Risk factors for maternal complications following uterine rupture: a 12-year single-center experience.

PURPOSE: To determine maternal outcomes and risk factors for composite maternal morbidity following uterine rupture during pregnancy. METHODS: A retrospective cohort study including all women diagnosed with uterine rupture during pregnancy, between 2011 and 2023, at a single-center. Patients with partial uterine rupture or dehiscence were excluded. We compared women who had composite maternal morbidity following uterine rupture to those without. Composite maternal morbidity was defined as any of the following: maternal death; hysterectomy; severe postpartum hemorrhage; disseminated intravascular coagulation; injury to adjacent organs; admission to the intensive care unit; or the need for relaparotomy. The primary outcome was risk factors associated with composite maternal morbidity following uterine rupture. The secondary outcome was the incidence of maternal and neonatal complications following uterine rupture. RESULTS: During the study period, 147,037 women delivered. Of them, 120 were diagnosed with uterine rupture. Among these, 44 (36.7%) had composite maternal morbidity. There were no cases of maternal death and two cases of neonatal death (1.7%); packed cell transfusion was the major contributor to maternal morbidity [occurring in 36 patients (30%)]. Patients with composite maternal morbidity, compared to those without, were characterized by: increased maternal age (34.7 vs. 32.8 years, p = 0.03); lower gestational age at delivery (35 + 5 vs. 38 + 1 weeks, p = 0.01); a higher rate of unscarred uteri (22.7% vs. 2.6%, p < 0.01); and rupture occurring outside the lower uterine segment (52.3% vs. 10.5%, p < 0.01). CONCLUSION: Uterine rupture entails increased risk for several adverse maternal outcomes, though possibly more favorable than previously described. Numerous risk factors for composite maternal morbidity following rupture exist and should be carefully assessed in these patients.

Perinatal outcomes following uterine rupture during a trial of labor after cesarean: A 12-year single-center experience.

OBJECTIVE: To determine perinatal outcomes following uterine rupture during a trial of labor after one previous cesarean delivery (CD) at term. METHODS: A retrospective single-center study examining perinatal outcomes in women with term singleton pregnancies with one prior CD, who underwent a trial of labor after cesarean (TOLAC) and were diagnosed with uterine rupture, between 2011 and 2022. The primary outcome was a composite maternal outcome, and the secondary outcome was a composite neonatal outcome. Additionally, we compared perinatal outcomes between patients receiving oxytocin during labor with those who did not. RESULTS: Overall, 6873 women attempted a TOLAC, and 116 were diagnosed with uterine rupture. Among them, 63 (54.3%) met the inclusion criteria, and 18 (28%) had the maternal composite outcome, with no cases of maternal death. Sixteen cases (25.4%) had the composite neonatal outcome, with one case (1.6%) of perinatal death. No differences were noted between women receiving oxytocin and those not receiving oxytocin in the rates of maternal composite (35.7% vs 26.5%, P = 0.502, respectively) or neonatal composite outcomes (21.4% vs 26.5%, P = 0.699). CONCLUSION: Uterine rupture during a TOLAC entails increased risk for myriad adverse outcomes for the mother and neonate, though possibly more favorable than previously described. Oxytocin use does not affect these risks.

Predicting EGFR mutation status by a deep learning approach in patients with non-small cell lung cancer brain metastases.

PURPOSE: Non-small cell lung cancer (NSCLC) tends to metastasize to the brain. Between 10 and 60% of NSCLCs harbor an activating mutation in the epidermal growth-factor receptor (EGFR), which may be targeted with selective EGFR inhibitors. However, due to a high discordance rate between the molecular profile of the primary tumor and the brain metastases (BMs), identifying an individual patient's EGFR status of the BMs necessitates tissue diagnosis via an invasive surgical procedure. We employed a deep learning (DL) method with the aim of noninvasive detection of the EGFR mutation status in NSCLC BM. METHODS: We retrospectively collected clinical, radiological, and pathological-molecular data of all the NSCLC patients who had been diagnosed with BMs and underwent resection of their BM during 2009-2019. The study population was then divided into two groups based upon EGFR mutational status. We further employed a DL technique to classify the two groups according to their preoperative magnetic resonance imaging features. Augmentation techniques, transfer learning approach, and post-processing of the predicted results were applied to overcome the relatively small cohort. Finally, we established the accuracy of our model in predicting EGFR mutation status of BM of NSCLC. RESULTS: Fifty-nine patients were included in the study, 16 patients harbored EGFR mutations. Our model predicted mutational status with mean accuracy of 89.8%, sensitivity of 68.7%, specificity of 97.7%, and a receiver operating characteristic curve value of 0.91 across the 5 validation datasets. CONCLUSION: DL-based noninvasive molecular characterization is feasible, has high accuracy and should be further validated in large prospective cohorts.

Differentiating Small-Cell Lung Cancer From Non-Small-Cell Lung Cancer Brain Metastases Based on MRI Using Efficientnet and Transfer Learning Approach.

Differentiation between small-cell lung cancer (SCLC) from non-small-cell lung cancer (NSCLC) brain metastases is crucial due to the different clinical behaviors of the two tumor types. We propose the use of a deep learning and transfer learning approach based on conventional magnetic resonance imaging (MRI) for non-invasive classification of SCLC vs. NSCLC brain metastases. Sixty-nine patients with brain metastasis of lung cancer origin were included. Of them, 44 patients had NSCLC and 25 patients had SCLC. Classification was performed with EfficientNet architecture on crop images of lesion areas and based on post-contrast T1-weighted, T2-weighted and FLAIR imaging input data. Evaluation of the model was carried out in a 5-fold cross-validation manner, and based on accuracy, precision, recall, F1 score and area under the receiver operating characteristic curve. The best classification results were obtained with multiparametric MRI input data (T1WI+c+FLAIR+T2WI), with a mean overall accuracy of 0.90 ± 0.04, and F1 score of 0.92 ± 0.05 for NSCLC and 0.87 ± 0.08 for SCLC for the validation data and an accuracy of 0.87 ± 0.05, with an F1 score of 0.88 ± 0.05 for NSCLC and 0.85 ± 0.05 for SCLC for the test dataset. The proposed method provides an automatic noninvasive method for the classification of brain metastasis with high sensitivity and specificity for differentiation between NSCLC vs. SCLC brain metastases. It may be used as a diagnostic tool for improving decision-making in the treatment of patients with these metastases. Further studies on larger patient samples are required to validate the current results.