Prediction of local relapse and distant metastasis in patients with definitive chemoradiotherapy-treated cervical cancer by deep learning from [18F]-fluorodeoxyglucose positron emission tomography/computed tomography.

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.

[18]Fluorodeoxyglucose Positron Emission Tomography for the Textural Features of Cervical Cancer Associated with Lymph Node Metastasis and Histological Type.

BACKGROUND: In this study, we investigated the correlation between the lymph node (LN) status or histological types and textural features of cervical cancers on 18F-fluorodeoxyglucose positron emission tomography/computed tomography. METHODS: We retrospectively reviewed the imaging records of 170 patients with International Federation of Gynecology and Obstetrics stage IB-IVA cervical cancer. Four groups of textural features were studied in addition to the maximum standardized uptake value (SUVmax), metabolic tumor volume, and total lesion glycolysis (TLG). Moreover, we studied the associations between the indices and clinical parameters, including the LN status, clinical stage, and histology. Receiver operating characteristic curves were constructed to evaluate the optimal predictive performance among the various textural indices. Quantitative differences were determined using the Mann-Whitney U test. Multivariate logistic regression analysis was performed to determine the independent factors, among all the variables, for predicting LN metastasis. RESULTS: Among all the significant indices related to pelvic LN metastasis, homogeneity derived from the gray-level co-occurrence matrix (GLCM) was the sole independent predictor. By combining SUVmax, the risk of pelvic LN metastasis can be scored accordingly. The TLGmean was the independent feature of positive para-aortic LNs. Quantitative differences between squamous and nonsquamous histology can be determined using short-zone emphasis (SZE) from the gray-level size zone matrix (GLSZM). CONCLUSION: This study revealed that in patients with cervical cancer, pelvic or para-aortic LN metastases can be predicted by using textural feature of homogeneity from the GLCM and TLGmean, respectively. SZE from the GLSZM is the sole feature associated with quantitative differences between squamous and nonsquamous histology.

Correlation of pretreatment 18F-FDG PET tumor textural features with gene expression in pharyngeal cancer and implications for radiotherapy-based treatment outcomes.

PURPOSE: This study investigated the correlation of the matrix heterogeneity of tumors on 18F-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.

Immunohistochemical overexpression of hypoxia-induced factor 1α associated with slow reduction in 18fluoro-2-deoxy-D-glucose uptake for chemoradiotherapy in patients with pharyngeal cancer.

BACKGROUND: This study examined genomic factors associated with a reduction in 18fluoro-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 (SUVmax) of the interim PET-CT and the reduction ratio of the SUVmax (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 iSUVmax 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 SUVmax 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.

Early prediction of distant metastasis in patients with uterine cervical cancer treated with definitive chemoradiotherapy by deep learning using pretreatment [ 18 F]fluorodeoxyglucose positron emission tomography/computed tomography.

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.

Imaging prediction of KRAS mutation in patients with rectal cancer through deep metric learning using pretreatment [18F]Fluorodeoxyglucose positron emission tomography/computed tomography.

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.

Use of pretreatment metabolic tumour volumes to predict the outcome of pharyngeal cancer treated by definitive radiotherapy.

PURPOSE: The aim of the study was to investigate the predictive role of pretreatment metabolic volume (MTV) in pharyngeal cancer (PC) patients treated with definitive (chemo) radiotherapy. METHODS: This retrospective analysis enrolled 64 patients with PC treated with (chemo) radiotherapy. All patients received pretreatment fluorodeoxyglucose (FDG) positron emission tomography (PET)/CT. Four PET segmentation methods were used, namely applying an isocontour at a standardized uptake value (SUV) of either 2.5 or 3.0 (MTV2.5 and MTV3.0) or using fixed thresholds of either 40 or 50 % (MTV40 %, MTV50 %) of the maximum intratumoural FDG activity. Disease-free survival (DFS) and primary relapse-free survival (PRFS) were examined according to cutoffs of the median values for each MTV and the gross tumour volume (GTVp). Independent prognosticators were identified by Cox regression analysis. RESULTS: With a median follow-up of 24 months, 19 patients died, and 26 patients experienced tumour relapse at primary sites. Multivariate analysis of the DFS showed that MTV2.5 > 13.6 ml was the only predictor of relapse [p = 0.011, hazard ratio = 2.69, 95 % confidence interval (CI) 1.25-5.76]. The independent predictor for PRFS was MTV2.5 > 13.6 ml (p = 0.003, hazard ratio = 3.76, 95 % CI 1.57-8.92), whereas GTVp > 15.5 ml had a marginal impact on PRFS (p = 0.06, hazard ratio = 3.54, 95 % CI 0.97-11.85). Patients having tumours with MTV2.5 > 13.6 ml had a significantly inferior 2-year PRFS compared with patients who had lower MTV2.5 tumours (39 vs 72 %, respectively, p = 0.001). CONCLUSION: For PC patients treated with definitive (chemo)radiotherapy, pretreatment MTV2.5 volume achieved the best predictive value for primary recurrence, and the same value was also a prognosticator for DFS.

