CBCT-based active contour segmentation of bone invasion in oral squamous cell carcinoma - A preliminary retrospective study

Context: Bone invasion by oral squamous cell carcinoma (OSSC) alone forms a predictor of overall prognosis, survival rate. Volume of actual bone osteolysis might give valuable information for treatment planning rather than planar measurements. Aim: To compare the manual and semiautomatic active contour segmentation in the volumetric analysis of mandibular bone invasion by OSCC through Cone Beam Computed Tomography (CBCT). Settings and Design: Hospital-based, preliminary, retrospective study. Methods and Material: Ten CBCT DICOM images of histologically confirmed cases of OSCC with frank bone invasion in the mandible were selected from the archive after satisfying our inclusion and exclusion criteria. Volumetrical analysis of tumor eroded area in the mandibular alveolus were done through ITK SNAP software (version 3.2) both by manual and semiautomatic segmentation and compared with each other. Statistical Analysis Used: R statistical computing software v3.6.3 (R core foundation, Vienna) was used. Intraclass correlation coefficient (ICC), Bland and Altman method, Passing Bablok analysis with Pearson's Correlation, Dice Similarity coefficient (DSC) were done for single examiner reliability, comparison of volumes and segmentation accuracy respectively. The third metric, that is, time needed for each segmentation method was also compared and statistically analysed. Results: Since Pearson's correlation coefficient of r = 1 and P value was 0.233 (>0.005), semiautomatic method of volumetric segmentation proved to be as accurate and reproducible as manual method without any significant volume difference. In addition to that, semiautomatic method was 10 times more rapid than manual method. Conclusions: This is the first kind of study in the literature showing the feasibility of active contour semiautomatic segmentation in the volumetric analysis of bone invasion caused by the OSCC which might be helpful in efficient oncological treatment planning.