Chinese expert consensus statement on clinical diagnosis and treatment of malignant tumor bone metastasis and bone related diseases

Yu, Shiying; Jiang, Zefei; Zhang, Li; Niu, Xiaohui; Wang, Lvhua; Wu, Ning; Ma, Jin

doi:10.1007/s10330-009-0188-2

Cited by 4 publications

(1 citation statement)

References 30 publications

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“…The definitive identification of abnormal bone lesions is beneficial to proper personalized treatment and subserves the patients who were suffering from advanced malignant cancers [ 26 ]. However, the precise differentiation of suspicious bone lesions is still tricky based on the WBS images only [ 27 ].…”

Section: Discussionmentioning

confidence: 99%

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

Liu

Yang

et al. 2021

BMC Med Imaging

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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.

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Section: Discussionmentioning

confidence: 99%

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

Liu

Yang

et al. 2021

BMC Med Imaging

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Clinical application of OxyContin hydrochloride controlled release tablets in treatment of pain suffered from advanced cancer

Wang

Ouyang

et al. 2012

Chin. -Ger. J. Clin. Oncol.

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Analyses of the clinical characteristics of 49 cases of malignancy with multiple bone lesions as the first manifestation

Huang

Peng

Zhang

et al. 2021

Oncology and Translational Medicine

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ObjectiveTo investigate the clinical features of several cases of malignancy with multiple bone lesions as the first manifestation.MethodsForty-nine cases of malignancy with multiple bone lesions as the first manifestation were retrospectively analyzed from May 2018 to July 2019. All patients complained of "pain at the site of bone lesion" upon admission. Baseline patient information, such as age, gender, location of bone lesions, etiology, diagnosis method, time of onset was collected.ResultsThe median age of the patients was 56 years old, of which 83.7% (41/49) were aged ≥50 years. The median time of onset was 2 months. Among the cases, 40 were confirmed as solid tumor bone metastasis, whereas the remaining 9 cases as hematological system tumor. Lung cancer and multiple myeloma were the main tumor types, accounting for 40.8% (20/49) and 16.3% (8/49), respectively. Other common causes were seven cases of cancer affecting the digestive system (three cases of liver cancer, three cases of gastric cancer, and one case of esophageal cancer), as well as seven cases of unknown primary cancer. Half of the solid tumors had only multiple bone metastases but no other distant metastasis.ConclusionMultiple bone lesions accompanied by pain may be the first clinical manifestation in various malignant tumors. The common tumor types were lung cancer, multiple myeloma, and digestive system tumor. It is more common in people aged 50 years and older. Multiple bone lesions might be the only metastasis site of some solid tumors, and its mechanism needs further investigation.

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Chinese expert consensus statement on clinical diagnosis and treatment of malignant tumor bone metastasis and bone related diseases

Cited by 4 publications

References 30 publications

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

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

Clinical application of OxyContin hydrochloride controlled release tablets in treatment of pain suffered from advanced cancer

Analyses of the clinical characteristics of 49 cases of malignancy with multiple bone lesions as the first manifestation

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