Dataset Information

Prediction of Non-Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer Patients with ¹⁸F-FDG PET Radiomics Based Machine Learning Classification.

ABSTRACT:

Background

Approximately 26% of esophageal cancer (EC) patients do not respond to neoadjuvant chemoradiotherapy (nCRT), emphasizing the need for pre-treatment selection. The aim of this study was to predict non-response using a radiomic model on baseline ¹⁸F-FDG PET.

Methods

Retrospectively, 143 ¹⁸F-FDG PET radiomic features were extracted from 199 EC patients (T1N1-3M0/T2-4aN0-3M0) treated between 2009 and 2019. Non-response (n = 57; 29%) was defined as Mandard Tumor Regression Grade 4-5 (n = 44; 22%) or interval progression (n = 13; 7%). Randomly, 139 patients (70%) were allocated to explore all combinations of 24 feature selection strategies and 6 classification methods towards the cross-validated average precision (AP). The predictive value of the best-performing model, i.e AP and area under the ROC curve analysis (AUC), was evaluated on an independent test subset of 60 patients (30%).

Results

The best performing model had an AP (mean ± SD) of 0.47 ± 0.06 on the training subset, achieved by a support vector machine classifier trained on five principal components of relevant clinical and radiomic features. The model was externally validated with an AP of 0.66 and an AUC of 0.67.

Conclusion

In the present study, the best-performing model on pre-treatment ¹⁸F-FDG PET radiomics and clinical features had a small clinical benefit to identify non-responders to nCRT in EC.

SUBMITTER: Beukinga RJ

PROVIDER: S-EPMC9139915 | biostudies-literature | 2022 Apr

REPOSITORIES: biostudies-literature

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Publications

Prediction of Non-Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer Patients with <sup>18</sup>F-FDG PET Radiomics Based Machine Learning Classification.

Beukinga Roelof J RJ Poelmann Floris B FB Kats-Ugurlu Gursah G Viddeleer Alain R AR Boellaard Ronald R De Haas Robbert J RJ Plukker John Th M JTM Hulshoff Jan Binne JB

Diagnostics (Basel, Switzerland) 20220424 5

<h4>Background</h4>Approximately 26% of esophageal cancer (EC) patients do not respond to neoadjuvant chemoradiotherapy (nCRT), emphasizing the need for pre-treatment selection. The aim of this study was to predict non-response using a radiomic model on baseline <sup>18</sup>F-FDG PET.<h4>Methods</h4>Retrospectively, 143 <sup>18</sup>F-FDG PET radiomic features were extracted from 199 EC patients (T1N1-3M0/T2-4aN0-3M0) treated between 2009 and 2019. Non-response (<i>n</i> = 57; 29%) was defined ...[more]

PMID: 35626225

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Project description:Active surveillance for patients with esophageal cancer and a clinically complete response (cCR) after neoadjuvant chemoradiotherapy (nCRT) is being studied. Active surveillance requires accurate clinical response evaluations. 18F-FDG PET/CT might be able to detect local tumor recurrence after nCRT as soon as the esophagus recovers from radiation-induced esophagitis. The aims of this study were to assess the value of serial 18F-FDG PET/CT scans for detecting local recurrence in patients beyond 3 mo after nCRT and to determine when radiation-induced esophagitis has resolved. Methods: This retrospective multicenter study included patients who had cCR after nCRT, who initially declined surgery, and who subsequently underwent active surveillance. Clinical response evaluations included 18F-FDG PET/CT, endoscopic biopsies, and endoscopic ultrasound with fine-needle aspiration at regular intervals. SUVmax normalized for lean body mass (SULmax) was measured at the primary tumor site. The percentage change in SULmax (Δ%SULmax) between the last follow-up scan and the scan at 3 mo after nCRT was calculated. Tumor recurrence was defined as biopsy-proven vital tumor at the initial tumor site. Results: Of 41 eligible patients, 24 patients had recurrent disease at a median of 6.5 mo after nCRT and 17 patients remained cancer free during a median follow-up of 24 mo after nCRT. Five of 24 patients with tumor recurrence had sudden intense SULmax increases of greater than 180%. In 19 of 24 patients with tumor recurrence, SULmax gradually increased (median Δ%SULmax, +18%), whereas SULmax decreased (median Δ%SULmax, -12%) in patients with ongoing cCR (P < 0.001, independent-samples t test). In patients with ongoing cCR, SULmax was lowest at 11 mo after nCRT. Conclusion: Serial 18F-FDG PET/CT might be a useful tool for detecting tumor recurrence during active surveillance. In patients with ongoing cCR, the lowest SULmax was reached at 11 mo after nCRT, suggesting that radiation-induced esophagitis had mostly resolved by that time. These findings warrant further evaluation in a larger cohort.

