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Background Semiparametric survival analysis such as the Cox proportional hazards (CPH) regression model is commonly employed in endometrial cancer (EC) study. Although this method does not need to know the baseline hazard function, it cannot estimate event time ratio (ETR) which measures relative increase or decrease in survival time. To estimate ETR, the Weibull parametric model needs to be applied. The objective of this study is to develop and evaluate the Weibull parametric model for EC patients’ survival analysis. Methods Training ( n  = 411) and testing ( n  = 80) datasets from EC patients were retrospectively collected to investigate this problem. To determine the optimal CPH model from the training dataset, a bi-level model selection with minimax concave penalty was applied to select clinical and radiomic features which were obtained from T2-weighted MRI images. After the CPH model was built, model diagnostic was carried out to evaluate the proportional hazard assumption with Schoenfeld test. Survival data were fitted into a Weibull model and hazard ratio (HR) and ETR were calculated from the model. Brier score and time-dependent area under the receiver operating characteristic curve (AUC) were compared between CPH and Weibull models. Goodness of the fit was measured with Kolmogorov-Smirnov (KS) statistic. Results Although the proportional hazard assumption holds for fitting EC survival data, the linearity of the model assumption is suspicious as there are trends in the age and cancer grade predictors. The result also showed that there was a significant relation between the EC survival data and the Weibull distribution. Finally, it showed that Weibull model has a larger AUC value than CPH model in general, and it also has smaller Brier score value for EC survival prediction using both training and testing datasets, suggesting that it is more accurate to use the Weibull model for EC survival analysis. Conclusions The Weibull parametric model for EC survival analysis allows simultaneous characterization of the treatment effect in terms of the hazard ratio and the event time ratio (ETR), which is likely to be better understood. This method can be extended to study progression free survival and disease specific survival. Trial registration ClinicalTrials.gov NCT03543215, https://clinicaltrials.gov/ , date of registration: 30th June 2017.

Locally advanced cervical cancer is treated with chemoradiotherapy (standard of care), but many patients still relapse and die from metastatic disease. We investigated chemoradiotherapy with or without induction chemotherapy to determine whether induction chemotherapy improves both progression-free survival and overall survival. The INTERLACE trial was a multicentre, randomised phase 3 trial done at 32 medical centres in Brazil, India, Italy, Mexico, and the UK. Adults (aged ≥18 years) with locally advanced cervical cancer (FIGO 2008 stage IB1 disease with nodal involvement, or stage IB2, IIA, IIB, IIIB, or IVA disease) were randomly assigned (1:1), by minimisation, using a central electronic system, to standard cisplatin-based chemoradiotherapy (once-a-week intravenous cisplatin 40 mg/m2 for 5 weeks with 45·0–50·4 Gy external beam radiotherapy delivered in 20–28 fractions plus brachytherapy to achieve a minimum total 2 Gy equivalent dose of 78–86 Gy) alone or induction chemotherapy (once-a-week intravenous carboplatin area under the receiver operator curve 2 and paclitaxel 80 mg/m2 for 6 weeks) followed by standard cisplatin-based chemoradiotherapy. Stratification factors were recruiting site, stage, nodal status, three-dimensional conformal radiotherapy or intensity modulated radiotherapy, age, tumour size, and histology (squamous vs non-squamous). Primary endpoints were progression-free survival and overall survival within the intention-to-treat population. This trial is registered with ClinicalTrials.gov, NCT01566240, and EUDRACT, 2011-001300-35. Between Nov 8, 2012, and Nov 17, 2022, 500 eligible patients were enrolled and randomly assigned to the chemoradiotherapy alone group (n=250) or the induction chemotherapy with chemoradiotherapy group. Of 500 patients, 354 (70%) had stage IIB disease and 56 (11%) stage IIIB disease. Pelvic lymph nodes were positive in 215 (43%) patients. 230 (92%) patients who received induction chemotherapy had at least five cycles. Median interval between induction chemotherapy and chemoradiotherapy was 7 days. Four or more cycles of cisplatin were given to 212 (85%) participants in the induction chemotherapy with chemoradiotherapy group and to 224 (90%) of participants in the chemoradiotherapy alone group. 462 (92%) participants received external beam radiotherapy and brachytherapy with a median overall treatment time of 45 days. After a median follow-up of 67 months, 5-year progression-free survival rates were 72% in the induction chemotherapy with chemoradiotherapy group and 64% in the chemoradiotherapy alone group with a hazard ratio (HR) of 0·65 (95% CI 0·46–0·91, p=0·013). 5-year overall survival rates were 80% in the induction chemotherapy with chemoradiotherapy group and 72% in the chemoradiotherapy alone group, with an HR of 0·60 (95% CI 0·40–0·91, p=0·015). Grade 3 or greater adverse events were reported in 147 (59%) of 250 individuals in the induction chemotherapy with chemoradiotherapy group versus 120 (48%) of 250 individuals in the chemoradiotherapy alone group. Short-course induction chemotherapy followed by chemoradiotherapy significantly improves survival of patients with locally advanced cervical cancer. Cancer Research UK and University College London–University College London Hospitals Biomedical Research Centre.

