Response assessment to neoadjuvant therapy in soft tissue sarcomas: using CT texture analysis in comparison to tumor size, density, and perfusion
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| 005 | 20210128100515.0 | ||
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| 008 | 210128e20150801xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s00261-014-0318-3 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s00261-014-0318-3 | ||
| 245 | 0 | 0 | |a Response assessment to neoadjuvant therapy in soft tissue sarcomas: using CT texture analysis in comparison to tumor size, density, and perfusion |h [Elektronische Daten] |c [Fang Tian, Koichi Hayano, Avinash Kambadakone, Dushyant Sahani] |
| 520 | 3 | |a Purpose: To evaluate the role of computed tomography (CT) texture analysis in assessing response of soft tissue sarcoma (STS) treated with neoadjuvant bevacizumab (BVZ) plus radiotherapy in comparison to tumor size, density, and perfusion. Methods: In the phase II clinical trial, 20 patients with STSs received BVZ alone for 2weeks followed by BVZ plus radiotherapy for 6weeks prior to surgery. All patients received CT perfusion at baseline, 2 and 8weeks after the therapy, and tumor blood flow (BF) was measured. In contrast enhanced CT image at the arterial peak enhancement time, mean of positive pixels (MPP) was measured as a texture parameter using texture analysis software, and tumor size and density were also measured. The percent changes of these parameters were compared with pathological response on surgical specimen. Results: After 2weeks of the therapy, MPP and BF decreased by 10.42% and 20.08%, while changes of tumor size and density were not obvious. After 8weeks, MPP, BF, and density decreased by 29.2% (p=0.03), 53.2% (p=0.001), and 30.41% (p=0.005), respectively, without a significant change in size. The percent change of MPP after 8weeks had a significant correlation with tumor necrosis in surgical specimen (r=−0.801, p<0.001), whereas those of size, density, and BF did not. The receiver-operating characteristic analysis demonstrated that the percent change of MPP<−35.36% was an optimal cut-off value to differentiate pathological responders. Conclusion: The change of MPP is the best biomarker for the treatment response in STS. | |
| 540 | |a Springer Science+Business Media New York, 2014 | ||
| 690 | 7 | |a Soft tissue sarcomas |2 nationallicence | |
| 690 | 7 | |a Computed tomography texture analysis |2 nationallicence | |
| 690 | 7 | |a CT perfusion |2 nationallicence | |
| 690 | 7 | |a Heterogeneity |2 nationallicence | |
| 690 | 7 | |a Treatment response |2 nationallicence | |
| 700 | 1 | |a Tian |D Fang |u Division of Abdominal Imaging and Intervention, Harvard Medical School and Massachusetts General Hospital, 55 Fruit St White 270, 02114, Boston, MA, USA |4 aut | |
| 700 | 1 | |a Hayano |D Koichi |u Division of Abdominal Imaging and Intervention, Harvard Medical School and Massachusetts General Hospital, 55 Fruit St White 270, 02114, Boston, MA, USA |4 aut | |
| 700 | 1 | |a Kambadakone |D Avinash |u Division of Abdominal Imaging and Intervention, Harvard Medical School and Massachusetts General Hospital, 55 Fruit St White 270, 02114, Boston, MA, USA |4 aut | |
| 700 | 1 | |a Sahani |D Dushyant |u Division of Abdominal Imaging and Intervention, Harvard Medical School and Massachusetts General Hospital, 55 Fruit St White 270, 02114, Boston, MA, USA |4 aut | |
| 773 | 0 | |t Abdominal Imaging |d Springer US; http://www.springer-ny.com |g 40/6(2015-08-01), 1705-1712 |x 0942-8925 |q 40:6<1705 |1 2015 |2 40 |o 261 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s00261-014-0318-3 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s00261-014-0318-3 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Tian |D Fang |u Division of Abdominal Imaging and Intervention, Harvard Medical School and Massachusetts General Hospital, 55 Fruit St White 270, 02114, Boston, MA, USA |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Hayano |D Koichi |u Division of Abdominal Imaging and Intervention, Harvard Medical School and Massachusetts General Hospital, 55 Fruit St White 270, 02114, Boston, MA, USA |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Kambadakone |D Avinash |u Division of Abdominal Imaging and Intervention, Harvard Medical School and Massachusetts General Hospital, 55 Fruit St White 270, 02114, Boston, MA, USA |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Sahani |D Dushyant |u Division of Abdominal Imaging and Intervention, Harvard Medical School and Massachusetts General Hospital, 55 Fruit St White 270, 02114, Boston, MA, USA |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Abdominal Imaging |d Springer US; http://www.springer-ny.com |g 40/6(2015-08-01), 1705-1712 |x 0942-8925 |q 40:6<1705 |1 2015 |2 40 |o 261 | ||