Journal of Medical Physics
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Year : 2020  |  Volume : 45  |  Issue : 4  |  Page : 199-205

Simplifying tumor volume estimation from linear dimensions for intra-cranial lesions treated with stereotactic radiosurgery

1 Division of PET Imaging, Institute of Nuclear Medicine and Allied Sciences, Delhi, India
2 Department of Neurosurgery, Army Hospital Research and Referral, Delhi, India
3 Department of Radiation Oncology, Army Hospital Research and Referral, Delhi, India

Correspondence Address:
Dr. Manoj Kumar Semwal
Department of Radiation Oncology, Army Hospital Research and Referral, Subroto Park, Dhaula Kuan, New Delhi 110 010
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/jmp.JMP_56_20

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Aims: This study aims to derive simple yet robust formula(s) for the calculation of cranial tumor volume using linear tumor dimensions in anterioposterior (AP), mediolateral (ML) and craniocaudal (CC) directions and also propose a reproducible methodology for tumor dimension measurements. Materials and Methods: Magnetic resonance images (MRI) of 337 patients planned for Gammaknife Stereotactic Radiosurgery for different types of brain tumors were analyzed using Leksell Gamma Plan (LGP) software. Tumor volume in three dimensional was outlined and maximum tumor diameters were measured in three orthogonal directions AP, ML, and CC on the MRI. Formulas were derived to calculate tumor volume from AP, ML, and CC diameters using linear regression technique. An agreement between the calculated volume and standard volume observed from LGP software was determined using Bland Altman (B-A) plot. A comparison was made between the volume calculated using traditionally used formula of ellipsoid, standard volume obtained from LGP software and volume calculated from formulas derived in the present study. Results: The tumors were divided into two categories based on their size for better volume prediction. The tumors having product of their diameters in the range 0–2.5cc were called “small tumors” and the formula proposed for their volume estimation ([INSIDE:1]) was found to predict the tumor volume with an average bias of 0.0005cc. For “large tumors,” having product of diameters in the range 2.5–36cc, the proposed formula ([INSIDE:2]) predicted the tumor volume with an average bias of 0.007cc. Conclusions: The two formulas proposed in the study are more accurate as compared to the commonly used formula that considers the tumors as ellipsoids. The methodology proposed in the study for measurement of linear tumor dimensions is simple and reproducible.

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