Purpose: To predict deep myometrial infiltration (DMI), clinical risk category, histological type, and lymphovascular space invasion (LVSI) in women with endometrial cancer using machine learning classification methods based on clinical and image signatures from T2-weighted MR images. Methods: A training dataset containing 413 patients and an independent testing dataset consisting of 82 cases were employed in this retrospective study. Manual segmentation of the whole tumor volume on sagittal T2-weighted MRI was performed. Clinical and radiomic features were extracted to predict: (i) DMI of endometrial cancer patients, (ii) endometrial cancer clinical high-risk level, (iii) histological subtype of tumor, and (iv) presence of LVSI. A classification model with different automatically selected hyperparameter values was created. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve, F1 score, average recall, and average precision were calculated to evaluate different models. Results: Based on the independent external testing dataset, the AUCs for DMI, high-risk endometrial cancer, endometrial histological type, and LVSI classification were 0.79, 0.82, 0.91, and 0.85, respectively. The corresponding 95% confidence intervals (CI) of the AUCs were [0.69, 0.89], [0.75, 0.91], [0.83, 0.97], and [0.77, 0.93], respectively. Conclusion: It is possible to classify endometrial cancer DMI, risk, histology type, and LVSI using different machine learning methods.

ObjectiveThe purpose of this study was to describe the patterns of relapse in uterine cancer (UC) and the role of surgery in the recurrent setting.MethodsWe describe surgical and clinical outcomes of all patients who underwent surgery for recurrent UC in a gynecological oncology tertiary referral center between May 1, 2013, and April 30, 2016. Progression-free survival and overall survival were estimated using Kaplan-Meier methods with the surgery at relapse being the starting point.ResultsWe evaluated 15 patients with a median age of 66 years. The predominant histology was the endometrioid variant (n = 11; 73.3%). The median interval between the end of previous treatment and relapse surgery was 24 months (range, 8–164). Locoregional pelvic recurrences were the most common type of recurrence (n = 13; 86.7%) with the para-aortic lymph node space being the most commonly affected extrapelvic site (13%). Patients predominantly presented with a multifocal pattern of relapse (n = 10; 66.7%) requiring multivisceral resections such as bowel (n = 7; 46.6%) and/or bladder/ureteric resections (n = 8; 53.3%) to achieve complete tumor clearance. All patients were operated tumor free with a 30-day major morbidity and mortality rate of 6.7% and 0%, respectively. Five patients (33.3%) received postoperative chemotherapy or radiotherapy. Five patients (33.3%) relapsed, and 3 died within a mean follow-up of 12.4 months (95% confidence interval [CI], 6.5–18.2). Two of those patients had a sarcoma.Mean progression-free survival and overall survival for the entire cohort postrelapse surgery was 21.7 months (95%CI, 13.9–29.5) and 26.0 months (95%CI, 18.4–33.7), respectively. Survival was significantly worse in patients with nonendometrioid histology (P < 0.0001).ConclusionsSurgery for UC relapse seems feasible with acceptable morbidity and high complete resection rates despite the multifocal patterns of relapse in a selected group of patients in a reference center for gynecological cancers. Larger scale studies are warranted to establish the value of surgery at relapse for UC.

Disseminated cancer presenting in pregnancy is extremely rare and its presentation can easily be obscured by pregnancy and physiological changes that occur with it. This case describes a patient who was diagnosed incidentally following investigations for preeclampsia. Despite initially being thought to be of low risk, the final diagnosis was that of metastasis from a primary cancer of an unknown origin, most likely a gastric adenocarcinoma. Although patients are under thorough observation throughout their pregnancy, this case highlights the potential need for additional investigations or adjustment of current practices. It also draws attention to the lack of sufficient reporting of cancer in pregnancy, which, considering its rarity, greatly influences how patients are managed.