Relationship of FDG PET/CT imaging features with tumor immune microenvironment and prognosis in colorectal cancer: a retrospective study
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SUBMITTER: Lee J
PROVIDER: S-EPMC11020988 | biostudies-literature | 2024 Jan
REPOSITORIES: biostudies-literature
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Project description:The relationship between 2-deoxy-2-[18F]fluoro-D-glucose (FDG) positron emission tomography/computed tomography (PET/CT) textural features and histopathological findings in gastric cancer has not been fully evaluated. We investigated the relationship between the textural features of primary tumors on FDG PET/CT with histopathological findings and recurrence-free survival (RFS) in patients with advanced gastric cancer (AGC). Fifty-six patients with AGC who underwent FDG PET/CT for staging work-ups were retrospectively enrolled. Conventional parameters and the first- and second-order textural features of AGC were extracted using PET textural analysis. Upon histopathological analysis, along with histopathological classification and staging, the degree of CD4, CD8, and CD163 cell infiltrations and expressions of interleukin-6 and matrix-metalloproteinase-11 (MMP-11) in the primary tumor were assessed. The histopathological classification, Lauren classification, lymph node metastasis, CD8 T lymphocyte and CD163 macrophage infiltrations, and MMP-11 expression were significantly associated with the textural features of AGC. The multivariate survival analysis showed that increased FDG uptake and intra-tumoral metabolic heterogeneity were significantly associated with an increased risk of recurrence after curative surgery. Textural features of AGC on FDG PET/CT showed significant correlations with the inflammatory response in the tumor microenvironment and histopathological features of AGC, and they showed significant prognostic values for predicting RFS.
| S-EPMC9406203 | biostudies-literature
Project description:F-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) is a robust imaging modality used for staging multiple myeloma (MM) and assessing treatment responses. Herein, we extracted features from the FDG PET/CT images of MM patients using an artificial intelligence autoencoder algorithm that constructs a compressed representation of input data. We then evaluated the prognostic value of the image-feature clusters thus extracted. Conventional image parameters including metabolic tumor volume (MTV) were measured on volumes-of-interests (VOIs) covering only the bones. Features were extracted with the autoencoder algorithm on bone-covering VOIs. Supervised and unsupervised clustering were performed on image features. Survival analyses for progression-free survival (PFS) were performed for conventional parameters and clusters. In result, supervised and unsupervised clustering of the image features grouped the subjects into three clusters (A, B, and C). In multivariable Cox regression analysis, unsupervised cluster C, supervised cluster C, and high MTV were significant independent predictors of worse PFS. Supervised and unsupervised cluster analyses of image features extracted from FDG PET/CT scans of MM patients by an autoencoder allowed significant and independent prediction of worse PFS. Therefore, artificial intelligence algorithm-based cluster analyses of FDG PET/CT images could be useful for MM risk stratification.
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Project description:Objectives: To evaluate the ability of 18F-labeled fluoro-2-deoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) to predict survivorship of patients with bladder cancer (BC) and/or upper urinary tract carcinoma (UUTC). Materials: Data from patients who underwent FDG PET/CT for suspicion of recurrent urothelial carcinoma (UC) between 2007 and 2015 were retrospectively collected in a multicenter study. Disease management after the introduction of FDG PET/CT in the diagnostic algorithm was assessed in all patients. Kaplan-Meier and log-rank analysis were computed for survival assessment. A Cox regression analysis was used to identify predictors of recurrence and death, for BC, UUTC, and concomitant BC and UUTC. Results: Data from 286 patients were collected. Of these, 212 had a history of BC, 38 of UUTC and 36 of concomitant BC and UUTC. Patient management was changed in 114/286 (40%) UC patients with the inclusion of FDG PET/CT, particularly in those with BC, reaching 74% (n = 90/122). After a mean follow-up period of 21 months (Interquartile range: 4-28 mo.), 136 patients (47.4%) had recurrence/progression of disease. Moreover, 131 subjects (45.6%) died. At Kaplan-Meier analyses, patients with BC and positive PET/CT had a worse overall survival than those with a negative scan (log-rank < 0.001). Furthermore, a negative PET/CT scan was associated with a lower recurrence rate than a positive examination, independently from the primary tumor site. At multivariate analysis, in patients with BC and UUTC, a positive FDG PET/CT resulted an independent predictor of disease-free and overall survival (p < 0,01). Conclusions: FDG PET/CT has the potential to change patient management, particularly for patients with BC. Furthermore, it can be considered a valid survival prediction tool after primary treatment in patients with recurrent UC. However, a firm recommendation cannot be made yet. Further prospective studies are necessary to confirm our findings.
