Feasibility of ADC histogram analysis for predicting of postoperative recurrence in aggressive spinal tumors
Background: Risk stratification of spinal tumors is a major unmet clinical need for personalized therapy. Purpose: To explore the feasibility of pretreatment whole-lesion apparent diffusion coefficient (ADC) histogram in predicting local recurrence of aggressive spinal tumors. Methods: 119 aggressiv...
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Elsevier
2025-04-01
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| Series: | Journal of Bone Oncology |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2212137425000077 |
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| author | Qizheng Wang Yongye Chen Guangjin Zhou Tongyu Wang Jingchao Fang Ke Liu Siyuan Qin Weili Zhao Dapeng Hao Ning Lang |
| author_facet | Qizheng Wang Yongye Chen Guangjin Zhou Tongyu Wang Jingchao Fang Ke Liu Siyuan Qin Weili Zhao Dapeng Hao Ning Lang |
| author_sort | Qizheng Wang |
| collection | DOAJ |
| description | Background: Risk stratification of spinal tumors is a major unmet clinical need for personalized therapy. Purpose: To explore the feasibility of pretreatment whole-lesion apparent diffusion coefficient (ADC) histogram in predicting local recurrence of aggressive spinal tumors. Methods: 119 aggressive spinal tumor patients (median age, 40; range, 13–74 years) confirmed by pathological findings with a mean follow-up of 36 months were enrolled and divided into the recurrence and non-recurrence group. The histogram metrics of whole-lesion, including the maximum, mean, kurtosis, skewness, entropy, and percentiles (10th, 25th, 50th, 75th, 95th) ADC values, were evaluated and take the average. Fractal dimension (FD) was assessed in the three orthogonal directions and take maximum. Clinical and general imaging features were used to construct an alternative prognostic model for comparison. Variables with statistical differences would be included in stepwise logistic regression analysis. Results: As for the clinical model, Enneking staging (odds ratio [OR]: 3.572; P = 0.04) and vertebral compression (OR: 4.302; P = 0.002) were independent predictors of recurrence. There was no statistical difference in FD between the two groups (P = 0.623). Among the ADC histogram parameters compared, skewness, maximum, and mean ADC values were independent risk factors and constructed ADC histogram prediction models. The ADC histogram model (AUC = 0.871) and the combined model (AUC = 0.884) performed better than the clinical prediction model (AUC = 0.704) with P-values of 0.004 and 0.001, respectively. Conclusion: Prediction models based on the ADC histogram analysis might represent serviceable instruments for the aggressive spinal tumors. |
| format | Article |
| id | doaj-art-2aea8c3ac8ae4d3ebd7a58a0dea0bc68 |
| institution | DOAJ |
| issn | 2212-1374 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Bone Oncology |
| spelling | doaj-art-2aea8c3ac8ae4d3ebd7a58a0dea0bc682025-08-20T02:50:53ZengElsevierJournal of Bone Oncology2212-13742025-04-015110066610.1016/j.jbo.2025.100666Feasibility of ADC histogram analysis for predicting of postoperative recurrence in aggressive spinal tumorsQizheng Wang0Yongye Chen1Guangjin Zhou2Tongyu Wang3Jingchao Fang4Ke Liu5Siyuan Qin6Weili Zhao7Dapeng Hao8Ning Lang9Department of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District 100069 Beijing, PR ChinaDepartment of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District 100069 Beijing, PR ChinaDepartment of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District 100069 Beijing, PR ChinaDepartment of Radiology, the Affiliated Hospital of Qingdao University, No. 16 Jiangsu Rd, Qingdao 266000 Shandong, PR ChinaDepartment of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District 100069 Beijing, PR ChinaDepartment of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District 100069 Beijing, PR ChinaDepartment of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District 100069 Beijing, PR ChinaDepartment of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District 100069 Beijing, PR ChinaDepartment of Radiology, the Affiliated Hospital of Qingdao University, No. 16 Jiangsu Rd, Qingdao 266000 Shandong, PR ChinaDepartment of Radiology, Peking University Third Hospital, 49 North Garden Road, Haidian District 100069 Beijing, PR China; Corresponding author at: Peking University Third Hospital, Department of Radiology, 49 North Garden Road, Haidian District, Beijing 100191, PR China.Background: Risk stratification of spinal tumors is a major unmet clinical need for personalized therapy. Purpose: To explore the feasibility of pretreatment whole-lesion apparent diffusion coefficient (ADC) histogram in predicting local recurrence of aggressive spinal tumors. Methods: 119 aggressive spinal tumor patients (median age, 40; range, 13–74 years) confirmed by pathological findings with a mean follow-up of 36 months were enrolled and divided into the recurrence and non-recurrence group. The histogram metrics of whole-lesion, including the maximum, mean, kurtosis, skewness, entropy, and percentiles (10th, 25th, 50th, 75th, 95th) ADC values, were evaluated and take the average. Fractal dimension (FD) was assessed in the three orthogonal directions and take maximum. Clinical and general imaging features were used to construct an alternative prognostic model for comparison. Variables with statistical differences would be included in stepwise logistic regression analysis. Results: As for the clinical model, Enneking staging (odds ratio [OR]: 3.572; P = 0.04) and vertebral compression (OR: 4.302; P = 0.002) were independent predictors of recurrence. There was no statistical difference in FD between the two groups (P = 0.623). Among the ADC histogram parameters compared, skewness, maximum, and mean ADC values were independent risk factors and constructed ADC histogram prediction models. The ADC histogram model (AUC = 0.871) and the combined model (AUC = 0.884) performed better than the clinical prediction model (AUC = 0.704) with P-values of 0.004 and 0.001, respectively. Conclusion: Prediction models based on the ADC histogram analysis might represent serviceable instruments for the aggressive spinal tumors.http://www.sciencedirect.com/science/article/pii/S2212137425000077Diffusion weighted imagingSpinal tumorRecurrenceHistogram analysis |
| spellingShingle | Qizheng Wang Yongye Chen Guangjin Zhou Tongyu Wang Jingchao Fang Ke Liu Siyuan Qin Weili Zhao Dapeng Hao Ning Lang Feasibility of ADC histogram analysis for predicting of postoperative recurrence in aggressive spinal tumors Journal of Bone Oncology Diffusion weighted imaging Spinal tumor Recurrence Histogram analysis |
| title | Feasibility of ADC histogram analysis for predicting of postoperative recurrence in aggressive spinal tumors |
| title_full | Feasibility of ADC histogram analysis for predicting of postoperative recurrence in aggressive spinal tumors |
| title_fullStr | Feasibility of ADC histogram analysis for predicting of postoperative recurrence in aggressive spinal tumors |
| title_full_unstemmed | Feasibility of ADC histogram analysis for predicting of postoperative recurrence in aggressive spinal tumors |
| title_short | Feasibility of ADC histogram analysis for predicting of postoperative recurrence in aggressive spinal tumors |
| title_sort | feasibility of adc histogram analysis for predicting of postoperative recurrence in aggressive spinal tumors |
| topic | Diffusion weighted imaging Spinal tumor Recurrence Histogram analysis |
| url | http://www.sciencedirect.com/science/article/pii/S2212137425000077 |
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