What MRI-based tumor size measurement is best for predicting long-term survival in uterine cervical cancer?

Background: Tumor size assessment by MRI is central for staging uterine cervical cancer. However, the optimal role of MRI-derived tumor measurements for prognostication is still unclear. Material and methods: This retrospective cohort study included 416 women (median age: 43 years) diagnosed with cervical cancer during 2002–2017 who underwent pretreatment pelvic MRI. The MRIs were independently read by three radiologists, measuring maximum tumor diameters in three orthogonal planes and maximum diameter irrespective of plane (MAXimaging). Inter-reader agreement for tumor size measurements was assessed by intraclass correlation coefficients (ICCs). Size was analyzed in relation to age, International Federation of Gynecology and Obstetrics (FIGO) (2018) stage, histopathological markers, and disease-specific survival using Kaplan–Meier-, Cox regression-, and time-dependent receiver operating characteristics (tdROC) analyses. Results: All MRI tumor size variables (cm) yielded high areas under the tdROC curves (AUCs) for predicting survival (AUC 0.81–0.84) at 5 years after diagnosis and predicted outcome (hazard ratios [HRs] of 1.42–1.76, p < 0.001 for all). Only MAXimaging independently predicted survival (HR = 1.51, p = 0.03) in the model including all size variables. The optimal cutoff for maximum tumor diameter (≥ 4.0 cm) yielded sensitivity (specificity) of 83% (73%) for predicting disease-specific death after 5 years. Inter-reader agreement for MRI-based primary tumor size measurements was excellent, with ICCs of 0.83–0.85. Conclusion: Among all MRI-derived tumor size measurements, MAXimaging was the only independent predictor of survival. MAXimaging ≥ 4.0 cm represents the optimal cutoff for predicting long-term disease-specific survival in cervical cancer. Inter-reader agreement for MRI-based tumor size measurements was excellent.publishedVersio

Computer-Aided Detection of Pathologically Enlarged Lymph Nodes On Non-Contrast CT In Cervical Cancer Patients For Low-Resource Settings

The mortality rate of cervical cancer is approximately 266,000 people each year, and 70% of the burden occurs in Low- and Middle- Income Countries (LMICs). Radiation therapy is the primary modality for treatment of locally advanced cervical cancer cases. In the absence of high quality diagnostic imaging needed to identify nodal metastasis, many LMIC sites treat standard pelvic fields, failing to include node metastasis outside of the field and/or to boost lymph nodes in the abdomen and pelvis. The first goal of this project was to create a program which automatically identifies positive cervical cancer lymph nodes on non-contrast daily CT images, which are widely available in LMICs(1). A region of interest which is likely to contain the nodal volumes relevant for cervical cancer was defined on a single patient CT(2). This region was deformed onto new patients using an in-house, demons-based deformation software. Edge detection and erosion filtering were used to distinguish potential positive nodes from normal structures. Regions on adjacent slices were then connected into a potential nodal 3D-structure. To differentiate these 3D structures from normal tissues, eighty-six features were generated based on the shape and mean pixel values of the structures, and four classification ensemble methods were tested to differentiate the positive nodes from normal tissues. A cohort of fifty-eight MD Anderson cervical cancer patients with pathologically enlarged lymph nodes were used as a training-test set. Similarly, twenty MD Anderson cervical cancer patients were obtained as a validation set. They contained 154 and 35 pathologically enlarged lymph nodes, respectively. Model comparison led to the selection of the Adaboost ensemble model, utilizing 17 features. In the validation set, 60% of the clinically significant positive cervical cancer nodes were identified along with a false/true positive ratio of ~4:1. The entire process takes approximately 10/number-of-cores-minutes. Our findings demonstrated that our computer-aided detection model can assist in the identification of metastatic nodal disease where high quality diagnostic imaging is not readily available. By identifying these nodes, radiation treatment fields can be modified to include pathologically enlarged lymph nodes, which is an essential element to providing potentially curative radiotherapy for cervical cancer

A review of segmentation and deformable registration methods applied to adaptive cervical cancer radiation therapy treatment planning

Objective: Manual contouring and registration for radiotherapy treatment planning and online adaptation for cervical cancer radiation therapy in computed tomography (CT) and magnetic resonance images (MRI) are often necessary. However manual intervention is time consuming and may suffer from inter or intra-rater variability. In recent years a number of computer-guided automatic or semi-automatic segmentation and registration methods have been proposed. Segmentation and registration in CT and MRI for this purpose is a challenging task due to soft tissue deformation, inter-patient shape and appearance variation and anatomical changes over the course of treatment. The objective of this work is to provide a state-of-the-art review of computer-aided methods developed for adaptive treatment planning and radiation therapy planning for cervical cancer radiation therapy. Methods: Segmentation and registration methods published with the goal of cervical cancer treatment planning and adaptation have been identified from the literature (PubMed and Google Scholar). A comprehensive description of each method is provided. Similarities and differences of these methods are highlighted and the strengths and weaknesses of these methods are discussed. A discussion about choice of an appropriate method for a given modality is provided. Results: In the reviewed papers a Dice similarity coefficient of around 0.85 along with mean absolute surface distance of 2-4. mm for the clinically treated volume were reported for transfer of contours from planning day to the treatment day. Conclusions: Most segmentation and non-rigid registration methods have been primarily designed for adaptive re-planning for the transfer of contours from planning day to the treatment day. The use of shape priors significantly improved segmentation and registration accuracy compared to other models

Cervical nodal volume for prognostication and risk stratification of patients with nasopharyngeal carcinoma, and implications on the TNM-staging system

published_or_final_versio

UWOMJ Volume 76, No. 1

Schulich School of Medicine & Dentistryhttps://ir.lib.uwo.ca/uwomj/1080/thumbnail.jp

High thymidylate synthase gene expression predicts poor outcome after resection of hepatocellular carcinoma.

IntroductionPrognosis after resection of hepatocellular carcinoma (HCC) is highly variable. Compared to clinicopathologic factors, the use of molecular markers to predict outcome has not been well studied. We investigated the prognostic importance of thymidylate synthase (TS) gene expression and polymorphisms in patients after resection of HCC.MethodsPatients who underwent complete resection of HCC for whom tissue was available were identified. TS gene expression level and polymorphisms were determined in HCC specimens. Prognostic factors were evaluated using Kaplan-Meier curves and Cox proportional hazard models.ResultsThe study included 67 patients. In univariate analysis, variables that negatively influenced survival included TNM stage, microvascular invasion, and high TS expression. For the high TS expression group, median survival was 54 months and 5-year actuarial survival was 47%. For the low TS expression group, median survival was not reached and the 5-year actuarial survival was 91%. In multivariate analysis, only high TS expression remained an independent predictor of poor survival (HR = 10.77, 95% CI 1.36-84.91; P = 0.02). TS gene polymorphisms were not associated with TS expression or overall survival.ConclusionsHigh TS expression predicts poor outcome after resection of HCC. Molecular markers might be robust predictors of patient outcome after resection of HCC

Focal Spot, Spring 1999

https://digitalcommons.wustl.edu/focal_spot_archives/1081/thumbnail.jp