Project description:PurposeTo evaluate whether quantitative [18F]FDG-PET/CT assessment, including radiomic analysis of [18F]FDG-positive thyroid nodules, improved the preoperative differentiation of indeterminate thyroid nodules of non-Hürthle cell and Hürthle cell cytology.MethodsProspectively included patients with a Bethesda III or IV thyroid nodule underwent [18F]FDG-PET/CT imaging. Receiver operating characteristic (ROC) curve analysis was performed for standardised uptake values (SUV) and SUV-ratios, including assessment of SUV cut-offs at which a malignant/borderline neoplasm was reliably ruled out (≥ 95% sensitivity). [18F]FDG-positive scans were included in radiomic analysis. After segmentation at 50% of SUVpeak, 107 radiomic features were extracted from [18F]FDG-PET and low-dose CT images. Elastic net regression classifiers were trained in a 20-times repeated random split. Dimensionality reduction was incorporated into the splits. Predictive performance of radiomics was presented as mean area under the ROC curve (AUC) across the test sets.ResultsOf 123 included patients, 84 (68%) index nodules were visually [18F]FDG-positive. The malignant/borderline rate was 27% (33/123). SUV-metrices showed AUCs ranging from 0.705 (95% CI, 0.601-0.810) to 0.729 (0.633-0.824), 0.708 (0.580-0.835) to 0.757 (0.650-0.864), and 0.533 (0.320-0.747) to 0.700 (0.502-0.898) in all (n = 123), non-Hürthle (n = 94), and Hürthle cell (n = 29) nodules, respectively. At SUVmax, SUVpeak, SUVmax-ratio, and SUVpeak-ratio cut-offs of 2.1 g/mL, 1.6 g/mL, 1.2, and 0.9, respectively, sensitivity of [18F]FDG-PET/CT was 95.8% (95% CI, 78.9-99.9%) in non-Hürthle cell nodules. In Hürthle cell nodules, cut-offs of 5.2 g/mL, 4.7 g/mL, 3.4, and 2.8, respectively, resulted in 100% sensitivity (95% CI, 66.4-100%). Radiomic analysis of 84 (68%) [18F]FDG-positive nodules showed a mean test set AUC of 0.445 (95% CI, 0.290-0.600) for the PET model.ConclusionQuantitative [18F]FDG-PET/CT assessment ruled out malignancy in indeterminate thyroid nodules. Distinctive, higher SUV cut-offs should be applied in Hürthle cell nodules to optimize rule-out ability. Radiomic analysis did not contribute to the additional differentiation of [18F]FDG-positive nodules.Trial registration numberThis trial is registered with ClinicalTrials.gov: NCT02208544 (5 August 2014), https://clinicaltrials.gov/ct2/show/NCT02208544 .

