Pre-Chemoradiotherapy FDG PET/CT cannot Identify Residual Metabolically-Active Volumes within Individual Esophageal Tumors

Use of baseline 18F-FDG PET scan to identify initial sub-volumes with local failure after concomitant radio-chemotherapy in head and neck cancer

Introduction

Head and neck squamous cell carcinoma (HNSCC) treated by radio-chemotherapy have a significant local recurrence rate. It has been previously suggested that 18F-FDG PET could identify the high uptake areas that can be potential targets for dose boosting. The purpose of this study was to compare the location of initial hypermetabolic regions on baseline scans with the metabolic relapse sites after radio-chemotherapy in HNSCC.

Results

The initial functional tumor volume was significantly higher for patients with proven local recurrence or residual disease (23.5 cc vs. 8.9 cc; p = 0.0005). The overlap between baseline and follow-up sub-volumes were moderate with an overlap fraction ranging from 0.52 to 0.39 between R40 and I30 to I60.

Conclusion

In our study the overlap between baseline and post-therapeutic metabolic tumor sub-volumes was only moderate. These results need to be investigated in a larger cohort acquired with a more standardized patient repositioning protocol for sequential PET imaging.

Methods

Pre and post treatment PET/CT scans of ninety four HNSCC patients treated with radio-chemotherapy were retrospectively reviewed. Follow-up 18F-FDG PET/CT images were registered to baseline scans using a rigid body transformation. Seven metabolic tumor sub-volumes were obtained on the baseline scans using a fixed percentage of SUV_max (I30, I40, I50, I60, I70, I80, and I90) and were subsequently compared with two post-treatment sub-volumes (R40, R90) in 38 cases of local recurrence or residual metabolic disease. Overlap fraction, Dice and Jaccard indices, common volume/baseline volume and common volume/recurrent volume were used to determine the overlap of the different estimated sub-volumes.

Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer

PURPOSE

To develop a radiomics prediction model to improve pulmonary nodule (PN) classification in low-dose CT. To compare the model with the American College of Radiology (ACR) Lung CT Screening Reporting and Data System (Lung-RADS) for early detection of lung cancer.

METHODS

We examined a set of 72 PNs (31 benign and 41 malignant) from the Lung Image Database Consortium image collection (LIDC-IDRI). One hundred three CT radiomic features were extracted from each PN. Before the model building process, distinctive features were identified using a hierarchical clustering method. We then constructed a prediction model by using a support vector machine (SVM) classifier coupled with a least absolute shrinkage and selection operator (LASSO). A tenfold cross-validation (CV) was repeated ten times (10 × 10-fold CV) to evaluate the accuracy of the SVM-LASSO model. Finally, the best model from the 10 × 10-fold CV was further evaluated using 20 × 5- and 50 × 2-fold CVs.

RESULTS

The best SVM-LASSO model consisted of only two features: the bounding box anterior-posterior dimension (BB_AP) and the standard deviation of inverse difference moment (SD_IDM). The BB_AP measured the extension of a PN in the anterior-posterior direction and was highly correlated (r = 0.94) with the PN size. The SD_IDM was a texture feature that measured the directional variation of the local homogeneity feature IDM. Univariate analysis showed that both features were statistically significant and discriminative (P = 0.00013 and 0.000038, respectively). PNs with larger BB_AP or smaller SD_IDM were more likely malignant. The 10 × 10-fold CV of the best SVM model using the two features achieved an accuracy of 84.6% and 0.89 AUC. By comparison, Lung-RADS achieved an accuracy of 72.2% and 0.77 AUC using four features (size, type, calcification, and spiculation). The prediction improvement of SVM-LASSO comparing to Lung-RADS was statistically significant (McNemar's test P = 0.026). Lung-RADS misclassified 19 cases because it was mainly based on PN size, whereas the SVM-LASSO model correctly classified 10 of these cases by combining a size (BB_AP) feature and a texture (SD_IDM) feature. The performance of the SVM-LASSO model was stable when leaving more patients out with five- and twofold CVs (accuracy 84.1% and 81.6%, respectively).

CONCLUSION

We developed an SVM-LASSO model to predict malignancy of PNs with two CT radiomic features. We demonstrated that the model achieved an accuracy of 84.6%, which was 12.4% higher than Lung-RADS.