Search

Objectives: To predict KRAS mutation in rectal cancer (RC) through computer vision of [18F]fluorodeoxyglucose (18F‐FDG) positron emission tomography (PET)/computed tomography (CT) by using metric learning (ML). Methods: This study included 160 patients with RC who had undergone preoperative PET/CT. KRAS mutation was identified through polymerase chain reaction analysis. This model combined ML with the deep‐learning framework to analyze PET data with or without CT images. The Batch Balance Wrapper framework and K‐fold cross‐validation were employed during the learning process. A receiver operating characteristic (ROC) curve analysis was performed to assess the model's predictive performance. Results: Genetic alterations in KRAS were identified in 82 (51%) tumors. Both PET and CT images were used, and the proposed model had an area under the ROC curve of 0.836 for its ability to predict a mutation status. The sensitivity, specificity, and accuracy were 75.3%, 79.3%, and 77.5%, respectively. When PET images alone were used, the area under the curve was 0.817, whereas the sensitivity, specificity, and accuracy were 73.2%, 79.6%, and 76.2%, respectively. Conclusions: The ML model presented herein revealed that baseline 18F‐FDG PET/CT images could provide supplemental information to determine KRAS mutation in RC. Additional studies are required to maximize the predictive accuracy. Advances in knowledge: The results of the ML model presented herein indicate that baseline 18F‐FDG PET/CT images could provide supplemental information for determining KRAS mutation in RC. The predictive accuracy of the model was 77.5% when both image types were used and 76.2% when PET images alone were used. Additional studies are required to maximize the predictive accuracy. [ABSTRACT FROM AUTHOR]

Background: We designed a deep learning model for assessing 18F-FDG PET/CT for early prediction of local and distant failures for patients with locally advanced cervical cancer.Methods: All 142 patients with cervical cancer underwent 18F-FDG PET/CT for pretreatment staging and received allocated treatment. To augment the amount of image data, each tumor was represented as 11 slice sets each of which contains 3 2D orthogonal slices to acquire a total of 1562 slice sets. In each round of k-fold cross-validation, a well-trained proposed model and a slice-based optimal threshold were derived from a training set and used to classify each slice set in the test set into the categories of with or without local or distant failure. The classification results of each tumor were aggregated to summarize a tumor-based prediction result.Results: In total, 21 and 26 patients experienced local and distant failures, respectively. Regarding local recurrence, the tumor-based prediction result summarized from all test sets demonstrated that the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 71%, 93%, 63%, 95%, and 89%, respectively. The corresponding values for distant metastasis were 77%, 90%, 63%, 95%, and 87%, respectively.Conclusion: This is the first study to use deep learning model for assessing 18F-FDG PET/CT images which is capable of predicting treatment outcomes in cervical cancer patients.Key Points: • This is the first study to use deep learning model for assessing 18 F-FDG PET/CT images which is capable of predicting treatment outcomes in cervical cancer patients. • All 142 patients with cervical cancer underwent 18 F-FDG PET/CT for pretreatment staging and received allocated treatment. To augment the amount of image data, each tumor was represented as 11 slice sets each of which contains 3 2D orthogonal slices to acquire a total of 1562 slice sets. • For local recurrence, all test sets demonstrated that the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 71%, 93%, 63%, 95%, and 89%, respectively. The corresponding values for distant metastasis were 77%, 90%, 63%, 95%, and 87%, respectively. [ABSTRACT FROM AUTHOR]

Purpose: To understand the association between genetic mutations and radiomics of 2-deoxy-2-[18F]fluoro-D-glucose ([18F]FDG) positron emission tomography (PET)/x-ray computed tomography (CT) in patients with colorectal cancer (CRC).Procedures: This study included 74 CRC patients who had undergone preoperative [18F]FDG PET/CT. A total of 65 PET/CT-related features including intensity, volume-based, histogram, and textural features were calculated. High-resolution melting methods were used for genetic mutation analysis.Results: Genetic mutants were found in 21 KRAS tumors (28 %), 31 TP53 tumors (42 %), and 17 APC tumors (23 %). Tumors with a mutated KRAS had an increased value at the 25th percentile of maximal standardized uptake value (SUVmax) within their metabolic tumor volume (MTV) (P < .0001; odds ratio [OR] 1.99; 95 % confidence interval [CI] 1.37-2.90) and their contrast from the gray-level cooccurrence matrix (P = .005; OR 1.52; 95 % CI 1.14-2.04). A mutated TP53 was associated with an increased value of short-run low gray-level emphasis derived from the gray-level run length matrix (P = .001; OR 243006.0; 95 % CI 59.2-996,872,313). APC mutants exhibited lower low gray-level zone emphasis derived from the gray-level zone length matrix (P = .006; OR < .0001; 95 % CI 0.000-0.22).Conclusion: PET/CT-derived radiomics can provide supplemental information to determine KRAS, TP53, and APC genetic alterations in CRC. [ABSTRACT FROM AUTHOR]

