scholarly journals Prediction of 5-year Progression-Free Survival in Advanced Nasopharyngeal Carcinoma with Pretreatment PET/CT using Multi-Modality Deep Learning-based Radiomics

2021 ◽  
Author(s):  
Bingxin Gu ◽  
Mingyuan Meng ◽  
Lei Bi ◽  
Jinman Kim ◽  
David Dagan Feng ◽  
...  
Keyword(s):  
Deep Learning ◽  
Medical Image Analysis ◽  
Progression Free Survival ◽  
Ct Images ◽  
Tnm Stage ◽  
Support Vector ◽  
Free Survival ◽  
Prognostic Performance ◽  
Pet Ct ◽  
Testing Error

Abstract Purpose Deep Learning-based Radiomics (DLR) has achieved great success on medical image analysis. In this study, we aimed to explore the capability of our proposed end-to-end multi-modality DLR model using pretreatment PET/CT images to predict 5-year Progression-Free Survival (PFS) in advanced NPC.Methods A total of 170 patients with pathological confirmed advanced NPC (TNM stage III or IVa) were enrolled in this study. A 3D Convolutional Neural Network (CNN), with two branches to process PET and CT separately, was optimized to extract deep features from pretreatment multi-modality PET/CT images and use the derived features to predict the probability of 5-year PFS. Optionally, TNM stage, as a high-level clinical feature, can be integrated into our DLR model to further improve prognostic performance. Results For a comparison between Conventional Radiomic (CR) and DLR, 1456 handcrafted features were extracted, and three top CR methods, Random Forest (RF) + RF (AUC = 0.796 ± 0.009, testing error = 0.267 ± 0.007), RF + Adaptive Boosting (AdaBoost) (AUC = 0.783 ± 0.011, testing error = 0.286 ± 0.009), and L1-Logistic Regression (L1-LOG) + Kernel Support Vector Machines (KSVM) (AUC = 0.769 ± 0.008, testing error = 0.298 ± 0.006), were selected as benchmarks from 54 combinations of 6 feature selection methods and 9 classification methods. Compared to the three CR methods, our multi-modality DLR models using both PET and CT, with or without TNM stage (named PCT or PC model), resulted in the highest prognostic performance (PCT model: AUC = 0.842 ± 0.034, testing error = 0.194 ± 0.029; PC model: AUC = 0.825 ± 0.041, testing error = 0.223 ± 0.035). Furthermore, the multi-modality PCT model outperformed single-modality DLR models using only PET and TNM stage (named PT model: AUC = 0.818 ± 0.029, testing error = 0.218 ± 0.024) or only CT and TNM stage (named CT model: AUC = 0.657 ± 0.055, testing error = 0.375 ± 0.048). Conclusion Our study identified potential radiomics-based prognostic model for survival prediction in advanced NPC, and suggests that DLR could serve as a tool for aiding in cancer management.

scholarly journals Progression Free Survival Prediction for Head and Neck Cancer using Deep Learning based on Clinical and PET-CT Imaging Data

2021 ◽  
Author(s):  
Mohamed A. Naser ◽  
Kareem A. Wahid ◽  
Abdallah Sherif Radwan Mohamed ◽  
Moamen Abobakr Abdelaal ◽  
Renjie He ◽  
...  
Keyword(s):  
Deep Learning ◽  
Head And Neck ◽  
Cross Validation ◽  
Progression Free Survival ◽  
Ct Images ◽  
Training Data ◽  
Learning Approaches ◽  
Test Set ◽  
Free Survival ◽  
Pet Ct

