PET/CT in Cancers Outcomes Prediction

Objectives: Accurate outcome prediction is important for making informed clinical decisions in cancer treatment. In this study, we assessed the feasibility of using changes in radiomic features over time (Delta radiomics: absolute and relative) following chemotherapy, to predict relapse/progression and time to progression (TTP) of primary mediastinal large B-cell lymphoma (PMBCL) patients. Material and Methods: Given the lack of standard staging PET scans until 2011, only 31 out of 103 PMBCL patients in our retrospective study had both pre-treatment and end-of-treatment (EoT) scans. Consequently, our radiomics analysis focused on these 31 patients who underwent [¹⁸F]FDG PET-CT scans before and after R-CHOP chemotherapy. Expert manual lesion segmentation was conducted on their scans for delta radiomics analysis, along with an additional 19 EoT scans, totaling 50 segmented scans for single time point analysis. Radiomics features (on PET and CT), along with maximum and mean standardized uptake values (SUVmax and SUVmean), total metabolic tumor volume (TMTV), tumor dissemination (Dmax), total lesion glycolysis (TLG), and the area under the curve of cumulative standardized uptake value-volume histogram (AUC-CSH) were calculated. We additionally applied longitudinal analysis using radial mean intensity (RIM) changes. For prediction of relapse/progression, we utilized the individual coefficient approximation for risk estimation (ICARE) and machine learning (ML) techniques (K-Nearest Neighbor (KNN), Linear Discriminant Analysis (LDA), and Random Forest (RF)) including sequential feature selection (SFS) following correlation analysis for feature selection. For TTP, ICARE and CoxNet approaches were utilized. In all models, we used nested cross-validation (CV) (with 10 outer folds and 5 repetitions, along with 5 inner folds and 20 repetitions) after balancing the dataset using Synthetic Minority Oversampling TEchnique (SMOTE). Results: To predict relapse/progression using Delta radiomics between the baseline (staging) and EoT scans, the best performances in terms of accuracy and F1 score (F1 score is the harmonic mean of precision and recall, where precision is the ratio of true positives to the sum of true positives and false positives, and recall is the ratio of true positives to the sum of true positives and false negatives) were achieved with ICARE (accuracy = 0.81 ± 0.15, F1 = 0.77 ± 0.18), RF (accuracy = 0.89 ± 0.04, F1 = 0.87 ± 0.04), and LDA (accuracy = 0.89 ± 0.03, F1 = 0.89 ± 0.03), that are higher compared to the predictive power achieved by using only EoT radiomics features. For the second category of our analysis, TTP prediction, the best performer was CoxNet (LASSO feature selection) with c-index = 0.67 ± 0.06 when using baseline + Delta features (inclusion of both baseline and Delta features). The TTP results via Delta radiomics were comparable to the use of radiomics features extracted from EoT scans for TTP analysis (c-index = 0.68 ± 0.09) using CoxNet (with SFS). The performance of Deauville Score (DS) for TTP was c-index = 0.66 ± 0.09 for n = 50 and 0.67 ± 03 for n = 31 cases when using EoT scans with no significant differences compared to the radiomics signature from either EoT scans or baseline + Delta features (p-value> 0.05). Conclusion: This work demonstrates the potential of Delta radiomics and the importance of using EoT scans to predict progression and TTP from PMBCL [¹⁸F]FDG PET-CT scans. Full article

We analyzed whether preoperative ¹⁸F-FDG PET/CT adds to conventional primary staging in patients with presumed non-metastatic colonic cancer (CC). The prognostic role of ¹⁸F-FDG uptake in the primary tumor was evaluated after a mean follow-up of 15 years. Patients with a new diagnosis of presumed localized CC were prospectively enrolled and underwent presurgical ¹⁸F-FDG PET/CT. For each colon lesion, SUVmax, SUVpeak, TLG, and MTV were assessed and tested as prognostic factors. Forty-eight patients were included. Post-surgery pathology identified a total of 103 colon lesions, including 58 invasive adenocarcinomas, 4 in situ adenocarcinomas, 3 adenomas with high-grade dysplasia, and 38 adenomas with low-grade dysplasia. Per lesion sensitivity, specificity, positive (PPVs) and negative predictive values (NPVs) for colonic primary tumor detection were 78%, 97%, 98%, and 73% for conventional workup, and 94%, 87%, 92%, and 89% for ¹⁸F-FDG PET/CT. Only sensitivity was significantly different between ¹⁸F-FDG PET/CT and conventional workup. PET detected an additional ten pathological colonic lesions in seven patients. SUVmax, SUVpeak, and TLG showed significant differences between invasive adenocarcinomas, in situ adenocarcinomas, and high-grade dysplasia compared to low-grade dysplasia. There was a statistically significant difference between pT1-pT2 and pT3-pT4 adenocarcinomas. On patient-based analysis, sensitivity, specificity, PPV, and NPV for nodal staging were 22%, 84%, 44%, and 65% for CECT, and 33%, 90%, 67%, and 70% for ¹⁸F-FDG PET/CT, without a statistically significant difference. PET/CT also identified unknown metastatic spread and one synchronous lung cancer in four patients. Overall, ¹⁸F-FDG PETCT had an additional diagnostic value in 11 out of 48 patients (23%). ¹⁸F-FDG uptake of the primary tumor did not predict nodal or distant metastases. The difference in disease-free survival categorized by median SUVmax, SUVpeak, TLG, and MTV was not significant. Finally, preoperative ¹⁸F-FDG PET/CT is valuable in detecting potential colon lesions not visualized by conventional workups, especially in cases of incomplete colonoscopy. It effectively highlights distant metastases but exhibits limitations for N staging. Mainly due to the relatively small sample size, the quantitative analysis of ¹⁸F-FDG uptake in the primary tumor did not reveal any association with recurrence or disease-free survival, adding no significant prognostic information. Full article