Purpose: This study investigated the correlation of the matrix heterogeneity of tumors on F-fluorodeoxyglucose positron emission tomography-computed tomography (PET-CT) with gene-expression profiling in patients with pharyngeal cancer and determined the prognostic factors for radiotherapy-based treatment outcomes. Methods: We retrospectively reviewed the records of 57 patients with stage III-IV oropharyngeal or hypopharyngeal cancer who had completed definitive therapy. Four groups of the textural features as well as 31 indices were studied in addition to maximum standard uptake value, metastatic tumor volume, and total lesion glycolysis. Immunohistochemical data from pretreatment biopsy specimens ( Glut1, CAIX, VEGF, HIF-1α, EGFR, Ki-67, Bcl-2, CLAUDIN-4, YAP-1, c-Met, and p16) were analyzed. The relationships between the indices and genomic expression were studied, and the robustness of various textural features relative to cause-specific survival and primary relapse-free survival was analyzed. Results: The overexpression of VEGF was positively associated with the increased values of the matrix heterogeneity obtained using gray-level nonuniformity for zone (GLNUz) and run-length nonuniformity (RLNU). Advanced T stage ( p = 0.01, hazard ratio [HR] = 3.38), a VEGF immunoreactive score of >2 ( p = 0.03, HR = 2.79), and a higher GLNUz value ( p = 0.04, HR = 2.51) were prognostic factors for low cause-specific survival, whereas advanced T stage, a HIF-1α staining percentage of ≥80%, and a higher GLNUz value were prognostic factors for low primary-relapse free survival. Conclusions: The overexpression of VEGF was associated with the increased matrix index of GLNUz and RLNU. For patients with pharyngeal cancer requiring radiotherapy, the treatment outcome can be stratified according to the textural features, T stage, and biomarkers. [ABSTRACT FROM AUTHOR]

Background: This study examined genomic factors associated with a reduction in fluoro-2-deoxy- D-glucose (FDG) uptake during positron emission tomography-computed tomography (PET-CT) for definitive chemoradiotherapy (CRT) in patients with pharyngeal cancer. Methods: The pretreatment and interim PET-CT images of 25 patients with advanced pharyngeal cancers receiving definitive CRT were prospectively evaluated. The maximum standardized uptake value (SUV) of the interim PET-CT and the reduction ratio of the SUV (SRR) between the two images were measured. Genomic data from pretreatment incisional biopsy specimens ( SLC2A1, CAIX, VEGF, HIF1A, BCL2, Claudin-4, YAP1, MET, MKI67, and EGFR) were analyzed using tissue microarrays. Differences in FDG uptake and SRRs between tumors with low and high gene expression were examined using the Mann-Whitney test. Cox regression analysis was performed to examine the effects of variables on local control. Results: The SRR of the primary tumors (SRR-P) was 0.59 ± 0.31, whereas the SRR of metastatic lymph nodes (SRR-N) was 0.54 ± 0.32. Overexpression of HIF1A was associated with a high iSUV of the primary tumor ( P < 0.001) and neck lymph node ( P = 0.04) and a low SRR-P ( P = 0.02). Multivariate analysis revealed that patients who had tumors with low SRR-P or high HIF1A expression levels showed inferior local control. Conclusion: In patients with pharyngeal cancer requiring CRT, HIF1A overexpression was positively associated with high interim SUV or a slow reduction in FDG uptake. Prospective trials are needed to determine whether the local control rate can be stratified using the HIF1A level as a biomarker and SRR-P. [ABSTRACT FROM AUTHOR]