Determining progression-free survival (PFS) for head and neck squamous cell carcinoma (HNSCC) patients is a challenging but pertinent task that could help stratify patients for improved overall outcomes. PET/CT images provide a rich source of anatomical and metabolic data for potential clinical biomarkers that would inform treatment decisions and could help improve PFS. In this study, we participate in the 2021 HECKTOR Challenge to predict PFS in a large dataset of HNSCC PET/CT images using deep learning approaches. We develop a series of deep learning models based on the DenseNet architecture using a negative log-likelihood loss function that utilizes PET/CT images and clinical data as separate input channels to predict PFS in days. Internal model validation based on 10-fold cross-validation using the training data (N=224) yielded C-index values up to 0.622 (without) and 0.842 (with) censoring status considered in C-index computation, respectively. We then implemented model ensembling approaches based on the training data cross-validation folds to predict the PFS of the test set patients (N=101). External validation on the test set for the best ensembling method yielded a C-index value of 0.694. Our results are a promising example of how deep learning approaches can effectively utilize imaging and clinical data for medical outcome prediction in HNSCC, but further work in optimizing these processes is needed.

scholarly journals Radiomics Analysis of PET and CT Components of 18F-FDG PET/CT Imaging for Prediction of Progression-Free Survival in Advanced High-Grade Serous Ovarian Cancer

2021 ◽  
Vol 11 ◽  
Author(s):  
Xihai Wang ◽  
Zaiming Lu
Keyword(s):  
Clinical Features ◽  
Cox Regression ◽  
Progression Free Survival ◽  
Predictive Performance ◽  
Ct Images ◽  
Metabolic Parameters ◽  
P Value ◽  
Free Survival ◽  
Pet Ct ◽  
Fdg Pet Ct

ObjectiveTo investigate radiomics features extracted from PET and CT components of 18F-FDG PET/CT images integrating clinical factors and metabolic parameters of PET to predict progression-free survival (PFS) in advanced high-grade serous ovarian cancer (HGSOC).MethodsA total of 261 patients were finally enrolled in this study and randomly divided into training (n=182) and validation cohorts (n=79). The data of clinical features and metabolic parameters of PET were reviewed from hospital information system(HIS). All volumes of interest (VOIs) of PET/CT images were semi-automatically segmented with a threshold of 42% of maximal standard uptake value (SUVmax) in PET images. A total of 1700 (850×2) radiomics features were separately extracted from PET and CT components of PET/CT images. Then two radiomics signatures (RSs) were constructed by the least absolute shrinkage and selection operator (LASSO) method. The RSs of PET (PET_RS) and CT components(CT_RS) were separately divided into low and high RS groups according to the optimum cutoff value. The potential associations between RSs with PFS were assessed in training and validation cohorts based on the Log-rank test. Clinical features and metabolic parameters of PET images (PET_MP) with P-value <0.05 in univariate and multivariate Cox regression were combined with PET_RS and CT_RS to develop prediction nomograms (Clinical, Clinical+ PET_MP, Clinical+ PET_RS, Clinical+ CT_RS, Clinical+ PET_MP + PET_RS, Clinical+ PET_MP + CT_RS) by using multivariate Cox regression. The concordance index (C-index), calibration curve, and net reclassification improvement (NRI) was applied to evaluate the predictive performance of nomograms in training and validation cohorts.ResultsIn univariate Cox regression analysis, six clinical features were significantly associated with PFS. Ten PET radiomics features were selected by LASSO to construct PET_RS, and 1 CT radiomics features to construct CT_RS. PET_RS and CT_RS was significantly associated with PFS both in training (P <0.00 for both RSs) and validation cohorts (P=0.01 for both RSs). Because there was no PET_MP significantly associated with PFS in training cohorts. Only three models were constructed by 4 clinical features with P-value <0.05 in multivariate Cox regression and RSs (Clinical, Clinical+ PET_RS, Clinical+ CT_RS). Clinical+ PET_RS model showed higher prognostic performance than other models in training cohort (C-index=0.70, 95% CI 0.68-0.72) and validation cohort (C-index=0.70, 95% CI 0.66-0.74). Calibration curves of each model for prediction of 1-, 3-year PFS indicated Clinical +PET_RS model showed excellent agreements between estimated and the observed 1-, 3-outcomes. Compared to the basic clinical model, Clinical+ PET_MS model resulted in greater improvement in predictive performance in the validation cohort.ConclusionPET_RS can improve diagnostic accuracy and provide complementary prognostic information compared with the use of clinical factors alone or combined with CT_RS. The newly developed radiomics nomogram is an effective tool to predict PFS for patients with advanced HGSOC.