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Project description:BackgroundDiagnostic guidelines for isolated cardiac sarcoidosis (iCS) were first proposed in 2016, but there are few reports on the imaging and prognosis of iCS. This study aimed to evaluate the use of 18F-fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT) imaging in predicting iCS prognosis.Methods and resultsWe retrospectively reviewed the clinical and imaging data of 306 consecutive patients with suspected CS who underwent FDG PET/CT with a dedicated preparation protocol and included 82 patients (55 with systemic sarcoidosis including cardiac involvement [sCS], 27 with iCS) in the study. We compared the FDG PET/CT findings between the two groups. We examined the relationship between the CS type and the rate of adverse cardiac events. The iCS group had a significantly lower target-to-background ratio than the sCS group (P = 0.0010). The event-free survival rate was significantly lower in the iCS group than the sCS group (log-rank test, P < 0.0001). iCS was identified as an independent prognostic factor for adverse events (hazard ratio 3.82, P = 0.0059).ConclusioniCS was an independent prognostic factor for adverse cardiac events in patients with CS. The clinical diagnosis of iCS based on FDG PET/CT and new guidelines may be important.
| S-EPMC9984349 | biostudies-literature
Project description:IntroductionEpithelioid hemangioma (EH) is an intermediate locally aggressive tumor that consists of epithelioid cells and endothelial cell differentiation, which can occur at any age, but is most common between the ages of 30 and 40 years. EH in the thoracic spine is rare, and accurate diagnosis is critical to treatment planning. Our aim was to explore the imaging and clinical data of thoracic spine EH to improve the understanding of this rare disease.MethodsFrom January 1, 2018 to June 30, 2023, a database of thoracic spine masses was retrospectively reviewed. Five patients with histologically proven thoracic spine EH and complete imaging available were identified and analyzed. Computed tomography (CT) and magnetic resonance imaging (MRI) findings were evaluated separately by two radiologists with more than 10 years of experience. Positron emission tomography (PET)/CT was conducted by two nuclear medicine diagnostic technologists with at least 5 years of experience.ResultsThe patients included three male and two female patients aged 23 to 56 years (mean age was 38.4 ± 14.3 years). All patients underwent CT, MRI, and 18F-FDG PET/CT examination before treatment. Four patients were limited to one vertebral involvement, only one patient had multiple vertebral involvement, and all tumors involved the accessories, including one involving the posterior ribs. The maximum diameter of the tumor ranged from 2.7 to 4.3.ConclusionsCT, MRI, and 18F-FDG PET/CT findings of thoracic spine EH have certain characteristics, and understanding these imaging findings will help to obtain accurate diagnosis before surgery.
