Influence of the Different Primary Cancers and Different Types of Bone Metastasis on the Lesion-based Artificial Neural Network Value Calculated by a Computer-aided Diagnostic System,BONENAVI, on Bone Scintigraphy Images

Document Type : Original Article

Authors

1 Department of Diagnostic Radiology Sapporo Medical University

2 Department of Clinical Radiology Graduated School of Medical Sciences Kyushul University

3 Department of Clinical Radiology Graduated School of Medical Sciences Kyushu University

4 Department of Health Sciences Graduate School of Medical Sciences Kyushu University

5 Department of Clinical Radiology Graduate School of Medical Sciences Kyushu University

Abstract

Objective(s): BONENAVI, a computer-aided diagnostic system, is used in bone scintigraphy. This system provides the artificial neural network (ANN) and bone scan index (BSI) values. ANN is associated with the possibility of bone metastasis, while BSI is related to the amount of bone metastasis. The degree of uptake on bone scintigraphy can be affected by the type of bone metastasis. Therefore, the ANN value provided by BONENAVI may be influenced by the characteristics of bone metastasis. In this study, we aimed to assess the relationship between ANN value and characteristics of bone metastasis.
Methods: We analyzed 50 patients (36 males, 14 females; age range: 42–87 yrs, median age: 72.5 yrs) with prostate, breast, or lung cancer who had undergone bone scintigraphy and were diagnosed with bone metastasis (32 cases of prostate cancer, nine cases of breast cancer, and nine cases of lung cancer). Those who had received systematic therapy over the past years were excluded. Bone metastases were diagnosed clinically, and the type of bone metastasis (osteoblastic, mildly osteoblastic,osteolytic, and mixed components) was decided visually by the agreement of two radiologists. We compared the ANN values (case-based and lesion-based) among the three primary cancers and four types of bone metastasis.
Results: There was no significant difference in case-based ANN values among prostate, breast, and lung cancers. However, the lesion-based ANN values were the highest in cases with prostate cancer and the lowest in cases of lung cancer (median values: prostate cancer, 0.980; breast cancer, 0.909; and lung cancer, 0.864). Mildly osteoblastic lesions showed significantly lower ANN values than the other three types of bone metastasis (median values: osteoblastic, 0.939; mildly osteoblastic, 0.788; mixed type, 0.991; and osteolytic, 0.969). The possibility of a lesion-based ANN value below 0.5 was 10.9% for bone metastasis in prostate cancer, 12.9% for breast cancer, and 37.2% for lung cancer. The corresponding possibility were 14.7% for osteoblastic metastases, 23.9% for mildly osteoblastic metastases, 7.14% for mixedtype metastases, and 16.0% for osteolytic metastases.
Conclusion: The lesion-based ANN values calculated by BONENAVI can be influenced by the type of primary cancer and bone metastasis.

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References

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Asia Oceania Journal of Nuclear Medicine and Biology
Volume 5, Issue 1
January 2017
Pages 49-55

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