Joint Department of Physics, The Institute of Cancer Research, London, UK.
The CRUK Cancer Imaging Centre, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK.
Int J Hyperthermia. 2020;37(1):1033-1045. doi: 10.1080/02656736.2020.1812736.
Patient suitability for magnetic resonance-guided high intensity focused ultrasound (MRgHIFU) ablation of pelvic tumors is initially evaluated clinically for treatment feasibility using referral images, acquired using standard supine diagnostic imaging, followed by MR screening of potential patients lying on the MRgHIFU couch in a 'best-guess' treatment position. Existing evaluation methods result in ≥40% of referred patients being screened out because of tumor non-targetability. We hypothesize that this process could be improved by development of a novel algorithm for predicting tumor coverage from referral imaging.
The algorithm was developed from volunteer images and tested with patient data. MR images were acquired for five healthy volunteers and five patients with recurrent gynaecological cancer. Subjects were MR imaged supine and in oblique-supine-decubitus MRgHIFU treatment positions. Body outline and bones were segmented for all subjects, with organs-at-risk and tumors also segmented for patients. Supine images were aligned with treatment images to simulate a treatment dataset. Target coverage (of patient tumors and volunteer intra-pelvic soft tissue), i.e. the volume reachable by the MRgHIFU focus, was quantified. Target coverage predicted from supine imaging was compared to that from treatment imaging.
Mean (±standard deviation) absolute difference between supine-predicted and treatment-predicted coverage for 5 volunteers was 9 ± 6% (range: 2-22%) and for 4 patients, was 12 ± 7% (range: 4-21%), excluding a patient with poor acoustic coupling (coverage difference was 53%).
Prediction of MRgHIFU target coverage from referral imaging appears feasible, facilitating further development of automated evaluation of patient suitability for MRgHIFU.
最初，在临床上使用参考图像来评估患者是否适合接受磁共振引导高强度聚焦超声（MRgHIFU）消融盆腔肿瘤，这些图像是使用标准仰卧诊断成像获得的，然后对潜在患者进行 MR 筛查，让他们躺在 MRgHIFU 治疗台上的“最佳猜测”治疗位置。现有的评估方法导致>40%的转介患者因肿瘤不可靶向性而被筛选掉。我们假设可以通过开发一种新的算法来预测从转诊图像获得的肿瘤覆盖率来改进这一过程。
该算法是从志愿者的图像中开发出来的，并在患者的数据中进行了测试。为五名健康志愿者和五名患有复发性妇科癌症的患者采集了 MR 图像。受检者仰卧位和斜仰卧位-MRgHIFU 治疗位进行 MR 成像。对所有受检者进行了身体轮廓和骨骼分割，对患者进行了危险器官和肿瘤的分割。将仰卧位图像与治疗图像对齐，以模拟治疗数据集。对患者肿瘤和志愿者盆腔内软组织的目标覆盖（即 MRgHIFU 焦点可到达的体积）进行了量化。将仰卧位成像预测的目标覆盖与治疗成像预测的目标覆盖进行了比较。
5 名志愿者的仰卧预测和治疗预测覆盖之间的平均（±标准偏差）绝对差值为 9±6%（范围：2-22%），4 名患者的平均差值为 12±7%（范围：4-21%），排除了一个因声耦合不良而导致的患者（覆盖差异为 53%）。
从转诊成像预测 MRgHIFU 目标覆盖的方法似乎是可行的，这有助于进一步开发自动化评估患者接受 MRgHIFU 治疗的适用性。
