Cost-effectiveness of a nurse-led sexual rehabilitation intervention for women treated with radiotherapy for gynaecological cancer in a randomized trial.

Purpose: To compare the cost-effectiveness of a nurse-led sexual rehabilitation intervention with standard care in women treated with external beam radiotherapy, with or without brachytherapy, for gynaecological cancers.

Methods: Eligible women were randomly assigned to the intervention (n = 112) or standard care (n = 117). Primary endpoint was sexual functioning at 12-months post-radiotherapy, assessed by the Female Sexual Function Index (FSFI).

Efficacy of a nurse-led sexual rehabilitation intervention for women with gynaecological cancers receiving radiotherapy: results of a randomised trial.

Background: The multicentre randomised SPARC trial evaluated the efficacy of a nurse-led sexual rehabilitation intervention on sexual functioning, distress, dilator use, and vaginal symptoms after radiotherapy for gynaecological cancers.

Methods: Eligible women were randomised to the rehabilitation intervention or care-as-usual. Four intervention sessions were scheduled over 12 months, with concurrent validated questionnaires and clinical assessments.

A unified strategy to focal brachytherapy incorporating transperineal biopsy, image fusion, and real-time implantation with and without rectal spacer simulated in prostate phantoms.

Purpose: To develop an approach to the diagnosis and treatment of prostate cancer using one platform for fusion biopsy, followed by focal gland ablation utilizing permanent prostate brachytherapy with and without a rectal spacer.

Material And Methods: Prostate phantoms containing multiparametric magnetic resonance imaging (mpMRI) regions of interest (ROI) underwent fusion biopsy, followed by image co-registration of positive sites to a treatment planning brachytherapy program. A partial hemi-ablation and both posterior lobes using a Mick applicator and linked stranded seeds were simulated.

Prediction of recurrence risk in endometrial cancer with multimodal deep learning.

Predicting distant recurrence of endometrial cancer (EC) is crucial for personalized adjuvant treatment. The current gold standard of combined pathological and molecular profiling is costly, hampering implementation. Here we developed HECTOR (histopathology-based endometrial cancer tailored outcome risk), a multimodal deep learning prognostic model using hematoxylin and eosin-stained, whole-slide images and tumor stage as input, on 2,072 patients from eight EC cohorts including the PORTEC-1/-2/-3 randomized trials.