scholarly journals The Predictive Value of PET/CT for Post-Transplant Outcomes in T Cell Lymphoma

Blood ◽  
2018 ◽  
Vol 132 (Supplement 1) ◽  
pp. 2914-2914
Author(s):  
Nancy Kaddis ◽  
Eric D Jacobsen ◽  
Ailbhe O'Neill ◽  
Nikhil Ramaiya ◽  
Robert A. Redd
Keyword(s):  
Predictive Value ◽  
Progression Free Survival ◽  
Ct Images ◽  
Negative Group ◽  
Free Survival ◽  
Positive Group ◽  
Post Transplant ◽  
Pet Ct

Abstract Objective 18F-fluorodeoxyglucose (FDG)-positron emission tomography (PET) is used routinely for response assessment and treatment decision making in Hodgkin lymphoma and B cell non-Hodgkin lymphoma. The predictive value of PET/CT in patients with peripheral T-cell lymphomas (PTCL) is not well defined. We performed a retrospective single institution analysis to determine the utility of pre-transplant PET/CT to predict outcomes following autologous stem cell transplant (ASCT) for PTCL. Materials and Methods PET/CT patient population We screened the Dana-Farber Cancer Institute database for patients undergoing ASCT between 2005 and 2015 and identified 109 PTCL patients. Patients had PET/CT performed within 3 months prior to transplant and follow up PET/CT within one year of ASCT. 38 patients met the inclusion criteria (17 women, 21 men, mean age at transplant 56 years, SD ±14.6, range 22-73). Image interpretation The FDG-PET/CT images were reviewed on HERMES GOLD (Hermes Medical Solutions AB, Stockholm, Sweden) workstation by a radiologist (AON) blinded to clinical details. Pre-transplant PET/CT images were read initially and then one week later the post-transplant PET/CT images were read with the reader blinded to the pre-transplant PET/CT findings. The Deauville five-point scale was used for staging and assessment of treatment response and recurrence. A Deauville score of 3 or less was considered a complete response (CR). Results There was mean of 1.3 months between the initial PET/CT and transplant. Mean of 5 months between transplant and follow up PET/CT. A total of 30 patients had a CR on pre-transplant PET/CT. There were 8 patients with persistent sites of FDG uptake on PET/CT with Deauville 4 (n=4), Deauville 5 (n=2) consistent with partial response to treatment. Pre-transplant PET/CT did not correlate with long term survival outcomes including 3-year PFS in our data; a negative pre-transplant PET/CT was not associated with improved 3-year PFS as compared to a positive pre-transplant PET/CT. A total of 26 patients (68%) had no evidence of disease on post-transplant PET or negative post treatment PET/CT. Of those, 23 (88%) had a 3 -year progression free survival, 13 (50%) had a 5-year progression free survival, and 5 (19%) had died of recurrent disease at the time of our analysis. On post-transplant, a total of 12 patients had positive PET/CT with 6 achieving partial remission and 6 having progressive disease on post-transplant PET/CT. In terms of outcome, the 3-year PFS for the PET positive group was 42% (5/12). Of those, 2 (17%) had durable 5-year PFS with treatment after transplant while the other 10 (83%) eventually died of their disease. The 3-year PFS rate in the PET negative group was 88% (23/26) (95% CI: 70 - 98%) and 42% (5/12) (95% CI: 15 - 72%%) for PET positive group. The difference in the 3-year PFS in the PET negative group is significantly larger than that of the PET negative group (p<0.005). The 5-year PFS in the PET negative group was 50% (13/26) and 17% (2/12) for the PET positive group with a marginally significant difference (p=0.08) Conclusions: Patients with a negative post-transplant PET/CT had a 3-year PFS of 88% and 5-year PFS of 50% compared to a 3-year PFS of 42% and a 5-year PFS of 17% in patients with a positive post-transplant PET/CT. This suggests that post-transplant PET/CT is a clinically meaningful predictor of long-term disease-free survival. The PFS data in the patients with a negative post-transplant PET/CT compares favorably to that of patients not stratified by PET/CT in prospective trials including a 5-year PFS of 44% in the NLG-T-01 study which looked at 115 PTCL patients who underwent ASCT in the up-front setting (d'Amore F, et al. J Clin Oncol. 2012 Sep 1;30(25):3093-9). Our findings that negative pre-transplant PET/CT are not predictive of survival or associated with an improved 3-year PFS in comparison to positive pre-transplant PET/CT was in keeping with the findings of another retrospective analysis of 48 patients, which compared the 3-year PFS and OS of patients with positive and negative pre-transplant PET/CT studies (Shea L, et al. Leuk Lymphoma. 2015 January; 56(1): 256-259). Disclosures Jacobsen: Merck: Consultancy; Seattle Genetics: Consultancy.