Detection of gastroesophageal reflux esophagitis using 2-fluoro-2-deoxy-d-glucose positron emission tomography.

BACKGROUND: Gastroesophageal reflux disease (GERD) is a common disease and a major upper gastrointestinal problem. The purpose of the present study is to evaluate the use of noninvasive 2-fluoro-2-deoxy-d-glucose positron emission tomography (FDG-PET) to detect gastroesophageal reflux esophagitis. MATERIALS AND METHODS: This is a retrospective study reviewing 408 healthy check-up subjects (169 females and 239 men), who underwent both FDG-PET and upper gastrointestinal endoscopy during September 2008 to December 2009. Quantitative analysis of FDG uptake in the distal part of the esophagus was performed by calculating the maximum standard uptake value (SUVmax). This indicated the degree of esophagitis. FDG-PET findings were compared with endoscopic (modified version of the Los Angeles classification) diagnoses as the gold standard. RESULTS: The SUVmax ranged from 1.30 to 3.40 in normal subjects and from 1.30 to 4.00 in subjects with gastroesophageal reflux esophagitis. In the esophagitis group, the SUVmax was 2.13 ± 0.42 in subjects with modified LA grade M, 2.21 ± 0.45 in subjects with LA grade A, and 2.48 ± 0.44 in subjects with LA grade B and C gastroesophageal reflux esophagitis. One-way ANOVA and post-hoc comparison with Bonferroni correction (P value = 0.003) identified statistical differences between the three groups. CONCLUSION: Noninvasive FDG-PET may be useful in the detection and evaluation of various degrees of gastroesophageal reflux esophagitis.

18F-Fluciclovine Uptake in Intramuscular Injecting Site of Antiandrogen.

ABSTRACT: 18F-fluciclovine, a radiolabeled amino acid analog, has been approved by US Food and Drug Administration for detecting lesions of biochemical recurrence of prostate adenocarcinoma with PET/CT. However, it is not specific for prostate cancer and has been found to be present in variety of malignant and benign etiologies. We herein present an interesting case of the incidental finding of increasing uptake of 18F-fluciclovine related to intramuscular injection of antiandrogen.

Prediction of All-Cause Mortality Based on Stress/Rest Myocardial Perfusion Imaging (MPI) Using Deep Learning: A Comparison between Image and Frequency Spectra as Input.

BACKGROUND: Cardiovascular management and risk stratification of patients is an important issue in clinics. Patients who have experienced an adverse cardiac event are concerned for their future and want to know the survival probability. METHODS: We trained eight state-of-the-art CNN models using polar maps of myocardial perfusion imaging (MPI), gender, lung/heart ratio, and patient age for 5-year survival prediction after an adverse cardiac event based on a cohort of 862 patients who had experienced adverse cardiac events and stress/rest MPIs. The CNN model outcome is to predict a patient's survival 5 years after a cardiac event, i.e., two classes, either yes or no. RESULTS: The best accuracy of all the CNN prediction models was 0.70 (median value), which resulted from ResNet-50V2, using image as the input in the baseline experiment. All the CNN models had better performance after using frequency spectra as the input. The accuracy increment was about 7~9%. CONCLUSIONS: This is the first trial to use pure rest/stress MPI polar maps and limited clinical data to predict patients' 5-year survival based on CNN models and deep learning. The study shows the feasibility of using frequency spectra rather than images, which might increase the performance of CNNs.

