2011 to 2018 was collected. According to different treatments, these patients were divided into biotherapy group (18 cases) and non-biotherapy group (17 cases). Patients in the non-biotherapy group were treated with abiraterone or docetaxel, while the patients in biotherapy group were treated with cytotoxic T lymphocytes (CTL) in combination with cyclophosphamide (CTX). The treatment efficacy in the biotherapy group and the non-biotherapy group was evaluated by comparing the changes of prostate cancer-specific antigen (PSA), improvement of subjective indicators (bone pain, sleep, physical strength) and clinical efficacy before and after treatment. Results: (1) PSA level: after treatment, PSA was decreased in both groups; the biotherapy group had an obvious decrease (P<0.01),which was more significant than the decrease in non-biotherapy group (P<0.05). (2) Clinical efficacy: The clinical efficacy of patients after CTL treatment was significantly different from that of non-biotherapy group (P<0.01). (3) Subjective indicators: The bone pain, sleep and physical strength of the patients in the biotherapy group were significantly improved after treatment, and there was a significant difference as compared with patients of the non-biological treatment group (P<0.01). (4) Overall survival: The median survival of the patients receiving biotherapy was 4 months longer than patients from non-biological treatment group, but the difference was insignificant (P=0.3935). Conclusion: CTL combined with CTX in the treatment of castration resistant prostate cancer can significantly reduce PSA and improve the quality of life of patients.