scholarly journals Progression Free Survival and Predictor of Recurrence in DLBCL patients with Negative Interim 18FDG PET/CT Using Standardized Imaging and Reporting Protocols

2020 ◽  
Vol 21 (8) ◽  
pp. 2343-2348
Author(s):  
Maseeh uz Zaman ◽  
Nosheen Fatima ◽  
Areeba Zaman ◽  
Unaiza Zaman ◽  
Sidra Zaman ◽  
...  
Keyword(s):  
Progression Free Survival ◽  
Free Survival ◽  
18Fdg Pet ◽  
Pet Ct

scholarly journals Is interim 18F-fluoride PET/CT a predictor of outcomes after radium-223 therapy?

2019 ◽  
Vol 52 (1) ◽  
pp. 33-40 ◽  
Author(s):  
Elba Etchebehere ◽  
Ana Emília Brito ◽  
Kalevi Kairemo ◽  
Eric Rohren ◽  
John Araujo ◽  
...  
Keyword(s):  
Overall Survival ◽  
Standardized Uptake Value ◽  
Progression Free Survival ◽  
Tumor Burden ◽  
Free Survival ◽  
Radium 223 ◽  
Positron Emission ◽  
Pet Ct ◽  
Hormone Refractory ◽  
18F Fluoride

Abstract Objective: To determine whether an interim 18F-fluoride positron-emission tomography/computed tomography (PET/CT) study performed after the third cycle of radium-223 dichloride (223RaCl2) therapy is able to identify patients that will not respond to treatment. Materials and Methods: We retrospectively reviewed 34 histologically confirmed cases of hormone-refractory prostate cancer with bone metastasis in patients submitted to 223RaCl2 therapy. All of the patients underwent baseline and interim 18F-fluoride PET/CT studies. The interim study was performed immediately prior to the fourth cycle of 223RaCl2. The skeletal tumor burden-expressed as the total lesion fluoride uptake above a maximum standardized uptake value of 10 (TLF10)-was calculated for the baseline and the interim studies. The percent change in TLF10 between the baseline and interim studies (%TFL10) was calculated as follows: %TFL10 = interim TLF10 - baseline TLF10 / baseline TLF10. End points were overall survival, progression-free survival, and skeletal-related events. Results: The mean age of the patients was 72.4 ± 10.2 years (range, 43.3-88.8 years). The %TLF10 was not able to predict overall survival (p = 0.6320; hazard ratio [HR] = 0.753; 95% confidence interval [CI]: 0.236-2.401), progression-free survival (p = 0.5908; HR = 1.248; 95% CI: 0.557-2.797) nor time to a bone event (p = 0.5114; HR = 1.588; 95% CI: 0.399-6.312). Conclusion: The skeletal tumor burden on an interim 18F-fluoride PET/CT, performed after three cycles of 223RaCl2, is not able to predict overall survival, progression-free survival, or time to bone event, and should not be performed to monitor response at this time.

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