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Purpose: Canadian radiation oncology professionals have a strong history of involvement in global oncology initiatives worldwide. This pan-Canadian survey-based study was conducted to determine the current level of engagement of Canadian radiation oncologists (ROs) and medical physicists (MPs) in global oncology initiatives and broaden the development of these activities., Materials and Methods: This was a cross-sectional study. The survey was designed to characterize current levels of engagement of Canadian ROs and MPs in global oncology initiatives. The survey was open from March 2019 to April 2020. It was disseminated to all Canadian Association of Radiation Oncology and Canadian Organization of Medical Physicists members with two subsequent email reminders., Results: Survey responses were received from 40 (93%) of the 43 Canadian cancer treatment centers that offer radiotherapy. At least one RO responded at 34 centers (79%) and one MP from 34 centers (79%) with some overlap. A response was received from a total of 93 participants, 47 ROs and 46 MPs. Of all survey participants, 58% reported some experience with global oncology. Nineteen percent of the participants surveyed were currently directly involved in short- or long-term projects, more than half of which have opportunity for additional staff involvement. The projects spanned 26 countries in South America, Africa, and Asia. Quality improvement and capacity building accounted for 27% and 20% of initiatives, respectively. The most common area of engagement was in direct treatment care, accounting for 56% of the projects., Conclusion: This study demonstrates the landscape of involvement of Canadian ROs and MPs in global oncology initiatives. The study also highlights areas of opportunity for broadening international participation and collaboration as it relates to global oncology for Canadian radiation oncology professionals.

Purpose: To report clinical and treatment characteristics, remission and failure patterns, and risk factors for local failure (LF) from the EMBRACE-I study., Materials and Methods: EMBRACE-I was a prospective, observational, multicenter cohort study on magnetic resonance imaging-based image-guided adaptive brachytherapy (MR-IGABT) in locally advanced cervical cancer. Treatment consisted of external beam radiotherapy, concurrent chemotherapy, and MR-IGABT. LF was defined as progressive or recurrent disease in the cervix, uterus, parametria, pelvic wall, or vagina. Competing risk analysis was used to estimate local tumor control (LC) and Cox proportional regression models for multivariable analysis and dose-response analysis., Results: One thousand three hundred eighteen patients with a median follow-up of 52 months were available for this analysis. Eighty-one patients had persistent disease 3 months after end of treatment. Of those, 60 patients achieved LC at 6-9 months without further treatment, whereas 21 patients had progressive disease. In addition, 77 patients developed a local recurrence after complete remission comprising a total number of 98 LFs. LFs were located inside the MR-IGABT target volumes in 90% of patients with LF. In multivariable analysis, histology, minimal dose to 90% of high-risk clinical target volume (CTV HR ), maximum tumor dimension, CTV HR > 45 cm 3 , overall treatment time, tumor necrosis on magnetic resonance imaging at diagnosis, uterine corpus infiltration at diagnosis and at MR-IGABT, and mesorectal infiltration at MR-IGABT had significant impact on LF. Dose-response analysis showed that a minimal dose to 90% of 85 Gy to the CTV HR led to 95% (95% CI, 94 to 97) LC 3 years postintervention for squamous cell in comparison to 86% (95% CI, 81 to 90) for adeno/adenosquamous carcinoma histology., Conclusion: The present study demonstrates the safety and validity of the GYN GEC-ESTRO/ICRU-89 target concept and provides large-scale evidence for dose prescription and new risk factors for LF in MR-IGABT in locally advanced cervical cancer., Competing Interests: Maximilian P. SchmidHonoraria: ElektaResearch Funding: Elekta (Inst), Varian Medical Systems (Inst)Patents, Royalties, Other Intellectual Property: Patent: US20170120072 Umesh MahantshettyConsulting or Advisory Role: AstraZenecaResearch Funding: Varian Medical Systems (Inst) Kari TanderupResearch Funding: Varian Medical Systems (Inst), Elekta (Inst) Peter HoskinResearch Funding: Varian Medical Systems (Inst), Astellas Pharma (Inst), Bayer (Inst), Roche (Inst), Pfizer (Inst), Elekta (Inst), Bristol Meyers (Inst) Bradley R. PietersConsulting or Advisory Role: BD Bard (Inst)Research Funding: ElektaTravel, Accommodations, Expenses: BD BardOther Relationship: Elekta (Inst) Remi NoutConsulting or Advisory Role: Merck KGaA (Inst)Research Funding: Varian Medical Systems (Inst), Elekta (Inst), Accuray (Inst) Nicole NesvacilHonoraria: ElektaConsulting or Advisory Role: ElektaTravel, Accommodations, Expenses: Elekta Richard PötterResearch Funding: Elekta (Inst), Varian Medical Systems (Inst) Christian KirisitsHonoraria: ElektaResearch Funding: Elekta (Inst), Varian Medical (Inst)Patents, Royalties, Other Intellectual Property: Patent WO2015181632A1 on Methods and Systems for Brachytherapy Planning Based on Imaging DataNo other potential conflicts of interest were reported.

