Discrete-space versus continuous-space lesion boundary and area definitions

Variability of tumor area measurements for response assessment in malignant pleural mesothelioma

PURPOSE

The measurement of malignant pleural mesothelioma is critical to the assessment of tumor response to therapy. Current response assessment standards utilize summed linear measurements acquired on three computed tomography (CT) sections. The purpose of this study was to evaluate manual area measurements as an alternate response assessment metric, specifically through the study of measurement interobserver variability.

METHODS

Two CT scans from each of 31 patients were collected. Using a computer interface, five observers contoured tumor on three selected CT sections from each baseline scan. Four observers also constructed matched follow-up scan tumor contours for the same 31 patients. Area measurements extracted from these contours were compared using a random effects analysis of variance model to assess relative interobserver variability. The sums of section area measurements were also analyzed, since these area sums are more clinically relevant for response assessment.

RESULTS

When each observer's measurements were compared with those of the other four observers, strong correlation was observed. The 95% confidence interval for relative interobserver variability of baseline scan summed area measurements was [-71%, +240%], spanning 311%. For the follow-up scan summed area measurements, the 95% confidence interval for relative interobserver variability was [-41%, +70%], spanning 111%. At both baseline and follow-up, the variability among observers was a significant component of the total variability in both per-section and summed area measurements (p<0.0001).

CONCLUSIONS

Despite the ability of tumor area measurements to capture tumor burden with greater fidelity than linear tumor thickness measurements, manual area measurements may not be a robust means of response assessment in mesothelioma patients.

Lung volume measurements as a surrogate marker for patient response in malignant pleural mesothelioma

INTRODUCTION

The purpose of this study was to investigate the continuous changes in three distinct response assessment methods during treatment as a marker of response for patients with mesothelioma. Linear tumor thickness measurements, disease volume measurements, and lung volume measurements (a physiological correlate of disease volumes) were investigated in this study.

METHODS

Serial computed tomography scans were obtained during the course of clinically standard chemotherapy for 61 patients. For each of the 216 computed tomography scans, the aerated lung volumes were segmented using a fully automated method, and the pleural disease volume was segmented using a semiautomated method. Modified Response Evaluation Criteria in Solid Tumors linear-thickness measurements were acquired clinically. Diseased (ipsilateral) lung volumes were normalized by the respective contralateral lung volumes to account for the differences in inspiration between scans for each patient. Relative changes in each metric from baseline were tracked over the course of follow-up imaging. Survival modeling was performed using Cox proportional hazards models with time-varying covariates.

RESULTS

Median survival from pretreatment baseline imaging was 12.7 months. A negative correlation was observed between measurements of lung volume and disease volume, and a positive correlation was observed between linear-thickness measurements and disease volume. As continuous numerical parameters, all three response assessment methods were significant imaging biomarkers of patient prognosis in independent survival models.

CONCLUSIONS

Analysis of trajectories of linear-thickness measurements, disease volume measurements, and lung volume measurements during chemotherapy for patients with mesothelioma indicates that increasing linear thickness, increasing disease volume, and decreasing lung volume are all significantly and independently associated with poor patient prognosis.

Disease volumes as a marker for patient response in malignant pleural mesothelioma

BACKGROUND

The goal of this study was to create a comprehensive model for malignant pleural mesothelioma patient survival utilizing continuous, time-varying estimates of disease volume from computed tomography (CT) imaging in conjunction with clinical covariates.

PATIENTS AND METHODS

Serial CT scans were obtained during the course of clinically standard chemotherapy for 81 patients. The pleural disease volume was segmented for each of the 281 CT scans, and relative changes in disease volume from the baseline scan were tracked over the course of serial follow-up imaging. A prognostic model was built using time-varying disease volume measurements in conjunction with clinical covariates.

RESULTS

Over the course of treatment, disease volume decreased by an average of 19%, and median patient survival was 12.6 months from baseline. In a multivariate survival model, changes in disease volume were significantly associated with patient survival along with disease histology, Eastern Cooperative Oncology Group performance status, and presence of dyspnea.

CONCLUSIONS

Analysis of the trajectories of disease volumes during chemotherapy for patients with mesothelioma indicates that increasing disease volume was significantly and independently associated with poor patient prognosis in both univariate and multivariate survival models.

The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI): a completed reference database of lung nodules on CT scans

PURPOSE

The development of computer-aided diagnostic (CAD) methods for lung nodule detection, classification, and quantitative assessment can be facilitated through a well-characterized repository of computed tomography (CT) scans. The Lung Image Database Consortium (LIDC) and Image Database Resource Initiative (IDRI) completed such a database, establishing a publicly available reference for the medical imaging research community. Initiated by the National Cancer Institute (NCI), further advanced by the Foundation for the National Institutes of Health (FNIH), and accompanied by the Food and Drug Administration (FDA) through active participation, this public-private partnership demonstrates the success of a consortium founded on a consensus-based process.

METHODS

Seven academic centers and eight medical imaging companies collaborated to identify, address, and resolve challenging organizational, technical, and clinical issues to provide a solid foundation for a robust database. The LIDC/IDRI Database contains 1018 cases, each of which includes images from a clinical thoracic CT scan and an associated XML file that records the results of a two-phase image annotation process performed by four experienced thoracic radiologists. In the initial blinded-read phase, each radiologist independently reviewed each CT scan and marked lesions belonging to one of three categories ("nodule > or =3 mm," "nodule <3 mm," and "non-nodule > or =3 mm"). In the subsequent unblinded-read phase, each radiologist independently reviewed their own marks along with the anonymized marks of the three other radiologists to render a final opinion. The goal of this process was to identify as completely as possible all lung nodules in each CT scan without requiring forced consensus.

RESULTS

The Database contains 7371 lesions marked "nodule" by at least one radiologist. 2669 of these lesions were marked "nodule > or =3 mm" by at least one radiologist, of which 928 (34.7%) received such marks from all four radiologists. These 2669 lesions include nodule outlines and subjective nodule characteristic ratings.

CONCLUSIONS

The LIDC/IDRI Database is expected to provide an essential medical imaging research resource to spur CAD development, validation, and dissemination in clinical practice.