Does volumetric measurement serve as an imaging biomarker for tumor aggressiveness of ameloblastomas?

Accuracy of formula-based volume and image segmentation-based volume in calculation of preoperative cystic jaw lesions' volume

OBJECTIVE

The aim of this study was to assess the accuracy of formula-based volume measurements and the 3D volume analysis with different software packages in the calculation of preoperative cystic jaw lesions' volume. The secondary aim was to assess the reliability and the accuracy of 3 imaging software programs for measuring the cystic jaw lesions' volume in CBCT images.

MATERIALS AND METHODS

This study consisted of two parts: an in vitro part using 2 dry human mandibles that were used to create simulated osteolytic lesions to assess the accuracy of the volumetric analysis and formula-based volume. As a gold standard, the volume of each bone defect was determined by taking an impression using rapid soft silicone (Vinylight) and then quantifying the volume of the replica. Afterward, each tooth socket was scanned using a high-resolution CBCT. A retrospective part using archived CBCT radiographs that were taken from the database of the outpatient clinic of the oral and maxillofacial radiology department, Faculty of Dentistry, Minia University to assess the reliability of the 3 software packages. The volumetric data set was exported for volume quantification using the 3 software packages (MIMICS-OnDemand and InVesalius software). Also, the three greatest orthogonal diameters of the lesions were calculated, and the volume was assessed using the ellipsoid formula. Dunn's test was used for pair-wise comparisons when Friedman's test was significant. The inter-examiner agreement was assessed using Cronbach's alpha reliability coefficient and intra-class correlation coefficient.

RESULTS

Regarding the results of the retrospective part, there was a statistically significant difference between volumetric measurements by equation and different software (P value < 0.001, Effect size = 0.513). The inter-observer reliability of the measurements of the cystic lesions using the different software packages was very good. The highest inter-examiner agreement for volume measurement was found with InVesalius (Cronbach's alpha = 0.992). On the other hand, there was a statistically significant difference between dry mandible volumetric measurements and Gold Standard. All software showed statistically significantly lower dry mandible volumetric measurements than the gold standard.

CONCLUSION

Computer-aided assessment of cystic lesion volume using InVesalius, OnDemand, and MIMICS is a readily available, easy to use, non-invasive option. It confers an advantage over formula-based volume as it gives the exact morphology of the lesion so that potential problems can be detected before surgery. Volume analysis with InVesalius software was accurate in determining the volume of simulated periapical defects in a human cadaver mandible as compared to true volume. InVesalius software proved that open-source software can be robust yet user-friendly with the advantage of minimal cost to use.

Improved Diagnostic Accuracy of Ameloblastoma and Odontogenic Keratocyst on Cone-Beam CT by Artificial Intelligence

OBJECTIVE

The purpose of this study was to utilize a convolutional neural network (CNN) to make preoperative differential diagnoses between ameloblastoma (AME) and odontogenic keratocyst (OKC) on cone-beam CT (CBCT).

METHODS

The CBCT images of 178 AMEs and 172 OKCs were retrospectively retrieved from the Hospital of Stomatology, Wuhan University. The datasets were randomly split into a training dataset of 272 cases and a testing dataset of 78 cases. Slices comprising lesions were retained and then cropped to suitable patches for training. The Inception v3 deep learning algorithm was utilized, and its diagnostic performance was compared with that of oral and maxillofacial surgeons.

RESULTS

The sensitivity, specificity, accuracy, and F1 score were 87.2%, 82.1%, 84.6%, and 85.0%, respectively. Furthermore, the average scores of the same indexes for 7 senior oral and maxillofacial surgeons were 60.0%, 71.4%, 65.7%, and 63.6%, respectively, and those of 30 junior oral and maxillofacial surgeons were 63.9%, 53.2%, 58.5%, and 60.7%, respectively.

CONCLUSION

The deep learning model was able to differentiate these two lesions with better diagnostic accuracy than clinical surgeons. The results indicate that the CNN may provide assistance for clinical diagnosis, especially for inexperienced surgeons.

Dynamic Maxillary Sinus Changes of Facial Vascularized Composite Allotransplants

SUMMARY

Skin is one of the target tissues of rejection in face transplants and, because of its easy accessibility, has become the gold standard in the diagnosis of rejection. The allograft contains deeper tissues where rejection can occur, but samples cannot be obtained because of difficult access. Deep tissue changes were monitored on computed tomographic scans of the midface in six face transplant recipients with the help of image segmentation. The maxillary sinus was identified as a dynamic anatomical compartment. Observed changes in volume of the aeration relative to the opacification (aeration coefficient) of the maxillary sinus were quantified with the help of image segmentation. Changes in the aeration coefficient as a surrogate of mucosal swelling were quantified and related to time, treatment, and skin rejection grade. Lower aeration coefficients were found only in patients with transplanted maxillary sinus mucosa. Pathologic changes were not observed in face transplant recipients with a native maxillary sinus. The data show that the aeration coefficient was significantly lower at the time of biopsy-proven allograft rejection. Neither mechanical, nor infectious, nor medication side effects sufficiently explain the findings presented herein. The authors' findings are important to consider for clinical management of face transplant patients who receive parts of the sinonasal tract. The authors identify a potential radiologic biomarker of deep tissue allograft rejection. In the future, the proposed methodology might prove useful in monitoring deeper dynamic tissue changes in vascularized composite allografts and might help in designing patient-specific, individualized treatment strategies.

Image segmentation-based volume approximation-volume as a factor in the clinical management of osteolytic jaw lesions

OBJECTIVE

Size characterization of osteolytic jaw lesions (OJL), in particular of neoplastic nature, is heterogeneously performed and lacks standardization in the medical literature and clinical practice. An OJL's volume holds promise as a surrogate for treatment response and prognosis. We comparatively evaluate various methods for size characterization of odontogenic OJLs.

METHODS

We retrospectively performed semiautomatic image segmentation of CBCT data sets for volume approximation of neoplastic (51) and non-neoplastic odontogenic OJLs (100). We assessed the three greatest orthogonal diameters and calculated the volume using the cuboid- and ellipsoid-formula. Image segmentation was carried out using ITK-SNAP. Image segmentation-based volume approximation served as reference. Intra- and inter-rater variability were evaluated at hand of Bland-Altman-Analysis and dice similarity coefficient (DSC).

RESULTS

Concerning the intrarater variability, we found the DSC to be highest for image segmentation-based volume approximation, simultaneously showing the tightest limits of agreement and greatest reliability. The cuboid formula showed consistent overestimation of the lesion's volume with a percent mean difference of -52 % (upper and lower limits of agreement +8.57 %  and -112.63%, respectively). In mean, the ellipsoid formula underestimated the lesion's volume by 10.1% (upper and lower limits of agreement +76.8%  and -56.6%, respectively). Inter rater variability was higher for formula-based volume approximation. Volume and multilocularity (p = 0.001) correlate with aggressiveness and growth potential.

CONCLUSIONS

Segmentation-based volume approximation holds great promise for patient individualized treatment planning and clinical management. The data suggest that maximum tumour diameter-based size characterization, especially the cuboid-formula and the maximum diameter alone, should not be recommended.