When Experts Trace the Same Tumor: Study Reveals Hidden Variability in CT Tumor Outlines

Artificial intelligence is rapidly transforming medicine, and one of its most promising frontiers is radiomics, the extraction of hundreds of quantitative features from ordinary diagnostic images. But every AI model trained on medical images begins with a deceptively simple human act: someone must draw a line around the tumor. A new study from Cornell University has now measured just how consistent those hand-drawn lines really are when specialists outline a common canine oral cancer on computed tomography scans, and the results reveal both reassuring agreement and surprising discordance.

The research, published in Veterinary Oncology, focused on canine acanthomatous ameloblastoma, or CAA, the most common odontogenic tumor in dogs, accounting for roughly ten percent of oral neoplasms in a species where oral tumors overall represent six to seven percent of all cancers. The gold standard treatment is surgical excision with margins of five to ten millimeters, which makes accurate tumor delineation on preoperative imaging critically important. If the tumor boundary drawn on a CT scan is wrong, the surgeon’s plan may be wrong too, and incomplete excision carries well-documented consequences: higher recurrence rates, reduced survival, and poorer overall prognosis.

To test the reliability of manual tumor outlining, the team selected ten confirmed CAA cases from the Cornell University Hospital for Animals CT database covering 2014 to 2021. Every case had been diagnosed by histopathology, and each scan included both non-contrast and contrast-enhanced images with bone and soft tissue reconstructions. The cohort comprised four Labrador retrievers and six other dogs of assorted breeds, with a mean age of 7.4 years. Nine tumors sat in the mandible, six rostrally and three caudally, while one arose from the left maxillary canine. Scans were acquired on a Toshiba Aquilion 16-slice scanner at 120 kilovolt peak, with pixel dimensions averaging 0.40 millimeters and slice thicknesses ranging from 0.5 to 2 millimeters.

Three board-certified specialists, two veterinary dentists and oral surgeons and one veterinary medical oncologist, independently contoured each of the ten tumors twice, in two randomized sittings separated by at least one week to reduce recall bias. Working in the open-source platform 3DSlicer, they could toggle between contrast and non-contrast datasets and use paintbrush or drawing tools on each transaxial slice. They received only crude tumor locations and a dental map, with no radiology reports or medical records, deliberately mimicking the conditions under which such contouring is often performed in practice without radiologist input. Each case took between 30 and 60 minutes, and no participant faced time restrictions.

The investigators then compared all the resulting contours using three complementary metrics. The Dice Similarity Index quantifies spatial overlap between two segmentations, ranging from zero for no overlap to one for perfect overlap. The Hausdorff Distance captures the worst-case error, measuring the maximum distance between the boundaries of two contours. Centroid positions, the arithmetic mean coordinates of all voxels in a segmented volume, reveal whether observers placed the tumor’s geometric center in the same spot. Volumes were also compared, with the median tumor volume across all trials measuring 2.46 cubic centimeters and a range from 1.01 to 9.46 cubic centimeters.

The headline finding was a striking split between global and local agreement. Correlation coefficients for centroid coordinates and tumor volumes showed excellent agreement both within and between observers, suggesting that everyone was capturing roughly the same mass in roughly the same place. Yet the boundary-level metrics told a more nuanced story. Intraobserver Dice values averaged 0.78 with a standard deviation of 0.11, indicating only moderate agreement even with oneself across repeated trials. Interobserver Dice values dropped to a mean of 0.69 with a standard deviation of 0.14, which the study’s classification scheme labels as poor agreement between different participants.

Hausdorff Distances reinforced that pattern. Within a single observer’s two trials, the mean worst-case boundary discrepancy was 0.80 millimeters, but between different observers it climbed to 1.21 millimeters, with a standard deviation of 0.66. Effect size analysis using Cohen’s d showed small, medium, and large effects for the three pairwise observer comparisons, and interobserver volume estimates were statistically different at P less than 0.001, with effect sizes ranging from 0.31 to 0.72. Analysis of variance also flagged significant differences in the centroid Z coordinate between one pair of observers and in volumes between another. Notably, the authors estimated that observer differences accounted for 15.6 percent of volume variability and 17.7 percent of Hausdorff Distance variability.

Why does this matter beyond veterinary clinics? Because manual segmentations are the training data for the radiomics and AI models now being built to diagnose, classify, and prognose tumors automatically. In human medicine, variations in gross tumor volume delineation can reach as much as twenty percent, and radiomics-based models have shown good diagnostic performance in head and neck cancers, canine small intestinal tumors, and renal tumors. If the ground truth used to train these algorithms is itself inconsistent, the models inherit that inconsistency. The Cornell team frames their work as a technical validation step, one of the internal, external, and technical validation processes required before AI models can be trusted in the clinic, and their reliability data can now serve as a benchmark for comparing segmentation methods and developing autosegmentation tools.

The authors are candid about their study’s limitations. Ten cases and three observers is a small sample, CT acquisition parameters varied across cases, slice thickness ranged up to 2 millimeters along the Z axis, and the cohort overrepresented rostral mandibular tumors and Labrador retrievers, factors that could matter when building radiomics models even if they likely had little effect on observer agreement itself. Time per case was self-reported rather than measured, so learning effects cannot be excluded, and the design deliberately departed from standard clinical workflow by withholding radiology and pathology reports. The team also notes that whether these minor contour inconsistencies would actually change surgical margins or outcomes cannot be determined without histopathological assessment of complete excisions, a question they hope larger studies will address.

Still, the takeaway is clear and broadly relevant: even experienced specialists tracing the same tumor on the same scans produce boundaries that overlap only moderately, even though the tumor’s center and size come out remarkably consistently. As radiomics and AI move from promising research tools toward clinical integration in both human and veterinary oncology, the study argues that standardized contouring protocols are no longer optional. Reliable segmentation, the authors conclude, is foundational, and quantifying exactly how much human hands disagree is the first step toward building machines that do it better.

Subject of Research: Observer variability in manual CT tumor delineation for canine acanthomatous ameloblastoma and its implications for radiomics validation

Article Title: Assessment of inter and intraobserver variability in manually delineated oral canine acanthomatous ameloblastoma from computed tomography scans

Article References: Drozd, M. E., Fiani, N., Sylvester, S., Khalil, M. K., Peralta, S., & Basran, P. S. (2025). Assessment of inter and intraobserver variability in manually delineated oral canine acanthomatous ameloblastoma from computed tomography scans. Veterinary Oncology, 2(1), Article 37. https://doi.org/10.1186/s44356-025-00052-1

Image Credits: AI Generated

DOI: 10.1186/s44356-025-00052-1

Keywords: radiomics, canine acanthomatous ameloblastoma, computed tomography, tumor segmentation, observer variability, Dice Similarity Index, Hausdorff distance, veterinary oncology, 3DSlicer, artificial intelligence, surgical margins, gross tumor volume