Baseline and early digital [18F]FDG PET/CT and multiparametric MRI contain promising features to predict response to neoadjuvant therapy in locally advanced rectal cancer patients: a pilot study

Objective In this pilot study, we investigated the feasibility of response prediction using digital [18F]FDG PET/computed tomography (CT) and multiparametric MRI before, during, and after neoadjuvant chemoradiation therapy in locally advanced rectal cancer (LARC) patients and aimed to select the most promising imaging modalities and timepoints for further investigation in a larger trial.Methods Rectal cancer patients scheduled to undergo neoadjuvant chemoradiation therapy were prospectively included in this trial, and underwent multiparametric MRI and [18F]FDG PET/CT before, 2 weeks into, and 6–8 weeks after chemoradiation therapy. Two groups were created based on pathological tumor regression grade, that is, good responders (TRG1-2) and poor responders (TRG3-5). Using binary logistic regression analysis with a cutoff value of P ≤ 0.2, promising predictive features for response were selected.Results Nineteen patients were included. Of these, 5 were good responders, and 14 were poor responders. Patient characteristics of these groups were similar at baseline. Fifty-seven features were extracted, of which 13 were found to be promising predictors of response. Baseline [T2: volume, diffusion-weighted imaging (DWI): apparent diffusion coefficient (ADC) mean, DWI: difference entropy], early response (T2: volume change, DWI: ADC mean change) and end-of-treatment presurgical evaluation MRI (T2: gray level nonuniformity, DWI: inverse difference normalized, DWI: gray level nonuniformity normalized), as well as baseline (metabolic tumor volume, total lesion glycolysis) and early response PET/CT (Δ maximum standardized uptake value, Δ peak standardized uptake value corrected for lean body mass), were promising features.Conclusion Both multiparametric MRI and [18F]FDG PET/CT contain promising imaging features to predict response to neoadjuvant chemoradiotherapy in LARC patients. A future larger trial should investigate baseline, early response, and end-of-treatment presurgical evaluation MRI and baseline and early response PET/CT.</div

Co-factors related to the causal relationship between humen papillimavirus and invasive cervical cancer in Honduras

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Human papillomavirus infection, cervical dysplasia and invasive cervical cancer in Honduras: a case-control study.

Item does not contain fulltextA substantial body of evidence has confirmed human papillomavirus (HPV) infection as the central etiological agents in human cervical carcinogenesis. In Honduras, cervical cancer is the most common cancer among women, with a high annual incidence. We conducted a population-based, case-control study of 229 patients with different grades of CIN and invasive cervical cancer and 438 matched controls. A structured questionnaire was used to investigate known and probable risk factors for cervical cancer. Cervical scrapes were tested for the presence of different HPV types using a general primer-mediated PCR followed by PCR-based sequencing. HPV DNA was detected in 87% of all cancer in situ and invasive cancer cases, and 95% of invasive cases could be attributed to high-risk types. In control women, 39% were positive for HPV DNA sequences. HPV 16 prevalence ranked highest in all stages of cervical dysplasias, invasive cancers and controls. A statistically significant association with HPV was observed for CIN II, CIN III and invasive cancer, showing an upward trend to more severe lesions and being more pronounced for HPV 16 and related types. The OR for HPV 16-and 18-related invasive cancer cases was 14.88 (95% CI 5.12-43.25) and 74.66 (95% CI 7.77-717.62), respectively. Our results confirm a central role of HPV as the cause of cervical cancer in Honduras and provide information as to the type distribution of HPVs in the country

Keeping your best options open with AI-based treatment planning in prostate and cervix brachytherapy

PURPOSE: Without a clear definition of an optimal treatment plan, no optimization model can be perfect. Therefore, instead of automatically finding a single “optimal” plan, finding multiple, yet different near-optimal plans, can be an insightful approach to support radiation oncologists in finding the plan they are looking for. METHODS AND MATERIALS: BRIGHT is a flexible AI-based optimization method for brachytherapy treatment planning that has already been shown capable of finding high-quality plans that trade-off target volume coverage and healthy tissue sparing. We leverage the flexibility of BRIGHT to find plans with similar dose-volume criteria, yet different dose distributions. We further describe extensions that facilitate fast plan adaptation should planning aims need to be adjusted, and straightforwardly allow incorporating hospital-specific aims besides standard protocols. RESULTS: Results are obtained for prostate (n=12) and cervix brachytherapy (n=36). We demonstrate the possible differences in dose distribution for optimized plans with equal dosevolume criteria. We furthermore demonstrate that adding hospital-specific aims enables adhering to hospital-specific practice while still being able to automatically create cervix plans that more often satisfy the EMBRACE-II protocol than clinical practice. Finally, we illustrate the feasibility of fast plan adaptation. CONCLUSIONS: Methods such as BRIGHT enable new ways to construct high-quality treatment plans for brachytherapy while offering new insights by making explicit the options one has. In particular, it becomes possible to present to radiation oncologists a manageable set of alternative plans that, from an optimization perspective are equally good, yet differ in terms of coverage-sparing trade-offs and shape of the dose distribution</p

Efficacy and toxicity of chemoradiation with image-guided adaptive brachytherapy for locally advanced cervical cancer

Experimentele farmacotherapi

Automated optimization for cervix brachytherapy requires more than the EMBRACE-II planning aims

Biological, physical and clinical aspects of cancer treatment with ionising radiatio

Automated optimization for cervix brachytherapy requires more than the EMBRACE-II planning aims

Biological, physical and clinical aspects of cancer treatment with ionising radiatio

Keeping your best options open with AI-based treatment planning in prostate and cervix brachytherapy

PURPOSE: Without a clear definition of an optimal treatment plan, no optimization model can be perfect. Therefore, instead of automatically finding a single “optimal” plan, finding multiple, yet different near-optimal plans, can be an insightful approach to support radiation oncologists in finding the plan they are looking for. METHODS AND MATERIALS: BRIGHT is a flexible AI-based optimization method for brachytherapy treatment planning that has already been shown capable of finding high-quality plans that trade-off target volume coverage and healthy tissue sparing. We leverage the flexibility of BRIGHT to find plans with similar dose-volume criteria, yet different dose distributions. We further describe extensions that facilitate fast plan adaptation should planning aims need to be adjusted, and straightforwardly allow incorporating hospital-specific aims besides standard protocols. RESULTS: Results are obtained for prostate (n = 12) and cervix brachytherapy (n = 36). We demonstrate the possible differences in dose distribution for optimized plans with equal dose-volume criteria. We furthermore demonstrate that adding hospital-specific aims enables adhering to hospital-specific practice while still being able to automatically create cervix plans that more often satisfy the EMBRACE-II protocol than clinical practice. Finally, we illustrate the feasibility of fast plan adaptation. CONCLUSIONS: Methods such as BRIGHT enable new ways to construct high-quality treatment plans for brachytherapy while offering new insights by making explicit the options one has. In particular, it becomes possible to present to radiation oncologists a manageable set of alternative plans that, from an optimization perspective are equally good, yet differ in terms of coverage-sparing trade-offs and shape of the dose distribution