Purpose Accurate prognostic markers are urgently needed to identify diffuse large B-Cell lymphoma (DLBCL) patients at high risk of progression or relapse. Our purpose was to investigate the potential added value of baseline radiomics features to the international prognostic index (IPI) in predicting outcome after first-line treatment. Methods Three hundred seventeen newly diagnosed DLBCL patients were included. Lesions were delineated using a semi-automated segmentation method (standardized uptake value ≥ 4.0), and 490 radiomics features were extracted. We used logistic regression with backward feature selection to predict 2-year time to progression (TTP). The area under the curve (AUC) of the receiver operator characteristic curve was calculated to assess model performance. High-risk groups were defined based on prevalence of events; diagnostic performance was assessed using positive and negative predictive values. Results The IPI model yielded an AUC of 0.68. The optimal radiomics model comprised the natural logarithms of metabolic tumor volume (MTV) and of SUVpeak and the maximal distance between the largest lesion and any other lesion (Dmaxbulk, AUC 0.76). Combining radiomics and clinical features showed that a combination of tumor- (MTV, SUVpeak and Dmaxbulk) and patient-related parameters (WHO performance status and age > 60 years) performed best (AUC 0.79). Adding radiomics features to clinical predictors increased PPV with 15%, with more accurate selection of high-risk patients compared to the IPI model (progression at 2-year TTP, 44% vs 28%, respectively). Conclusion Prediction models using baseline radiomics combined with currently used clinical predictors identify patients at risk of relapse at baseline and significantly improved model performance. Trial registration number and date EudraCT: 2006–005,174-42, 01–08-2008.
Purpose Quantitative prostate-specific membrane antigen (PSMA) PET analysis may provide for non-invasive and objective risk stratification of primary prostate cancer (PCa) patients. We determined the ability of machine learning-based analysis of quantitative [ 18 F]DCFPyL PET metrics to predict metastatic disease or high-risk pathological tumor features. Methods In a prospective cohort study, 76 patients with intermediate- to high-risk PCa scheduled for robot-assisted radical prostatectomy with extended pelvic lymph node dissection underwent pre-operative [ 18 F]DCFPyL PET-CT. Primary tumors were delineated using 50–70% peak isocontour thresholds on images with and without partial-volume correction (PVC). Four hundred and eighty standardized radiomic features were extracted per tumor. Random forest models were trained to predict lymph node involvement (LNI), presence of any metastasis, Gleason score ≥ 8, and presence of extracapsular extension (ECE). For comparison, models were also trained using standard PET features (SUVs, volume, total PSMA uptake). Model performance was validated using 50 times repeated 5-fold cross-validation yielding the mean receiver-operator characteristic curve AUC. Results The radiomics-based machine learning models predicted LNI (AUC 0.86 ± 0.15, p < 0.01), nodal or distant metastasis (AUC 0.86 ± 0.14, p < 0.01), Gleason score (0.81 ± 0.16, p < 0.01), and ECE (0.76 ± 0.12, p < 0.01). The highest AUCs reached using standard PET metrics were lower than those of radiomics-based models. For LNI and metastasis prediction, PVC and a higher delineation threshold improved model stability. Machine learning pre-processing methods had a minor impact on model performance. Conclusion Machine learning-based analysis of quantitative [ 18 F]DCFPyL PET metrics can predict LNI and high-risk pathological tumor features in primary PCa patients. These findings indicate that PSMA expression detected on PET is related to both primary tumor histopathology and metastatic tendency. Multicenter external validation is needed to determine the benefits of using radiomics versus standard PET metrics in clinical practice. Electronic supplementary material The online version of this article (10.1007/s00259-020-04971-z) contains supplementary material, which is available to authorized users.
on behalf of the COLOR COLOR II study group jj Objective: The aim of this study was to evaluate oncological outcome for patients with and without anastomotic leakage after colon or rectal cancer surgery. Summary of Background Data: The role of anastomotic leakage in oncological outcome after colorectal cancer surgery is still topic of debate and impact on follow-up and consideration for further treatment remains unclear. Methods: Patients included in the international, multicenter, non-inferior, open label, randomized, controlled trials COLOR and COLOR II, comparing laparoscopic surgery for curable colon (COLOR) and rectal (COLOR II) cancer with open surgery, were analyzed. Patients operated by abdominoperineal excision were excluded. Both univariate and multivariate analyses were performed to investigate the impact of leakage on overall survival, diseasefree survival, local and distant recurrences, adjusted for possible confounders. Primary endpoints in the COLOR and COLOR II trial were disease-free survival and local recurrence at 3-year follow-up, respectively, and secondary endpoints included anastomotic leakage rate. Results: For colon cancer, anastomotic leakage was not associated with increased percentage of local recurrence or decreased disease-free-survival. For rectal cancer, an increase of local recurrences (13.3% vs 4.6%; hazard ratio 2.96; 95% confidence interval 1.38-6.34; P ¼ 0.005) and a decrease of disease-free survival (53.6% vs 70.9%; hazard ratio 1.67; 95% confidence interval 1.16-2.41; P ¼ 0.006) at 5-year follow-up were found in patients with anastomotic leakage.Conclusion: Short-term morbidity, mortality, and long-term oncological outcomes are negatively influenced by the occurrence of anastomotic leakage after rectal cancer surgery. For colon cancer, no significant effect was observed; however, due to low power, no conclusions on the influence of anastomotic leakage on outcomes after colon surgery could be reached. Clinical awareness of increased risk of local recurrence after anastomotic leakage throughout the follow-up is mandatory. Trial Registration: Registered with ClinicalTrials.gov, number NCT00387842 and NCT00297791.
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