» Articles » PMID: 23257692

Physiological Imaging-defined, Response-driven Subvolumes of a Tumor

Overview
Specialties Oncology
Radiology
Date 2012 Dec 22
PMID 23257692
Citations 9
Authors
Affiliations
Soon will be listed here.
Abstract

Purpose: To develop an image analysis framework to delineate the physiological imaging-defined subvolumes of a tumor in relating to treatment response and outcome.

Methods And Materials: Our proposed approach delineates the subvolumes of a tumor based on its heterogeneous distributions of physiological imaging parameters. The method assigns each voxel a probabilistic membership function belonging to the physiological parameter classes defined in a sample of tumors, and then calculates the related subvolumes in each tumor. We applied our approach to regional cerebral blood volume (rCBV) and Gd-DTPA transfer constant (K(trans)) images of patients who had brain metastases and were treated by whole-brain radiation therapy (WBRT). A total of 45 lesions were included in the analysis. Changes in the rCBV (or K(trans))-defined subvolumes of the tumors from pre-RT to 2 weeks after the start of WBRT (2W) were evaluated for differentiation of responsive, stable, and progressive tumors using the Mann-Whitney U test. Performance of the newly developed metrics for predicting tumor response to WBRT was evaluated by receiver operating characteristic (ROC) curve analysis.

Results: The percentage decrease in the high-CBV-defined subvolumes of the tumors from pre-RT to 2W was significantly greater in the group of responsive tumors than in the group of stable and progressive tumors (P<.007). The change in the high-CBV-defined subvolumes of the tumors from pre-RT to 2W was a predictor for post-RT response significantly better than change in the gross tumor volume observed during the same time interval (P=.012), suggesting that the physiological change occurs before the volumetric change. Also, K(trans) did not add significant discriminatory information for assessing response with respect to rCBV.

Conclusion: The physiological imaging-defined subvolumes of the tumors delineated by our method could be candidates for boost target, for which further development and evaluation is warranted.

Citing Articles

A Deep Learning Approach for Automatic Segmentation during Daily MRI-Linac Radiotherapy of Glioblastoma.

Breto A, Cullison K, Zacharaki E, Wallaengen V, Maziero D, Jones K Cancers (Basel). 2023; 15(21).

PMID: 37958415 PMC: 10647471. DOI: 10.3390/cancers15215241.


Stereotactic radiotherapy for brain oligometastases.

Lupattelli M, Tini P, Nardone V, Aristei C, Borghesi S, Maranzano E Rep Pract Oncol Radiother. 2022; 27(1):15-22.

PMID: 35402029 PMC: 8989457. DOI: 10.5603/RPOR.a2021.0133.


Habitat Imaging-Based F-FDG PET/CT Radiomics for the Preoperative Discrimination of Non-small Cell Lung Cancer and Benign Inflammatory Diseases.

Chen L, Liu K, Zhao X, Shen H, Zhao K, Zhu W Front Oncol. 2021; 11:759897.

PMID: 34692548 PMC: 8526895. DOI: 10.3389/fonc.2021.759897.


Artificial intelligence in tumor subregion analysis based on medical imaging: A review.

Lin M, Wynne J, Zhou B, Wang T, Lei Y, Curran W J Appl Clin Med Phys. 2021; 22(7):10-26.

PMID: 34164913 PMC: 8292694. DOI: 10.1002/acm2.13321.


MR-guided radiation therapy: transformative technology and its role in the central nervous system.

Cao Y, Tseng C, Balter J, Teng F, Parmar H, Sahgal A Neuro Oncol. 2017; 19(suppl_2):ii16-ii29.

PMID: 28380637 PMC: 5463498. DOI: 10.1093/neuonc/nox006.


References
1.
Chang E, Wefel J, Hess K, Allen P, Lang F, Kornguth D . Neurocognition in patients with brain metastases treated with radiosurgery or radiosurgery plus whole-brain irradiation: a randomised controlled trial. Lancet Oncol. 2009; 10(11):1037-44. DOI: 10.1016/S1470-2045(09)70263-3. View

2.
Huber P, Hawighorst H, Fuss M, van Kaick G, Wannenmacher M, Debus J . Transient enlargement of contrast uptake on MRI after linear accelerator (linac) stereotactic radiosurgery for brain metastases. Int J Radiat Oncol Biol Phys. 2001; 49(5):1339-49. DOI: 10.1016/s0360-3016(00)01511-x. View

3.
Cao Y, Nagesh V, Hamstra D, Tsien C, Ross B, Chenevert T . The extent and severity of vascular leakage as evidence of tumor aggressiveness in high-grade gliomas. Cancer Res. 2006; 66(17):8912-7. DOI: 10.1158/0008-5472.CAN-05-4328. View

4.
Menegakis A, Eicheler W, Yaromina A, Thames H, Krause M, Baumann M . Residual DNA double strand breaks in perfused but not in unperfused areas determine different radiosensitivity of tumours. Radiother Oncol. 2011; 100(1):137-44. DOI: 10.1016/j.radonc.2011.07.001. View

5.
Cao Y, Tsien C, Nagesh V, Junck L, Ten Haken R, Ross B . Survival prediction in high-grade gliomas by MRI perfusion before and during early stage of RT [corrected]. Int J Radiat Oncol Biol Phys. 2005; 64(3):876-85. DOI: 10.1016/j.ijrobp.2005.09.001. View