Survival prediction in patients with gynecological cancer irradiated for brain metastases.

BACKGROUND AND PURPOSE: This large population-based, retrospective, single-center study aimed to identify prognostic factors in patients with brain metastases (BM) from gynecological cancers. MATERIAL AND METHODS: One hundred and forty four patients with BM from gynecological cancer treated with radiotherapy (RT) were identified. Primary cancer diagnosis, age, performance status, number of BM, presence of extracranial disease, and type of BM treatment were assessed. Overall survival (OS) was calculated using the Kaplan-Meier method and the Cox proportional hazards regression model was used for multivariable analysis. A prognostic index (PI) was developed based on scores from independent predictors of OS. RESULTS: Median OS for the entire study population was 6.2 months. Forty per cent of patients died within 3 months after start of RT. Primary cancer with the origin in cervix or vulva (p = 0.001),  Eastern Cooperative Oncology Group (ECOG) 3-4 (p < 0.001), and the presence of extracranial disease (p = 0.001) were associated with significantly shorter OS. The developed PI based on these factors, categorized patients into three risk groups with a median OS of 13.5, 4.0, and 2.4 months for the good, intermediate, and poor prognosis group, respectively. INTERPRETATION: Patients with BM from gynecological cancers carry a poor prognosis. We identified prognostic factors and developed a scoring tool to select patients with better or worse prognosis. Patients in the high-risk group have a particular poor prognosis, and omission of RT could be considered.

MRI-guided dynamic risk assessment in cervical cancer based on tumor hypoxia at diagnosis and volume response at brachytherapy.

BACKGROUND AND PURPOSE: Improvements in treatment outcome for patients with locally advanced cervical cancer (LACC) require a better classification of patients according to their risk of recurrence. We investigated whether an imaging-based approach, combining pretreatment hypoxia and tumor response during therapy, could improve risk classification. MATERIAL AND METHODS: Ninety-three LACC patients with T2-weigthed (T2W)-, dynamic contrast enhanced (DCE)- and diffusion weighted (DW)-magnetic resonance (MR) images acquired before treatment, and T2W- and, for 64 patients, DW-MR images, acquired at brachytherapy, were collected. Pretreatment hypoxic fraction (HFpre) was determined from DCE- and DW-MR images using the consumption and supply-based hypoxia (CSH)-imaging method. Volume regression at brachytherapy was assessed from T2W-MR images and combined with HFpre. In 17 patients with adequate DW-MR images at brachytherapy, the apparent diffusion coefficient (ADC), reflecting tumor cell density, was calculated. Change in ADC during therapy was combined with volume regression yielding functional regression as explorative response measure. Endpoint was disease free survival (DFS). RESULTS: HFpre was the strongest predictor of DFS, but a significant correlation with outcome was found also for volume regression. The combination of HFpre and volume regression showed a stronger association with DFS than HFpre alone. Patients with disease recurrence were selected to either the intermediate- or high-risk group with a 100 % accuracy. Functional regression showed a stronger correlation to HFpre than volume regression. CONCLUSION: The combination of pretreatment hypoxia and volume regression at brachytherapy improved patient risk classification. Integration of ADC with volume regression showed promise as a new tumor response parameter.

Targeted Therapy on the Screen: Do We Hit the Target?

A phase II trial, investigating whether the antidiabetic drug metformin could reduce hypoxia in cervical cancer, used imaging to preselect patients and test the biological hypothesis behind the drug effect. This trial design would be of importance for the implementation of targeted treatment in the clinic. See related article by Han et al., p. 5263.

Risk of recurrence after chemoradiotherapy identified by multimodal MRI and 18F-FDG-PET/CT in locally advanced cervical cancer.

BACKGROUND AND PURPOSE: MRI, applying dynamic contrast-enhanced (DCE) and diffusion-weighted (DW) sequences, and 18F-fluorodeoxyglucose (18F-FDG) PET/CT provide information about tumor aggressiveness that is unexploited in treatment of locally advanced cervical cancer (LACC). We investigated the potential of a multimodal combination of imaging parameters for classifying patients according to their risk of recurrence. MATERIALS AND METHODS: Eighty-two LACC patients with diagnostic MRI and FDG-PET/CT, treated with chemoradiotherapy, were collected. Thirty-eight patients with MRI only were included for validation of MRI results. Endpoints were survival (disease-free, cancer-specific, overall) and tumor control (local, locoregional, distant). Ktrans, reflecting vascular function, apparent diffusion coefficient (ADC), reflecting cellularity, and standardized uptake value (SUV), reflecting glucose uptake, were extracted from DCE-MR, DW-MR and FDG-PET images, respectively. By applying an oxygen consumption and supply-based method, ADC and Ktrans parametric maps were voxel-wise combined into hypoxia images that were used to determine hypoxic fraction (HF). RESULTS: HF showed a stronger association with outcome than the single modality parameters. This association was confirmed in the validation cohort. Low HF identified low-risk patients with 95% precision. Based on the 50th SUV-percentile (SUV50), patients with high HF were divided into an intermediate- and high-risk group with high and low SUV50, respectively. This defined a multimodality biomarker, HF/SUV50. HF/SUV50 increased the precision of detecting high-risk patients from 41% (HF alone) to 57% and showed prognostic significance in multivariable analysis for all endpoints. CONCLUSION: Multimodal combination of MR- and FDG-PET/CT-images improves classification of LACC patients compared to single modality images and clinical factors.