The IMRiS Trial: A Phase II Study of Intensity-Modulated Radiotherapy in Extremity Soft Tissue Sarcoma.

PURPOSE: Primary soft tissue sarcoma (STS) is rare, with many tumours occurring in extremities. Local management is limb-sparing surgery and pre-operative/post-operative radiotherapy (RT) for patients at high risk of local recurrence. We prospectively investigated late normal tissue toxicity and limb function observed after intensity modulated RT (IMRT) in extremity STS. METHODS AND MATERIALS: Patients with extremity STS, age ≥16 years. Two treatment cohorts: IMRT 50Gy in 25 × 2Gy fractions (pre-operative) or 60/66Gy in 30/33 × 2Gy fractions (post-operative). Primary endpoint was rate of ≥ grade 2 late subcutaneous fibrosis at 24 months after IMRT (RTOG late radiation morbidity scoring). RESULTS: One hundred and sixty-eight patients were registered between March 2016-July 2017. Of those, 159 (95%) received IMRT (106, 67% pre-operative RT and 53, 33% post-operative RT) with a median follow-up of 35.2 months (IQR: 32.9 to 36.6); 62% male; median age 58 years. Of 111 patients assessable for primary endpoint at 24 months, 12 (10.8%, 95%CI: 5.7%-18.1%) had ≥ grade 2 subcutaneous fibrosis. The overall rate at 24 months of RTOG late skin, bone and joint toxicity was 7/112 (6.3%), 3/112 (2.7%) and 10/113 (8.8%), respectively, and for Stern's scale oedema was 6/113 (5.3%). More wound complications were observed with pre-operative than post-operative RT (29.2% vs 3.8%). Overall survival at 24 months was 84.6%, and local recurrence event rate at 24 months was 10%. CONCLUSIONS: The rate of ≥ grade 2 subcutaneous fibrosis at 24 months after IMRT was 10.8%, consistent with other recent trials of IMRT, and lower than historical reported rates in patients treated with 3D-CRT. This trial provides further evidence for the benefits of IMRT in this patient population.

Clinically Applicable Segmentation of Head and Neck Anatomy for Radiotherapy: Deep Learning Algorithm Development and Validation Study.

BACKGROUND: Over half a million individuals are diagnosed with head and neck cancer each year globally. Radiotherapy is an important curative treatment for this disease, but it requires manual time to delineate radiosensitive organs at risk. This planning process can delay treatment while also introducing interoperator variability, resulting in downstream radiation dose differences. Although auto-segmentation algorithms offer a potentially time-saving solution, the challenges in defining, quantifying, and achieving expert performance remain. OBJECTIVE: Adopting a deep learning approach, we aim to demonstrate a 3D U-Net architecture that achieves expert-level performance in delineating 21 distinct head and neck organs at risk commonly segmented in clinical practice. METHODS: The model was trained on a data set of 663 deidentified computed tomography scans acquired in routine clinical practice and with both segmentations taken from clinical practice and segmentations created by experienced radiographers as part of this research, all in accordance with consensus organ at risk definitions. RESULTS: We demonstrated the model's clinical applicability by assessing its performance on a test set of 21 computed tomography scans from clinical practice, each with 21 organs at risk segmented by 2 independent experts. We also introduced surface Dice similarity coefficient, a new metric for the comparison of organ delineation, to quantify the deviation between organ at risk surface contours rather than volumes, better reflecting the clinical task of correcting errors in automated organ segmentations. The model's generalizability was then demonstrated on 2 distinct open-source data sets, reflecting different centers and countries to model training. CONCLUSIONS: Deep learning is an effective and clinically applicable technique for the segmentation of the head and neck anatomy for radiotherapy. With appropriate validation studies and regulatory approvals, this system could improve the efficiency, consistency, and safety of radiotherapy pathways.

Clinical Outcomes of a Randomized Trial of Adaptive Plan-of-the-Day Treatment in Patients Receiving Ultra-hypofractionated Weekly Radiation Therapy for Bladder Cancer.

PURPOSE: Hypofractionated radiation therapy can be used to treat patients with muscle-invasive bladder cancer unable to have radical therapy. Toxicity is a key concern, but adaptive plan-of the day (POD) image-guided radiation therapy delivery could improve outcomes by minimizing the volume of normal tissue irradiated. The HYBRID trial assessed the multicenter implementation, safety, and efficacy of this strategy. METHODS: HYBRID is a Phase II randomized trial that was conducted at 14 UK hospitals. Patients with T2-T4aN0M0 muscle-invasive bladder cancer unsuitable for radical therapy received 36 Gy in 6 weekly fractions, randomized (1:1) to standard planning (SP) or adaptive planning (AP) using a minimization algorithm. For AP, a pretreatment cone beam computed tomography (CT) was used to select the POD from 3 plans (small, medium, and large). Follow-up included standard cystoscopic, radiologic, and clinical assessments. The primary endpoint was nongenitourinary Common Terminology Criteria for Adverse Events (CTCAE) grade ≥ 3 (≥G3) toxicity within 3 months of radiation therapy. A noncomparative single stage design aimed to exclude ≥30% toxicity rate in each planning group in patients who received ≥1 fraction of radiation therapy. Local control at 3-months (both groups combined) was a key secondary endpoint. RESULTS: Between April 15, 2014, and August 10, 2016, 65 patients were enrolled (SP, n = 32; AP, n = 33). The median follow-up time was 38.8 months (interquartile range [IQR], 36.8-51.3). The median age was 85 years (IQR, 81-89); 68% of participants (44 of 65) were male; and 98% of participants had grade 3 urothelial cancer. In 63 evaluable participants, CTCAE ≥G3 nongenitourinary toxicity rates were 6% (2 of 33; 95% confidence interval [CI], 0.7%-20.2%) for the AP group and 13% (4 of 30; 95% CI, 3.8%-30.7%) for the SP group. Disease was present in 9/48 participants assessed at 3 months, giving a local control rate of 81.3% (95% CI, 67.4%-91.1%). CONCLUSIONS: POD adaptive radiation therapy was successfully implemented across multiple centers. Weekly ultrahypofractionated 36 Gy/6 fraction radiation therapy is safe and provides good local control rates in this older patient population.

Assessment of the dosimetric accuracies of CATPhan 504 and CIRS 062 using kV-CBCT for performing direct calculations.

The dosimetric accuracies of CATPhan 504 and CIRS 062 have been evaluated using the kV-CBCT of Varian TrueBeam linac and Eclipse TPS. The assessment was done using the kV-CBCT as a standalone tool for dosimetric calculations towards Adaptive replanning. Dosimetric calculations were made without altering the HU-ED curves of the planning computed tomography (CT) scanner that is used by the Eclipse TPS. All computations were done using the images and dataset from kV-CBCT while maintaining the HU-ED calibration curve of the planning CT (pCT), assuming pCT was used for the initial treatment plan. Results showed that the CIRS phantom produces doses within ±5% of the CT-based plan while CATPhan 504 produces a variation of ±14% of the CT-based plan.