Head and neck cancers on CT: preliminary study of treatment response assessment based on computerized volume analysis

Abstract

Objective: The objective of our study was to investigate the feasibility of computerized segmentation of lesions on head and neck CT scans and evaluate its potential for estimating changes in tumor volume in response to treatment of head and neck cancers.

Materials and methods: Twenty-six CT scans were retrospectively collected from the files of 13 patients with 35 head and neck lesions. The CT scans were obtained from an examination performed before treatment (pretreatment scan) and an examination performed after one cycle of chemotherapy (posttreatment scan). Thirteen lesions were primary site cancers and 22 were metastatic lymph nodes. An experienced radiologist (radiologist 1) marked the 35 lesions and outlined each lesion's 2D contour on the best slice on both the pre- and posttreatment scans. Full 3D contours were also manually extracted for the 13 primary tumors. Another experienced radiologist (radiologist 2) verified and modified, if necessary, all manually drawn 2D and 3D contours. An in-house-developed computerized system performed 3D segmentation based on a level set model.

Results: The computer-estimated change in tumor volume and percentage change in tumor volume between the pre- and posttreatment scans achieved a high correlation (intraclass correlation coefficient [ICC] = 0.98 and 0.98, respectively) with the estimates from manual segmentation for the 13 primary tumors. The average error in estimating the percentage change in tumor volume by automatic segmentation relative to the radiologists' average error was -1.5% +/- 5.4% (SD). For the 35 lesions, the ICC between the automatic and manual estimates of change in pre- to posttreatment tumor area was 0.93 and of percentage change in pre- to posttreatment tumor area was 0.85. The average error in estimating the percentage change in tumor area by automatic segmentation was -3.2% +/- 15.3%.

Conclusion: Preliminary results indicate that this computerized segmentation system can reliably estimate changes in tumor size on CT scans relative to radiologists' manual segmentation. This information can be used to calculate changes in tumor size on pre- and posttreatment scans to assess response to treatment.

Fig 1. 81-year-old woman with tongue base carcinoma. This lesion is example of subtle lesion (difficulty rating = 4) in data set

A, Axial CT scan obtained before treatment. B, Reference standard (i.e., hand-drawn) segmentation (black contour) and automatic segmentation (white contour) are shown superimposed on pretreatment scan. C, Axial CT scan obtained after treatment. D, Reference standard segmentation (black contour) and automatic segmentation (white contour) are shown superimposed on posttreatment scan. Lesion is shown on best slice marked by radiologist for each scan.

Fig 2. Estimates of pre- and posttreatment volume of 13 primary site tumors (○)

A and B, Tumor volume estimates based on automatic (A) and manual (B) segmentation. Dashed line shows cases in which pre- and posttreatment volumes are identical, and solid line shows linear regression line for data.

Fig 3. Automatic versus manual estimates of tumor volume for 13 primary site tumors (○)

A and B, Estimates of pretreatment tumor volume (A) (intraclass correlation coefficient [ICC] = 0.98) and of posttreatment tumor volume (B) (ICC = 0.97) are shown. Dashed line shows cases in which automatic and manual estimates are identical, and solid line shows linear regression line for data.

Fig 4. Automatic versus manual estimates of change and percentage change in tumor volume for 13 primary site tumors (○)

A and B, Change in pre- to posttreatment volume (A) (intraclass correlation coefficient [ICC] = 0.98) and percentage change in pre- to posttreatment volume (B) (ICC = 0.98) are shown. Dashed line shows cases in which automatic and manual estimates are identical, and solid line shows linear regression line for data.

Fig 5. Automatic versus manual estimates of tumor area for 35 lesions (○)

A and B, Estimates of pretreatment area (A) (intraclass correlation coefficient [ICC] = 0.95) and posttreatment area (B) (ICC = 0.93) are shown. Dashed line shows cases in which automatic and manual estimates are identical, and solid line shows linear regression line for data.

Fig 6. Automatic versus manual estimates of change and percentage change in tumor area for all 35 lesions (○)

A and B, Change in pre- to posttreatment area (A) (intraclass correlation coefficient [ICC] = 0.93) and percentage change in pre- to-posttreatment area (B) (ICC = 0.85) are shown. Dashed line shows cases in which automatic and manual estimates are identical, and solid line shows linear regression line for data.