Weibull parametric model for survival analysis in women with endometrial cancer using clinical and T2-weighted MRI radiomic features.

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.

The Hammersmith Score Optimises Patient Selection and Predicts for Overall Survival in Early-Phase Cancer Trial Participants Independent of Tumour Burden.

INTRODUCTION: As tumour response rates are increasingly demonstrated in early-phase cancer trials (EPCT), optimal patient selection and accurate prognostication are paramount. Hammersmith Score (HS), a simple prognostic index derived on routine biochemical measures (albumin <35 g/L, lactate dehydrogenase >450 IU/L, sodium <135 mmol/L), is a validated predictor of response and survival in EPCT participants. HS has not been validated in the cancer immunotherapy era. METHODS: We retrospectively analysed characteristics and outcomes of unselected referrals to our early-phase unit (12/2019-12/2022). Independent predictors for overall survival (OS) were identified from univariable and multivariable models. HS was calculated for 66 eligible trial participants and compared with the Royal Marsden Score (RMS) to predict OS. Multivariable logistic regression and C-index was used to compare predictive ability of prognostic models. RESULTS: Of 212 referrals, 147 patients were screened and 82 patients treated in EPCT. Prognostic stratification by HS identifies significant difference in median OS, and HS was confirmed as a multivariable predictor for OS (HR: HS 1 vs. 0 2.51, 95% CI: 1.01-6.24, p = 0.049; HS 2/3 vs. 0: 10.32, 95% CI: 2.15-49.62, p = 0.004; C-index 0.771) with superior multivariable predictive ability than RMS (HR: RMS 2 vs. 0/1 5.46, 95% CI: 1.12-26.57, p = 0.036; RMS 3 vs. 0/1 6.83, 95% CI: 1.15-40.53, p < 0.001; C-index 0.743). CONCLUSIONS: HS is a validated prognostic index for patients with advanced cancer treated in the context of modern EPCTs, independent of tumour burden. HS is a simple, inexpensive prognostic tool to optimise referral for EPCT.

An Integrated Clinical-MR Radiomics Model to Estimate Survival Time in Patients With Endometrial Cancer.

BACKGROUND: Determination of survival time in women with endometrial cancer using clinical features remains imprecise. Features from MRI may improve the survival estimation allowing improved treatment planning. PURPOSE: To identify clinical features and imaging signatures on T2-weighted MRI that can be used in an integrated model to estimate survival time for endometrial cancer subjects. STUDY TYPE: Retrospective. POPULATION: Four hundred thirteen patients with endometrial cancer as training (N = 330, 66.41 ± 11.42 years) and validation (N = 83, 67.60 ± 11.89 years) data and an independent set of 82 subjects as testing data (63.26 ± 12.38 years). FIELD STRENGTH/SEQUENCE: 1.5-T and 3-T scanners with sagittal T2-weighted spin echo sequence. ASSESSMENT: Tumor regions were manually segmented on T2-weighted images. Features were extracted from segmented masks, and clinical variables including age, cancer histologic grade and risk score were included in a Cox proportional hazards (CPH) model. A group least absolute shrinkage and selection operator method was implemented to determine the model from the training and validation datasets. STATISTICAL TESTS: A likelihood-ratio test and decision curve analysis were applied to compare the models. Concordance index (CI) and area under the receiver operating characteristic curves (AUCs) were calculated to assess the model. RESULTS: Three radiomic features (two image intensity and volume features) and two clinical variables (age and cancer grade) were selected as predictors in the integrated model. The CI was 0.797 for the clinical model (includes clinical variables only) and 0.818 for the integrated model using training and validation datasets, the associated mean AUC value was 0.805 and 0.853. Using the testing dataset, the CI was 0.792 and 0.882, significantly different and the mean AUC was 0.624 and 0.727 for the clinical model and integrated model, respectively. DATA CONCLUSION: The proposed CPH model with radiomic signatures may serve as a tool to improve estimated survival time in women with endometrial cancer. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY: Stage 2.

Surgery for Recurrent Uterine Cancer: Surgical Outcomes and Implications for Survival-A Case Series.

OBJECTIVE: The purpose of this study was to describe the patterns of relapse in uterine cancer (UC) and the role of surgery in the recurrent setting. METHODS: We 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. RESULTS: We 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). CONCLUSIONS: Surgery 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.

Post-Radiation Therapy Imaging Appearances in Cervical Carcinoma.

Locally advanced and node-positive cervical cancers are usually treated with external beam radiation therapy and intracavitary brachytherapy with concomitant chemotherapy. In patients with locally advanced cervical cancer, imaging plays a vital role in pretreatment planning, assessment of primary tumor response to treatment, follow-up, and evaluation of treatment-related complications. Radiation therapy planning is crucial to successful local and regional control of disease. Patient selection criteria for radiation therapy with concomitant chemotherapy are described, as is assessment of treatment response of the primary cervical tumor at magnetic resonance (MR) imaging. Image interpretation can be challenging because of radiation therapy-related changes in the pelvic organs. Expected changes in the bladder, bowel, and bone marrow after radiation therapy are described, and multimodality imaging findings at computed tomography, MR imaging, and fluorine 18 fluorodeoxyglucose positron emission tomography are illustrated. Complications after radiation therapy have declined over recent years because of targeted radiation therapy. These complications can be divided into acute and chronic effects, where acute toxic effects occur within weeks of treatment. Chronic complications include cervical stenosis, small bowel stricture, fistula formation, and insufficiency fractures. Imaging is an essential tool in the care of patients with cervical cancer treated with chemotherapy and radiation therapy. The reporting radiologist should be familiar with the expected imaging appearances of the pelvic organs after radiation therapy, as well as potential complications, to avoid pitfalls in image interpretation.

Personalized Prehabilitation Improves Tolerance to Chemotherapy in Patients with Oesophageal Cancer.

BACKGROUND: Prehabilitation programmes aim to optimise patients before and after cancer treatment including surgery. Previous studies in surgical patients demonstrate that prehabilitation improves pre-operative fitness and overcomes the negative impact of neoadjuvant chemotherapy on fitness. The aim of this study was to assess the impact of prehabilitation on the tolerance of neoadjuvant chemotherapy in patients with oesophageal cancer. METHODS: Patients with oesophageal or gastroesophageal junction (GOJ) cancer from two oncology centres were retrospectively included in the present comparative cohort study; one provided a multimodal prehabilitation programme and one did not offer any prehabilitation. Tolerance of chemotherapy, defined as completion of the full chemotherapy regime as per protocol, was compared between the two groups. RESULTS: In terms of participants, 92 patients were included in this study, 47 patients in the prehabilitation cohort and 45 in the control cohort. Compared with the control group, the prehabilitation group demonstrated an improved rate of chemotherapy completion (p = 0.029). In multivariate analysis, participation in prehabilitation was significantly associated with an improved rate of chemotherapy completion. CONCLUSION: The findings of this exploratory study suggest that prehabilitation is associated with better tolerance for chemotherapy. Further research is needed to establish the long-term impact of prehabilitation on oncological outcomes.

Prediction of Deep Myometrial Infiltration, Clinical Risk Category, Histological Type, and Lymphovascular Space Invasion in Women with Endometrial Cancer Based on Clinical and T2-Weighted MRI Radiomic Features.

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.

Disseminated cancer in pregnancy: krukenberg tumour.

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.