Project description:ObjectivesTo assess the complementary value of human epidermal growth factor receptor 2 (HER2)-related biological tumor markers to clinico-radiomic models in predicting complete response to neoadjuvant chemoradiotherapy (NCRT) in esophageal cancer patients.MethodsExpression of HER2 was assessed by immunohistochemistry in pre-treatment tumor biopsies of 96 patients with locally advanced esophageal cancer. Five other potentially active HER2-related biological tumor markers in esophageal cancer were examined in a sub-analysis on 43 patients. Patients received at least four of the five cycles of chemotherapy and full radiotherapy regimen followed by esophagectomy. Three reference clinico-radiomic models based on 18F-FDG PET were constructed to predict pathologic response, which was categorized into complete versus incomplete (Mandard tumor regression grade 1 vs. 2-5). The complementary value of the biological tumor markers was evaluated by internal validation through bootstrapping.ResultsPathologic examination revealed 21 (22%) complete and 75 (78%) incomplete responders. HER2 and cluster of differentiation 44 (CD44), analyzed in the sub-analysis, were univariably associated with pathologic response. Incorporation of HER2 and CD44 into the reference models improved the overall performance (R2s of 0.221, 0.270, and 0.225) and discrimination AUCs of 0.759, 0.857, and 0.816. All models exhibited moderate to good calibration. The remaining studied biological tumor markers did not yield model improvement.ConclusionsIncorporation of HER2 and CD44 into clinico-radiomic prediction models improved NCRT response prediction in esophageal cancer. These biological tumor markers are promising in initial response evaluation.Key points• A multimodality approach, integrating independent genomic and radiomic information, is promising to improve prediction of γpCR in patients with esophageal cancer. • HER2 and CD44 are potential biological tumor markers in the initial work-up of patients with esophageal cancer. • Prediction models combining 18F-FDG PET radiomic features with HER2 and CD44 may be useful in the decision to omit surgery after neoadjuvant chemoradiotherapy in patients with esophageal cancer.

Project description:PurposeTo determine if quantitative features extracted from pretherapy fluorine 18 fluorodeoxyglucose (18F-FDG) PET/CT estimate prognosis in patients with locally advanced cervical cancer treated with chemoradiotherapy.Materials and methodsIn this retrospective study, PET/CT images and outcomes were curated from 154 patients with locally advanced cervical cancer, who underwent chemoradiotherapy from two institutions between March 2008 and June 2016, separated into independent training (n = 78; mean age, 51 years ± 13 [standard deviation]) and testing (n = 76; mean age, 50 years ± 10) cohorts. Radiomic features were extracted from PET, CT, and habitat (subregions with different metabolic characteristics) images that were derived by fusing PET and CT images. Parsimonious sets of these features were identified by the least absolute shrinkage and selection operator analysis and used to generate predictive radiomics signatures for progression-free survival (PFS) and overall survival (OS) estimation. Prognostic validation of the radiomic signatures as independent prognostic markers was performed using multivariable Cox regression, which was expressed as nomograms, together with other clinical risk factors.ResultsThe radiomics nomograms constructed with T stage, lymph node status, and radiomics signatures resulted in significantly better performance for the estimation of PFS (Harrell concordance index [C-index], 0.85 for training and 0.82 for test) and OS (C-index, 0.86 for training and 0.82 for test) compared with International Federation of Gynecology and Obstetrics staging system (C-index for PFS, 0.70 for training [P = .001] and 0.70 for test [P = .002]; C-index for OS, 0.73 for training [P < .001] and 0.70 for test [P < .001]), respectively.ConclusionPrognostic models were generated and validated from quantitative analysis of 18F-FDG PET/CT habitat images and clinical data, and may have the potential to identify the patients who need more aggressive treatment in clinical practice, pending further validation with larger prospective cohorts.Supplemental material is available for this article.© RSNA, 2020.

Dataset Information

Prediction of Non-Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer Patients with ¹⁸F-FDG PET Radiomics Based Machine Learning Classification.

Background

Methods

Results

Conclusion

Publications

Prediction of Non-Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer Patients with <sup>18</sup>F-FDG PET Radiomics Based Machine Learning Classification.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Dataset Information

Prediction of Non-Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer Patients with 18F-FDG PET Radiomics Based Machine Learning Classification.

Background

Methods

Results

Conclusion

Publications

Prediction of Non-Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer Patients with <sup>18</sup>F-FDG PET Radiomics Based Machine Learning Classification.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets

Prediction of Non-Response to Neoadjuvant Chemoradiotherapy in Esophageal Cancer Patients with ¹⁸F-FDG PET Radiomics Based Machine Learning Classification.