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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. Primary outcome was the Female Sexual Function Index (FSFI). A generalised-mixed-effects model compared groups over time. Results: In total, 229 women were included (n = 112 intervention; n = 117 care-as-usual). No differences in FSFI total scores were found between groups at any timepoint (P = 0.37), with 12-month scores of 22.57 (intervention) versus 21.76 (care-as-usual). The intervention did not significantly improve dilator use, reduce sexual distress or vaginal symptoms compared to care-as-usual. At 12 months, both groups had minimal physician-reported vaginal stenosis; 70% of women were sexually active and reported no or mild vaginal symptoms. After radiotherapy and brachytherapy, 85% (intervention) versus 75% (care-as-usual) of participants reported dilation twice weekly. Discussion: Sexual rehabilitation for women treated with combined (chemo)radiotherapy and brachytherapy improved before and during the SPARC trial, which likely contributed to comparable study groups. Best practice involves a sexual rehabilitation appointment 1 month post-radiotherapy, including patient information, with dilator guidance, preferably by a trained nurse, and follow-up during the first year after treatment. Clinical trial registration: NCT03611517.

Objective: Continuation of smoking after a cancer diagnosis increases the burdensome side effects from cancer treatment, and decreases the chances of cure. Smoking cessation may improve oncological outcomes in cancer patients. This study aims to evaluate if radiation oncologists can be motivated by a smoking cessation awareness intervention to discuss smoking status more frequently and increase the referral rate for smoking cessation-support. Study design and Setting: A multifaceted approach was used to improve awareness: First, current practice was evaluated by a retrospective chart review of 282 patients referred for radiotherapy to ascertain smoking status, discussion of smoking cessation support and referral rates. Secondly, radiation oncologists were interviewed about their motives and barriers to discuss smoking status and smoking cessation support. The results were fed back in a teaching lecture to the doctors involved. Finally, the effect of this smoking cessation awareness intervention was prospectively evaluated in 100 patients. Results: After the smoking cessation awareness intervention, smoking cessation was more frequently discussed compared to baseline (77% (10/13)) and 39.5% (17/43) respectively. The referral rate for smoking cessation increased from 2.3% (1/43) to 69.2% (9/13) Conclusion: Without an active smoking prevention awareness policy, referral for smoking cessation support for cancer patients by radiation oncologists is low. A relatively short and simple smoking awareness intervention for radiation oncologist may result in a more frequent discussion with patients about smoking cessation and an even larger increase in referrals for smoking cessation support.

(1) Background: Over the past two decades use of new imaging modalities and the adaptation of applicators have allowed for advances in volumetric (3D) imaging-based brachytherapy practices for patients with locally advanced cervical cancer. The aim of this study was to compare the oncological outcome and toxicity for three consecutively introduced brachytherapy practices in a large single-center cohort; (2) Methods: Patients treated for cervical cancer with primary radiotherapy and curative intent were consecutively included in this retrospective, single-center cohort study from 2006 to 2019. This cohort was divided into three groups (CT, MRI, and MRI+needles) based on the timing of the introduction of a novel brachytherapy practice; 3D brachytherapy planning using CT- and MRI-guided adaptive brachytherapy and the use of parametrial interstitial needles, respectively. Actuarial estimates were compared between groups. Multivariable Cox regression analyses were performed to correct for other risk factors. Crude rates of severe (≥grade 3) late toxicity were reported; (3) Results: A total of 397 patients were included in this cohort. At a median follow-up of 40 months (interquartile range (IQR) 22–62), actuarial 3-year local control, pelvic control, disease-free survival, and overall survival for the entire cohort were 91% (95% (Confidence Interval (CI)) 88–94), 88% (95% CI 84–91), 69% (95% CI 64–74), and 75% (95% CI 70–79), respectively). Local control, disease-free survival, and overall survival were significantly improved in the MRI+needles group compared to the CT group (p = 0.040, p = 0.004, and p < 0.001, respectively). Independent risk factors for overall survival were treatment in either the CT or MRI group (vs. MRI+needles), older age at diagnosis, adeno (squamous) carcinoma, FIGO stage III/IV, and lymph node metastases. The crude rate of severe late toxicity was 27% in the CT, 26% in the MRI, and 20% in the MRI+needles group; (4) Conclusions: Prognosis i

