Early palliative radiation versus observation for high-risk asymptomatic or minimally symptomatic bone metastases: study protocol for a randomized controlled trial

An Update on the Management of Bone Metastases

PURPOSE OF REVIEW

Increasing life expectancy among patients with advanced cancer has placed a greater emphasis on optimizing pain control and quality of life. Concurrently, significant advancements in radiotherapy for bone metastases have permitted for dose escalation strategies such as stereotactic radiotherapy. This review aims to provide updated information on the management of bone metastases in light of these developments.

RECENT FINDINGS

We reviewed recent studies regarding the role and details of external beam radiotherapy for bone metastases, with emphasis on differences by treatment site as well as intention (palliative versus ablative for oligometastases). Conventional palliative radiotherapy remains a mainstay of management. While stereotactic radiotherapy may augment durability of pain relief and even survival time, there are significant questions remaining regarding optimal dosing and patient selection. Radiotherapy for bone metastases continues to evolve, particularly with increasing use of stereotactic radiotherapy. Future studies are needed to clarify optimal dose, fractionation, modality, and patient selection criteria among different radiotherapy approaches.

Prophylactic Radiation Therapy Versus Standard of Care for Patients With High-Risk Asymptomatic Bone Metastases: A Multicenter, Randomized Phase II Clinical Trial

PURPOSE

External-beam radiation therapy (RT) is standard of care (SOC) for pain relief of symptomatic bone metastases. We aimed to evaluate the efficacy of radiation to asymptomatic bone metastases in preventing skeletal-related events (SRE).

METHODS

In a multicenter randomized controlled trial, adult patients with widely metastatic solid tumor malignancies were stratified by histology and planned SOC (systemic therapy or observation) and randomly assigned in a 1:1 ratio to receive RT to asymptomatic high-risk bone metastases or SOC alone. The primary outcome of the trial was SRE. Secondary outcomes included hospitalizations for SRE and overall survival (OS).

RESULTS

A total of 78 patients with 122 high-risk bone metastases were enrolled between May 8, 2018, and August 9, 2021, at three institutions across an affiliated cancer network in the United States. Seventy-three patients were evaluable for the primary end point. The most common primary cancer types were lung (27%), breast (24%), and prostate (22%). At 1 year, SRE occurred in one of 62 bone metastases (1.6%) in the RT arm and 14 of 49 bone metastases (29%) in the SOC arm (P < .001). There were significantly fewer patients hospitalized for SRE in the RT arm compared with the SOC arm (0 v 4, P = .045). At a median follow-up of 2.5 years, OS was significantly longer in the RT arm (hazard ratio [HR], 0.49; 95% CI, 0.27 to 0.89; P = .018), which persisted on multivariable Cox regression analysis (HR, 0.46; 95% CI, 0.23 to 0.85; P = .01).

CONCLUSION

Radiation delivered prophylactically to asymptomatic, high-risk bone metastases reduced SRE and hospitalizations. We also observed an improvement in OS with prophylactic radiation, although a confirmatory phase III trial is warranted.

Automatic identification of suspicious bone metastatic lesions in bone scintigraphy using convolutional neural network

Background

We aimed to construct an artificial intelligence (AI) guided identification of suspicious bone metastatic lesions from the whole-body bone scintigraphy (WBS) images by convolutional neural networks (CNNs).

Methods

We retrospectively collected the ^99mTc-MDP WBS images with confirmed bone lesions from 3352 patients with malignancy. 14,972 bone lesions were delineated manually by physicians and annotated as benign and malignant. The lesion-based differentiating performance of the proposed network was evaluated by fivefold cross validation, and compared with the other three popular CNN architectures for medical imaging. The average sensitivity, specificity, accuracy and the area under receiver operating characteristic curve (AUC) were calculated. To delve the outcomes of this study, we conducted subgroup analyses, including lesion burden number and tumor type for the classifying ability of the CNN.

Results

In the fivefold cross validation, our proposed network reached the best average accuracy (81.23%) in identifying suspicious bone lesions compared with InceptionV3 (80.61%), VGG16 (81.13%) and DenseNet169 (76.71%). Additionally, the CNN model's lesion-based average sensitivity and specificity were 81.30% and 81.14%, respectively. Based on the lesion burden numbers of each image, the area under the receiver operating characteristic curve (AUC) was 0.847 in the few group (lesion number n ≤ 3), 0.838 in the medium group (n = 4–6), and 0.862 in the extensive group (n > 6). For the three major primary tumor types, the CNN-based lesion identifying AUC value was 0.870 for lung cancer, 0.900 for prostate cancer, and 0.899 for breast cancer.

Conclusion

The CNN model suggests potential in identifying suspicious benign and malignant bone lesions from whole-body bone scintigraphic images.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12880-021-00662-9.