Differentiation Between Malignant and Benign Pulmonary Nodules by Using Automated Three-Dimensional High-Resolution Representation Learning With Fluorodeoxyglucose Positron Emission Tomography-Computed Tomography.

Background: The investigation of incidental pulmonary nodules has rapidly become one of the main indications for 18F-fluorodeoxyglucose (FDG) positron emission tomography (PET), currently combined with computed tomography (PET-CT). There is also a growing trend to use artificial Intelligence for optimization and interpretation of PET-CT Images. Therefore, we proposed a novel deep learning model that aided in the automatic differentiation between malignant and benign pulmonary nodules on FDG PET-CT. Methods: In total, 112 participants with pulmonary nodules who underwent FDG PET-CT before surgery were enrolled retrospectively. We designed a novel deep learning three-dimensional (3D) high-resolution representation learning (HRRL) model for the automated classification of pulmonary nodules based on FDG PET-CT images without manual annotation by experts. For the images to be localized more precisely, we defined the territories of the lungs through a novel artificial intelligence-driven image-processing algorithm, instead of the conventional segmentation method, without the aid of an expert; this algorithm is based on deep HRRL, which is used to perform high-resolution classification. In addition, the 2D model was converted to a 3D model. Results: All pulmonary lesions were confirmed through pathological studies (79 malignant and 33 benign). We evaluated its diagnostic performance in the differentiation of malignant and benign nodules. The area under the receiver operating characteristic curve (AUC) of the deep learning model was used to indicate classification performance in an evaluation using fivefold cross-validation. The nodule-based prediction performance of the model had an AUC, sensitivity, specificity, and accuracy of 78.1, 89.9, 54.5, and 79.4%, respectively. Conclusion: Our results suggest that a deep learning algorithm using HRRL without manual annotation from experts might aid in the classification of pulmonary nodules discovered through clinical FDG PET-CT images.

Neuroimaging findings in a brain with Niemann-Pick type C disease.

Niemann-Pick type C disease (NPC) is a rare autosomal recessive lipid storage disorder caused by impaired cellular functions in processing and transporting low-density lipoprotein-cholesterol. In this report, we present magnetic resonance imaging (MRI), magnetic resonance spectrography (MRS) and 18-fluoro-2-deoxyglucose positron emission tomography (PET) imaging results for a 22-year-old male NPC patient. The patient's two MRI studies (at age 19 years and 22 years) demonstrated progressive changes of brain atrophy that were more prominent at the frontal lobes, and hyperintense signals in bilateral parietal-occipital periventricular white matter. MRS (at age 19 years) revealed no significant decrease in N-acetyl aspartate/choline ratio in the left frontal central white matter. PET (at age 22 years) showed significant bilateral hypometabolism in the prefrontal cortex and dorsomedial thalamus, and hypermetabolism in the parietal-occipital white matter, lenticular nucleus of the basal ganglia, cerebellum and pons. The imaging findings noted by MRI, MRS and 18-fluoro-2-deoxyglucose PET offered a possible supplementary explanation for the clinical neurological symptoms of this NPC patient.

Lesion-Based Bone Metastasis Detection in Chest Bone Scintigraphy Images of Prostate Cancer Patients Using Pre-Train, Negative Mining, and Deep Learning.

This study aimed to explore efficient ways to diagnose bone metastasis early using bone scintigraphy images through negative mining, pre-training, the convolutional neural network, and deep learning. We studied 205 prostate cancer patients and 371 breast cancer patients and used bone scintigraphy data from breast cancer patients to pre-train a YOLO v4 with a false-positive reduction strategy. With the pre-trained model, transferred learning was applied to prostate cancer patients to build a model to detect and identify metastasis locations using bone scintigraphy. Ten-fold cross validation was conducted. The mean sensitivity and precision rates for bone metastasis location detection and classification (lesion-based) in the chests of prostate patients were 0.72 ± 0.04 and 0.90 ± 0.04, respectively. The mean sensitivity and specificity rates for bone metastasis classification (patient-based) in the chests of prostate patients were 0.94 ± 0.09 and 0.92 ± 0.09, respectively. The developed system has the potential to provide pre-diagnostic reports to aid in physicians' final decisions.