Hepatocellular carcinoma (HCC) is a disease usually diagnosed in its advanced-stage, and is frequently not amenable to curative surgical treatment. Also, HCC is resistant to chemotherapy and less vulnerable to radiation therapy compared to normal hepatic parenchyma. Both of these facts render the efficacy of adjuvant and palliative treatments problematic. Selective internal radiation therapy (SIRT) with Y-bearing microspheres is characterized by preferentially delivering substantially high doses of radiation to a liver tumor dose simultaneously limiting the damage to its non-tumorous cells, providing an opportunity for effective local tumor control and even tumor regression therapy. The current article reviews the specific characters, dosimetry, possible applications, and special considerations toward the pre-existing radiation therapy of Y microsphere SIRT in treating HCC. [ABSTRACT FROM AUTHOR]

Objectives/Hypothesis The study aimed to investigate the prognostic effects of interim 18fluoro-2-deoxy-D-glucose positron emission tomography/computed tomography (PET/CT) during definitive radiotherapy (RT) or chemoradiotherapy (CRT) in patients with head and neck cancer. Study Design This is a prospectively treatment study. Methods The pretreatment and interim PET/CT images of 51 patients with advanced pharyngeal cancers receiving definitive RT/CRT were evaluated prospectively. The interim PET/CT images were taken at a cumulative RT dose ranging from 41.4 to 46.8 Gy. The maximum standardized uptake value (SUVm) of the interim PET/CT and the reduction ratio of the SUVm (SRR) between the two images were measured. The differences between patients with or without local failures were examined using the Mann-Whitney test. Overall survival (OS), disease-free survival (DFS), and primary and nodal relapse-free survival rates were calculated using the Kaplan-Meier method. Independent prognosticators were identified using Cox regression analysis. Results After a median follow-up duration of 23 months, a higher interim SUVm was associated with local failures. Conducting a multivariate analysis revealed that a SUVmax reduction ratio of primary tumor (SRR-P) < 0.64 was associated with the inferior OS (hazard ratio 2.64; P =0.035) and DFS (hazard ratio 2.33; P = 0.045). Patients who had tumors with an SRR-P < 0.64 had a considerably lower 2-year OS and DFS compared with those who had SRR-P ≥ 0.64 (47% vs. 66%; 41% vs. 64%). Conclusion A higher interim SUVm was associated with local recurrence. In addition, patients with a lower SRR-P should be considered to be at risk of primary failure. Level of Evidence 4. Laryngoscope, 124:2732-2738, 2014 [ABSTRACT FROM AUTHOR]

Objectives: A deep learning (DL) model using image data from pretreatment [ 18 F]fluorodeoxyglucose ([ 18 F] FDG)-PET or computed tomography (CT) augmented with a novel imaging augmentation approach was developed for the early prediction of distant metastases in patients with locally advanced uterine cervical cancer., Methods: This study used baseline [18F]FDG-PET/CT images of newly diagnosed uterine cervical cancer patients. Data from 186 to 25 patients were analyzed for training and validation cohort, respectively. All patients received chemoradiotherapy (CRT) and follow-up. PET and CT images were augmented by using three-dimensional techniques. The proposed model employed DL to predict distant metastases. Receiver operating characteristic (ROC) curve analysis was performed to measure the model's predictive performance., Results: The area under the ROC curves of the training and validation cohorts were 0.818 and 0.830 for predicting distant metastasis, respectively. In the training cohort, the sensitivity, specificity, and accuracy were 80.0%, 78.0%, and 78.5%, whereas, the sensitivity, specificity, and accuracy for distant failure were 73.3%, 75.5%, and 75.2% in the validation cohort, respectively., Conclusion: Through the use of baseline [ 18 F]FDG-PET/CT images, the proposed DL model can predict the development of distant metastases for patients with locally advanced uterine cervical cancer treatment by CRT. External validation must be conducted to determine the model's predictive performance., (Copyright © 2023 Wolters Kluwer Health, Inc. All rights reserved.)