Purpose: To compare transarterial chemoembolization delivered with drug eluting beads (TACE-DEB) with stereotactioc body radiation therapy (SBRT) in patients with hepatocellular carcinoma (HCC) in a multicenter randomized trial. Methods and Materials: Patients were included if they were eligible for TACE. They could also be recruited if they required treatment prior to liver transplantation. A maximum of four TACE-DEB procedures and ablation after incomplete TACE-DEB were both allowed. SBRT was delivered in six fractions of 8-9Gy. Primary end point was time to progression (TTP). Secondary endpoints were local control (LC), overall survival (OS), response rate (RR), toxicity, and quality of life (QoL). The calculated sample size was 100 patients. Results: Between May 2015 and April 2020, 30 patients were randomized to the study. Due to slow accrual the trial was closed prematurely. Two patients in the SBRT arm were considered ineligible leaving 16 patients in the TACE-DEB arm and 12 in the SBRT arm. Median follow-up was 28.1 months. Median TTP was 12 months for TACEDEB and 19 months for SBRT (p=0.15). Median LC was 12 months for TACE-DEB and >40 months (not reached) for SBRT (p=0.075). Median OS was 36.8 months for TACEDEB and 44.1 months for SBRT (p=0.36). A post-hoc analysis showed 100% for SBRT 1- and 2-year LC, and 54.4% and 43.6% for TACE-DEB (p=0.019). Both treatments resulted in RR>80%. Three episodes of possibly related toxicity grade ≥3 were observed after TACE-DEB. No episodes were observed after SBRT. QoL remained stable after both treatment arms. Conclusions: In this trial, TTP after TACE-DEB was not significantly improved by SBRT, while SBRT showed higher local antitumoral activity than TACE-DEB, without detrimental effects on OS, toxicity and QoL. To overcome poor accrual in randomized trials that include SBRT, and to generate evidence for including SBRT in treatment guidelines, international cooperation is needed.

Purpose: Deformable image registration (DIR) can benefit from additional guidance using corresponding landmarks in the images. However, the benefits thereof are largely understudied, especially due to the lack of automatic landmark detection methods for three-dimensional (3D) medical images. Approach: We present a deep convolutional neural network (DCNN), called DCNN-Match, that learns to predict landmark correspondences in 3D images in a self-supervised manner. We trained DCNN-Match on pairs of computed tomography (CT) scans containing simulated deformations. We explored five variants of DCNN-Match that use different loss functions and assessed their effect on the spatial density of predicted landmarks and the associated matching errors. We also tested DCNN-Match variants in combination with the open-source registration software Elastix to assess the impact of predicted landmarks in providing additional guidance to DIR. Results: We tested our approach on lower abdominal CT scans from cervical cancer patients: 121 pairs containing simulated deformations and 11 pairs demonstrating clinical deformations. The results showed significant improvement in DIR performance when landmark correspondences predicted by DCNN-Match were used in the case of simulated (p = 0e0) as well as clinical deformations (p = 0.030). We also observed that the spatial density of the automatic landmarks with respect to the underlying deformation affect the extent of improvement in DIR. Finally, DCNN-Match was found to generalize to magnetic resonance imaging scans without requiring retraining, indicating easy applicability to other datasets. Conclusions: DCNN-match learns to predict landmark correspondences in 3D medical images in a self-supervised manner, which can improve DIR performance.

Purpose: Deformable image registration (DIR) can benefit from additional guidance using corresponding landmarks in the images. However, the benefits thereof are largely understudied, especially due to the lack of automatic landmark detection methods for three-dimensional (3D) medical images. Approach: We present a deep convolutional neural network (DCNN), called DCNN-Match, that learns to predict landmark correspondences in 3D images in a self-supervised manner. We trained DCNN-Match on pairs of computed tomography (CT) scans containing simulated deformations. We explored five variants of DCNN-Match that use different loss functions and assessed their effect on the spatial density of predicted landmarks and the associated matching errors. We also tested DCNN-Match variants in combination with the open-source registration software Elastix to assess the impact of predicted landmarks in providing additional guidance to DIR. Results: We tested our approach on lower abdominal CT scans from cervical cancer patients: 121 pairs containing simulated deformations and 11 pairs demonstrating clinical deformations. The results showed significant improvement in DIR performance when landmark correspondences predicted by DCNN-Match were used in the case of simulated (p = 0e0) as well as clinical deformations (p = 0.030). We also observed that the spatial density of the automatic landmarks with respect to the underlying deformation affect the extent of improvement in DIR. Finally, DCNN-Match was found to generalize to magnetic resonance imaging scans without requiring retraining, indicating easy applicability to other datasets. Conclusions: DCNN-match learns to predict landmark correspondences in 3D medical images in a self-supervised manner, which can improve DIR performance., ense., Algorithmics