Prediction of Neoadjuvant Chemoradiotherapy Response in Rectal Cancer with Metric Learning Using Pretreatment 18F-Fluorodeoxyglucose Positron Emission Tomography.

OBJECTIVES: Neoadjuvant chemoradiotherapy (NCRT) followed by surgery is the mainstay of treatment for patients with locally advanced rectal cancer. Based on baseline 18F-fluorodeoxyglucose ([18F]-FDG)-positron emission tomography (PET)/computed tomography (CT), a new artificial intelligence model using metric learning (ML) was introduced to predict responses to NCRT. PATIENTS AND METHODS: This study used the data of 236 patients with newly diagnosed rectal cancer; the data of 202 and 34 patients were for training and validation, respectively. All patients received pretreatment [18F]FDG-PET/CT, NCRT, and surgery. The treatment response was scored by Dworak tumor regression grade (TRG); TRG3 and TRG4 indicated favorable responses. The model employed ML combined with the Uniform Manifold Approximation and Projection for dimensionality reduction. A receiver operating characteristic (ROC) curve analysis was performed to assess the model's predictive performance. RESULTS: In the training cohort, 115 patients (57%) achieved TRG3 or TRG4 responses. The area under the ROC curve was 0.96 for the prediction of a favorable response. The sensitivity, specificity, and accuracy were 98.3%, 96.5%, and 97.5%, respectively. The sensitivity, specificity, and accuracy for the validation cohort were 95.0%, 100%, and 98.8%, respectively. CONCLUSIONS: The new ML model presented herein was used to determined that baseline 18F[FDG]-PET/CT images could predict a favorable response to NCRT in patients with rectal cancer. External validation is required to verify the model's predictive value.

Predicting pathological complete response in rectal cancer after chemoradiotherapy with a random forest using 18F-fluorodeoxyglucose positron emission tomography and computed tomography radiomics.

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 18F-fluorodeoxyglucose ([18F]FDG)-positron emission tomography (PET)/computed tomography (CT). METHODS: This study included 169 patients with newly diagnosed rectal cancer. All patients received 18F[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 18F[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.

Metabolic Imaging Phenotype Using Radiomics of [18F]FDG PET/CT Associated with Genetic Alterations of Colorectal Cancer.

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.

Textural features of cervical cancers on FDG-PET/CT associate with survival and local relapse in patients treated with definitive chemoradiotherapy.

We retrospectively reviewed the records of 142 patients with stage IB-IIIB cervical cancer who underwent 18F-FDG-PET/CT before external beam radiotherapy plus intracavitary brachytherapy and concurrent chemotherapy. The patients were divided into training and validation cohorts to confirm the reliability of predictors for recurrence. Kaplan-Meier analysis was performed and a Cox regression model was used to examine the effects of variables on overall survival (OS), progression-free survival (PFS), distant metastasis-free survival (DMFS), and pelvic relapse-free survival (PRFS). High gray-level run emphasis (HGRE) derived from gray-level run-length matrix most accurately and consistently predicted the presence of pelvic residual or recurrent tumors for both cohorts. In multivariate analysis, stages IIIA-IIIB (P = 0.001, hazard ratio [HR] = 4.07) and a low HGRE (P < 0.0001, HR = 4.34) were prognostic factors for low OS, whereas a low HGRE (P = 0.001, HR = 2.86) and nonsquamous cell histology (P = 0.003, HR = 2.76) were prognostic factors for inferior PFS. The nonsquamous cell histology (P < 0.0001, HR = 9.19) and a low HGRE (P = 0.001, HR = 4.69) were predictors for low PRFS. In cervical cancer patients receiving definitive chemoradiotherapy, pretreatment textural features on 18F-FDG-PET/CT can supplement the prognostic information.