Objectives: To predict KRAS mutation in rectal cancer (RC) through computer vision of [18F]fluorodeoxyglucose (18F-FDG) positron emission tomography (PET)/computed tomography (CT) by using metric learning (ML)., Methods: This study included 160 patients with RC who had undergone preoperative PET/CT. KRAS mutation was identified through polymerase chain reaction analysis. This model combined ML with the deep-learning framework to analyze PET data with or without CT images. The Batch Balance Wrapper framework and K-fold cross-validation were employed during the learning process. A receiver operating characteristic (ROC) curve analysis was performed to assess the model's predictive performance., Results: Genetic alterations in KRAS were identified in 82 (51%) tumors. Both PET and CT images were used, and the proposed model had an area under the ROC curve of 0.836 for its ability to predict a mutation status. The sensitivity, specificity, and accuracy were 75.3%, 79.3%, and 77.5%, respectively. When PET images alone were used, the area under the curve was 0.817, whereas the sensitivity, specificity, and accuracy were 73.2%, 79.6%, and 76.2%, respectively., Conclusions: The ML model presented herein revealed that baseline 18F-FDG PET/CT images could provide supplemental information to determine KRAS mutation in RC. Additional studies are required to maximize the predictive accuracy., Advances in Knowledge: The results of the ML model presented herein indicate that baseline 18F-FDG PET/CT images could provide supplemental information for determining KRAS mutation in RC.The predictive accuracy of the model was 77.5% when both image types were used and 76.2% when PET images alone were used. Additional studies are required to maximize the predictive accuracy.

Background: Neoadjuvant chemoradiotherapy (NCRT) followed by surgery is the standard treatment for patients with locally advanced rectal cancer. This study developed a random forest (RF) model to predict pathological complete response (pCR) based on radiomics derived from baseline 18 F-fluorodeoxyglucose ([ 18 F]FDG)-positron emission tomography (PET)/computed tomography (CT)., Methods: This study included 169 patients with newly diagnosed rectal cancer. All patients received 18 F[FDG]-PET/CT, NCRT, and surgery. In total, 68 radiomic features were extracted from the metabolic tumor volume. The numbers of splits in a decision tree and trees in an RF were determined based on their effects on predictive performance. Receiver operating characteristic curve analysis was performed to evaluate predictive performance and ascertain the optimal threshold for maximizing prediction accuracy., Results: After NCRT, 22 patients (13%) achieved pCR, and 42 features that could differentiate tumors with pCR were used to construct the RF model. Six decision trees and seven splits were suitable. Accordingly, the sensitivity, specificity, positive predictive value, negative predictive value, and accuracy were 81.8%, 97.3%, 81.8%, 97.3%, and 95.3%, respectively., Conclusions: By using an RF, we determined that radiomics derived from baseline 18 F[FDG]-PET/CT could accurately predict pCR in patients with rectal cancer. Highly accurate and predictive values can be achieved but should be externally validated., Competing Interests: Conflicts of Interest: The authors have no conflicts of interest to declare., (2020 Annals of Translational Medicine. All rights reserved.)

To know tumor PD-L1 expression through IHC or the FDG-PET related radiomics, we investigated the association between programmed cell death protein 1 ligand (PD-L1) expression and immunohistochemical (IHC) biomarkers or textural features of 18F-fluoro-2-deoxdeoxyglucose positron emission tomography ( 18 F-FDG PET) in 53 oropharyngeal or hypopharyngeal cancer patients who were ready to undergo radiotherapy-based treatment. Differences in textural features or biomarkers between tumors with and without PD-L1 expression were tested using a Mann-Whitney U test. The predicted values for PD-L1 expression were examined using logistic regression analysis. The mean percentages of tumor PD-L1 expression were 6.2 ± 13.5. Eighteen tumors had PD-L1 expression ≥5%, whereas 30 tumors ≥1%. Using a 5% cutoff, the p16 staining percentage and the textural index of correlation were two factors associated with PD-L1 expression. The odds ratios (ORs) were 17.00 (p = 0.028) and 0.009 (p = 0.015), respectively. When dichotomizing PD-L1 at 1%, the p16 and Ki-67 staining percentages were two predictors for PD-L1 expression with ORs of 11.41 (p = 0.035) and 757.77 (p = 0.045). p16 and Ki-67 staining percentages and several PET/CT-derived textural features can provide supplemental information to determine tumor PD-L1 expression in HNCs.