The Incremental Value of Subjective and Quantitative Assessment of ¹⁸F-FDG PET for the Prediction of Pathologic Complete Response to Preoperative Chemoradiotherapy in Esophageal Cancer

Abstract

A reliable prediction of a pathologic complete response (pathCR) to chemoradiotherapy before surgery for esophageal cancer would enable investigators to study the feasibility and outcome of an organ-preserving strategy after chemoradiotherapy. So far no clinical parameters or diagnostic studies are able to accurately predict which patients will achieve a pathCR. The aim of this study was to determine whether subjective and quantitative assessment of baseline and postchemoradiation 18 F-FDG PET can improve the accuracy of predicting pathCR to preoperative chemoradiotherapy in esophageal cancer beyond clinical predictors. Methods: This retrospective study was approved by the institutional review board, and the need for written informed consent was waived. Clinical parameters along with subjective and quantitative parameters from baseline and postchemoradiation 18 F-FDG PET were derived from 217 esophageal adenocarcinoma patients who underwent chemoradiotherapy followed by surgery. The associations between these parameters and pathCR were studied in univariable and multivariable logistic regression analysis. Four prediction models were constructed and internally validated using bootstrapping to study the incremental predictive values of subjective assessment of 18 F-FDG PET, conventional quantitative metabolic features, and comprehensive 18 F-FDG PET texture/geometry features, respectively. The clinical benefit of 18 F-FDG PET was determined using decision-curve analysis. Results: A pathCR was found in 59 (27%) patients. A clinical prediction model (corrected c-index, 0.67) was improved by adding 18 F-FDG PET-based subjective assessment of response (corrected c-index, 0.72). This latter model was slightly improved by the addition of 1 conventional quantitative metabolic feature only (i.e., postchemoradiation total lesion glycolysis; corrected c-index, 0.73), and even more by subsequently adding 4 comprehensive 18 F-FDG PET texture/geometry features (corrected c-index, 0.77). However, at a decision threshold of 0.9 or higher, representing a clinically relevant predictive value for pathCR at which one may be willing to omit surgery, there was no clear incremental value. Conclusion: Subjective and quantitative assessment of 18 F-FDG PET provides statistical incremental value for predicting pathCR after preoperative chemoradiotherapy in esophageal cancer. However, the discriminatory improvement beyond clinical predictors does not translate into a clinically relevant benefit that could change decision making.

Characterization of PET/CT images using texture analysis: the past, the present… any future?

“…Although a number of studies have included only between 20 and 70 patients [50, 71, 86–92], some of the most recent studies have included between 80 and more than 200 patients: 88 patients with oropharyngeal squamous cell carcinoma [93], 103 with bone and soft tissue lesions [94], 101 with early-stage NSCLC [95], 112 with oesophageal cancer and 101 with NSCLC [60], 113 with glioma [36], 107 and 217 with oesophageal cancer [96, 97], 132 with lymph node involvement in lung cancer [98], 116, 195 and 201 with NSCLC [99–101], 137 with pancreatic lesions [102], and 188 lesions in lymphoma patients [103]. Some of the most recent studies have also used more robust statistical analysis, compared to these recently reviewed [28], several of them using a machine-learning method, e.g.…”

“…neural networks [96], support vector machines [94, 98, 103] or the least absolute shrinkage and selection operator (LASSO) [95, 101]. The majority of these recent studies have concluded that TA can provide useful quantitative metrics regarding patient management (prognosis, response to therapy, distant metastasis prediction) in different cancer models except one that showed more mixed results [104], whereas another concluded that the improvement, although significant, may not be sufficient to have a clinical impact [97]. …”

Radiomics in esophageal and gastric cancer

“…An initial study by Tan et al found in 20 patients that 2 SUV mean parameters, SUV mean decline and SUV mean skewness, and 3 texture features GLCM inertia, GLCM correlation, and GLCM cluster prominence, were significant predictors of complete response with an AUROC of 0.76 [31]. In 217 patients with adenocarcinoma, Van Rossum et al developed a prediction model which included change in run length matrix (RLM) run percentage, change in GLCM entropy, and post-chemoradiation roundness, and increased the corrected c-index (concordance-index, comparable to AUROC) from 0.67 to 0.77, compared to the clinical model alone [32]. Yip et al found that a change in run length and size-zone matrix differentiated responders from non-responders [33].…”

Quantitative Radiomic Phenotyping of Cervix Cancer

“…Not every radiomic feature that significantly predicted the survival of lung cancer patients could also predict the survival of head-and-neck cancer patients and vice versa. Radiomic features are better at predicting treatment response than conventional measures, such as tumor volume and diameter, and the maximum radiotracer uptake on positron emission tomography (PET) imaging [19][20][21][22][23][24][25]. Metastatic latency of tumors could also be predicted by radiomic features [26][27].…”