Associations of Tumor PD-1 Ligands, Immunohistochemical Studies, and Textural Features in 18F-FDG PET in Squamous Cell Carcinoma of the Head and Neck.

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 (18F-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.

Predictive Value of 18F-FDG PET/CT for Vascular Invasion in Patients With Hepatocellular Carcinoma Before Liver Transplantation.

BACKGROUND: Positron emission tomography-computed tomography using F-fluorodeoxyglucose (F-FDG PET/CT) has been widely used in oncology. Vascular invasion of hepatocellular carcinoma (HCC) is associated with a high risk of tumor recurrence and low survival rates after liver transplantation (LT). This retrospective study determined the predictive value of F-FDG PET/CT for vascular invasion in patients with HCC before LT. METHODS: Sixty-five patients with HCC who underwent F-FDG PET/CT before LT were retrospectively included between January 2010 and July 2012. Volumes of interest (VOIs) were drawn for the tumors and normal liver tissues, and the standardized uptake value (SUV) in each VOI was measured. The maximal SUV (SUVmax) of the tumor, the ratio of tumor SUVmax to normal liver SUVmax (TSUVmax/LSUVmax), and the ratio of tumor SUVmax to normal liver SUVmean (TSUVmax/LSUVmean) were measured. The predictive value of metabolic parameters and conventional prognostic factors were analyzed. RESULTS: Vascular invasion was pathologically confirmed in 15 (23.08%) of 65 patients. Compared with patients without vascular invasion, patients with vascular invasion exhibited significantly higher serum alpha-fetoprotein (AFP) (P < 0.001), larger tumor size (P = 0.001), higher tumor number (P = 0.017), and higher SUVmax, TSUVmax/LSUVmax ratio, and higher TSUVmax/LSUVmean ratio (P = 0.008, P = 0.002, and P = 0.006, respectively). Univariate analysis revealed that SUVmax, TSUVmax/LSUVmax ratio, and TSUVmax/LSUVmean ratio of FDG PET/CT were significantly associated with vascular invasion in patients with HCC before LT (P = 0.019, P = 0.018, and P = 0.015, respectively). Multivariate analysis revealed that the TSUVmax/LSUVmean ratio of F-FDG PET/CT was a significant predictor of vascular invasion (P = 0.04) and that the TSUVmax/LSUVmax ratio of F-FDG PET/CT was an independent predictor of vascular invasion, although this finding demonstrated borderline statistical significance (P = 0.06) in patients with HCC before LT. CONCLUSIONS: According to the study results, the TSUVmax/LSUVmean ratio is an independent and significant predictor of vascular invasion, and the TSUVmax/LSUVmax ratio of F-FDG PET/CT is an independent predictor of vascular invasion, which is the main negative outcome after LT. Therefore, FDG PET/CT can provide vital information for determining prognosis and selecting an optimal candidate of LT for HCC.

Immunohistochemical biomarkers and volumetric parameters for predicting radiotherapy-based outcomes in patients with p16-negative pharyngeal cancer.

BACKGROUND: This study determined the prognostic effects of immunohistochemical biomarkers and volumetric parameters predicting radiotherapy-based treatment in patients with p16-negative squamous cell carcinoma of the oropharynx or hypopharynx. RESULTS: VEGF immunoreactivity > 2 and GLUT1 overexpression were prognostic factors for lower cause-specific survival. Moreover, both factors were associated with lower disease-free survival. The predictors of lower primary relapse-free survival were VEGF immunoreactivity > 2 and CT-based gross tumor volume > 16 mL. MATERIALS AND METHODS: Immunohistochemical biomarkers in pretreatment biopsy specimens from 60 patients with p16-negative cancer were analyzed using tissue microarrays. Computed tomography (CT)-based and biological tumor volumes were retrieved through fluorodeoxyglucose positron emission tomography-CT. Correlations of cause-specific, disease-free, and primary relapse-free survival with volumetric parameters and the immunohistochemical biomarker score were investigated. CONCLUSIONS: For patients with p16-negative pharyngeal cancer receiving radiotherapy, treatment outcomes can be stratified by VEGF and GLUT1 expression and CT-based gross tumor volume.