Development and validation of a prediction model for suboptimal ovarian response in polycystic ovary syndrome (PCOS) patients undergoing GnRH-antagonist protocol in IVF/ICSI cycles.

BACKGROUND: PCOS patients with unexpectedly low oocyte yield following conventional ovarian stimulation are referred to as suboptimal responders. However, identifying suboptimal responders presents a significant challenge within reproductive medicine and limited research exists on the occurrence of suboptimal response. This analysis aimed to develop a predictive model of suboptimal response during in vitro fertilization/intracytoplasmic sperm injection (IVF/ICSI) treatments in PCOS patients. METHODS: This retrospective study involved a cohort of 313 PCOS patients undergoing their first IVF/ICSI cycle from 2019 to 2022. Univariate logistic regression analyses, least absolute shrinkage, selection operator regression analysis, and recursive feature elimination were employed to identify relevant characteristics and construct predictive models. Moreover, a nomogram was constructed based on the best model. Receiver operating characteristic curves, decision curve analysis (DCA), and calibration curves were used to evaluate the model. RESULTS: The predictors included in the model were age, Anti-Mullerian hormone, antral follicle count, and basal follicle-stimulating hormone. The area under the receiver operating characteristic curve (AUC) was 0.7702 (95% confidence interval 0.7157-0.8191). The AUC, along with the DCA curve and calibration curve, demonstrated a satisfactory level of congruence and discrimination ability. CONCLUSION: The nomogram effectively predicted the probability of suboptimal response in PCOS patients undergoing gonadotropin-releasing hormone antagonist protocol during IVF/ICSI treatment.

Identification and prognostic analysis of candidate biomarkers for lung metastasis in colorectal cancer.

Colorectal cancer (CRC) is one of the most prevalent types of malignant tumors. It's vital to explore new biomarkers and potential therapeutic targets in CRC lung metastasis through adopting integrated bioinformatics tools. Multiple cohort datasets and databases were integrated to clarify and verify potential key candidate biomarkers and signal transduction pathways in CRC lung metastasis. DAVID, STRING, UALCAN, GEPIA, TIMER, cBioPortal, THE HUMAN PROTEIN ATLAS, GSEA 4.3.2, FUNRICH 3.1.3, and R 4.2.3 were utilized in this study. The enriched biological processes and pathways modulated by the differentially expressed genes (DEGs) were determined with Gene Ontology, Kyoto Encyclopedia of Genes and Genomes. The search tool Retrieval of Interacting Genes and Cytoscape were used to construct a protein-protein interaction network among DEGs. Four hundred fifty-nine colorectal primary cancer and lung metastatic gene expression profiles were screened from 3 gene expression profiles (GSE41258, GSE68468, and GSE41568). Forty-one upregulated genes and 8 downregulated genes were identified from these 3 gene expression profiles and verified by the transcriptional levels of hub genes in other GEO datasets and The Cancer Genome Atlas database. Two pathways (immune responses and chemokine receptors bind chemokines), 13 key DEGs, 6 hub genes (MMP3, SFTPD, ABCA3, CLU, APOE, and SPP1), and 2 biomarkers (APOE, SPP1) with significantly prognostic values were screened. Forty-nine DEGs were identified as potential candidate diagnostic biomarkers for patients with CRC lung metastasis in present study. Enrichment analysis indicated that immune responses and chemokine receptors bind chemokines may play a leading role in lung metastasis of CRC, and further studies are needed to validate these findings.

A multicenter clinical AI system study for detection and diagnosis of focal liver lesions.

Early and accurate diagnosis of focal liver lesions is crucial for effective treatment and prognosis. We developed and validated a fully automated diagnostic system named Liver Artificial Intelligence Diagnosis System (LiAIDS) based on a diverse sample of 12,610 patients from 18 hospitals, both retrospectively and prospectively. In this study, LiAIDS achieved an F1-score of 0.940 for benign and 0.692 for malignant lesions, outperforming junior radiologists (benign: 0.830-0.890, malignant: 0.230-0.360) and being on par with senior radiologists (benign: 0.920-0.950, malignant: 0.550-0.650). Furthermore, with the assistance of LiAIDS, the diagnostic accuracy of all radiologists improved. For benign and malignant lesions, junior radiologists' F1-scores improved to 0.936-0.946 and 0.667-0.680 respectively, while seniors improved to 0.950-0.961 and 0.679-0.753. Additionally, in a triage study of 13,192 consecutive patients, LiAIDS automatically classified 76.46% of patients as low risk with a high NPV of 99.0%. The evidence suggests that LiAIDS can serve as a routine diagnostic tool and enhance the diagnostic capabilities of radiologists for liver lesions.

Primary Surgery Followed by Selective Chemoradiotherapy Versus Preoperative Chemoradiotherapy Followed by Surgery for Locally Advanced Rectal Cancer: A Randomized Clinical Trial.

PURPOSE: The aim of this work was to determine whether locally advanced rectal cancer (LARC) with negative mesorectal fascia (MRF) predicted by magnetic resonance imaging (MRI) can be excluded from preoperative radiation therapy treatment. METHODS AND MATERIALS: This multicenter, open-label, non-inferiority, randomized clinical trial enrolled patients with LARC within 6 to 12 cm from the anal verge and with negative MRI-predicted MRF. Participants were randomized to the intervention group (primary surgery, in which the patients with positive pathologic [CRM] circumferential margins were subjected to chemoradiotherapy [CRT] and those with negative CRM underwent adjuvant chemotherapy according to pathologic staging) or the control group (preoperative CRT, in which all patients underwent subsequent surgery and adjuvant chemotherapy). The primary endpoint was 3-year disease-free survival (DFS). RESULTS: A total of 275 patients were randomly assigned to the intervention (n = 140) and control (n = 135) groups, in which 33.57% and 28.15% patients were at clinical T4 stage and 85.92% and 80.45% patients were at "bad" or "ugly" risk in the intervention and control groups, respectively. There were 2 patients (1.52%) and 1 patient (0.77%) with positive CRM in the intervention and control groups, respectively (P > .05). The non-adherence rates for the intervention and control groups were 3.6% and 23.7%, respectively. After a median follow-up of 34.6 months (IQR, 18.2-45.7), 43 patients had positive events (28 patients and 15 patients in the intervention and control groups, respectively). There were 6 patients (4.4%) with local recurrence in the intervention group and none in the control group, which led to the termination of the trial. The 3-year DFS rate was 81.82% in the intervention group (95% CI, 78.18%-85.46%) and 85.37% in the control group (95% CI, 81.75%-88.99%), with a difference of -3.55% (95% CI, -3.71% to -3.39%; hazard ratio, 1.76; 95% CI, 0.94-3.30). In the per-protocol data set, the difference between 3-year DFS rates was -5.44% (95% CI, -5.63% to -5.25%; hazard ratio, 2.02; 95% CI, 1.01-4.06). CONCLUSIONS: Based on the outcomes of this trial, in patients with LARC and MRI-negative MRF, primary surgery could negatively influence their DFS rates. Therefore, primary surgery was an inferior strategy compared with preoperative CRT followed by surgery and cannot be recommended for patients with LARC.