This novel deep-learning (DL) algorithm addresses the challenging task of predicting uterine shape and location when deformed from its natural anatomy by the presence of an intrauterine (tandem)/intravaginal (ring) applicator during brachytherapy (BT) treatment for locally advanced cervical cancer. Paired pelvic MRI datasets from 92 subjects, acquired without (pre-BT) and with (at-BT) applicators, were used. We propose a novel automated algorithm to segment the uterus in pre-BT MR images using a deep convolutional neural network (CNN) incorporated with autoencoders. The proposed neural net is based on a pre-trained CNN Inception V4 architecture. It predicts a compressed vector by applying a multi-layer autoencoder, which is then back-projected into the segmentation contour of the uterus. Following this, another transfer learning approach using a modified U-net model is employed to predict the at-BT uterus shape from pre-BT MRI. The complex and large deformations of the uterus are quantified using free form deformation method. The proposed algorithm yielded an average Dice Coefficient (DC) of 94.1±3.3 and an average Hausdorff Distance (HD) of 4.0±3.1 mm compared to the manually defined ground truth by expert clinicians. Further, the modified U-net prediction of the at-BT uterus resulted in a DC accuracy of 88.1±3.8 and HD of 5.8±3.6 mm. The mean uterine surface point-to-point displacement was 25.0 [10.0-62.5] mm from the pre-BT position. Our unique DL method can thus successfully predict tandem-deformed uterine shape and position from MR images taken before the BT implant procedure i.e. without the applicator in place. Clinical relevance-The proposed DL-based framework can be incorporated as an automatic prediction tool of uterine deformation due to applicator insertion for personalized BT treatments. It holds promise for more streamlined clinical/technical decision-making before BT applicator insertion resulting in improved dosimetric outcomes.

Estrogen is thought to cause proliferation of all estrogen receptor positive (ER+) breast cancers. Paradoxically, in the Women's Health Initiative Trial, estrogen-only hormone replacement therapy reduced the incidence and mortality of low grade, ER+, HER2- breast cancer. We gave estradiol to 19 post-menopausal women with newly diagnosed low-grade, ER+, HER2- breast cancer in a prospective window of opportunity clinical trial and examined the changes in proliferation and gene expression before and after estradiol treatment. Ki67 decreased in 13/19 (68%) patients and 8/13 (62%) showed a decrease in Risk of Recurrence Score. We chose three prototypical estrogen responders (greatest decrease in ROR) and non-responders (no/minimal change in ROR) and applied a differential gene expression analysis to develop pre-treatment (PRESTO-30 core ) and post-treatment (PRESTO-45 surg ) gene expression profiles. The PRESTO-30 core predicted adjuvant benefit in a published series of tamoxifen, the partial estrogen agonist. Of the 45 genes in the PRESTO-45 surg , thirty contain the Cell cycle genes Homology Region (CHR) motif that binds the class B multi-vulva complex (MuvB) a member of the DREAM (Dimerization partner, retinoblastoma-like proteins, E2F, MuvB) complex responsible for reversible cell cycle arrest or quiescence. There was also near uniform suppression (89%) of the remaining DREAM genes consistent with estrogen induced activation of the DREAM complex to mediate cell cycle block after a short course of estrogens. To our knowledge, this is the first report to show estrogen modulation of DREAM genes and suggest involvement of DREAM pathway associated quiescence in endocrine responsive post-menopausal ER+ breast cancers., Competing Interests: The authors declare no conflict of interest., (© 2021 The Author(s).)