| S-EPMC11220569 | biostudies-literature
Project description:BackgroundTo develop and validate a survival model with clinico-biological features and 18F- FDG PET/CT radiomic features via machine learning, and for predicting the prognosis from the primary tumor of colorectal cancer.MethodsA total of 196 pathologically confirmed patients with colorectal cancer (stage I to stage IV) were included. Preoperative clinical factors, serum tumor markers, and PET/CT radiomic features were included for the recurrence-free survival analysis. For the modeling and validation, patients were randomly divided into the training (n = 137) and validation (n = 59) set, while the 78 stage III patients [training (n = 55), and validation (n = 23)] was divided for the further experiment. After selecting features by the log-rank test and variable-hunting methods, random survival forest (RSF) models were built on the training set to analyze the prognostic value of selected features. The performance of models was measured by C-index and was tested on the validation set with bootstrapping. Feature importance and the Pearson correlation were also analyzed.ResultsRadiomics signature (containing four PET/CT features and four clinical factors) achieved the best result for prognostic prediction of 196 patients (C-index 0.780, 95% CI 0.634-0.877). Moreover, four features (including two clinical features and two radiomics features) were selected for prognostic prediction of the 78 stage III patients (C-index was 0.820, 95% CI 0.676-0.900). K-M curves of both models significantly stratified low-risk and high-risk groups (P < 0.0001). Pearson correlation analysis demonstrated that selected radiomics features were correlated with tumor metabolic factors, such as SUVmean, SUVmax.ConclusionThis study presents integrated clinico-biological-radiological models that can accurately predict the prognosis in colorectal cancer using the preoperative 18F-FDG PET/CT radiomics in colorectal cancer. It is of potential value in assisting the management and decision making for precision treatment in colorectal cancer. Trial registration The retrospectively registered study was approved by the Ethics Committee of Fudan University Shanghai Cancer Center (No. 1909207-14-1910) and the data were analyzed anonymously.
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Project description:PurposeWe aimed to investigate the role of dual-phase FDG PET/CT in predicting the prognosis of patients with operable breast cancer.MethodsWe retrospectively reviewed the data of 998 patients who underwent radical treatment for breast cancer. Before treatment, PET/CT scans were performed 1 and 2 h after FDG administration. The maximum standardized uptake value (SUVmax) at both time points (SUVmax1 and SUVmax2) in the primary tumor and the retention index (RI) were calculated. PET recurrence risk (PET-RR) was determined based on the SUVmax1 and RI, and disease-free survival (DFS) and overall survival (OS) were evaluated according to the metabolic parameters. Propensity score matching was performed to adjust for biological characteristics.ResultsThe cut-off values for SUVmax1 and RI were 3 and 5%, respectively. The 5-year DFS was 94.9% and 86.1% (P < 0.001), and the 5-year OS was 97.6% and 92.7% (P < 0.001) in the low and high PET-RR groups, respectively. In multivariate analysis, high T status, nodal metastasis, the triple-negative subtype, and high PET-RR were independent factors of poor DFS. Propensity score matching revealed similar findings (5-year DFS 91.8% vs. 88.6%, P = 0.041 and 5-year OS 97.1% vs. 94.2%, P = 0.240, respectively).ConclusionThe combined parameters of SUVmax1 and RI on dual-phase FDG PET/CT were useful for predicting prognosis of patients with breast cancer. Patients with a high SUVmax1 and a negative time course of FDG uptake had a favorable prognosis.
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Project description:BackgroundThe clinical diagnosis of deep sternal wound infection (DSWI) is supported by imaging findings including 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG-PET/CT). To avoid misinterpretation due to normal post-surgery inflammation we assessed normal imaging findings in non-infected patients after sternotomy.MethodsThis is a prospective cohort study including non-infectious patients with sternotomy. All patients underwent 18F-FDG-PET/CT at either 5 weeks (group 1), 12 weeks (group 2) or 52 weeks (group 3) post-surgery. 18F-FDG uptake was scored visually in five categories and assessed quantitatively.ResultsA total of 44 patients were included. Sternal mean SUVmax was 7.34 (± 1.86), 5.22 (± 2.55) and 3.20 (± 1.80) in group 1, 2 and 3, respectively (p < 0.01). Sternal mean SUVmean was 3.84 (± 1.00), 2.69 (± 1.32) and 1.71 (± 0.98) in group 1, 2 and 3 (p < 0.01). All patients in group 1 had elevated uptake whereas group 2 and 3 showed 2/15 (13%) and 11/20 (55%) patients respectively with no elevated uptake. Group 3 still showed an elevated uptake pattern in in 9/20 (45%) and in 3/9 (33%) with a high-grade diffuse uptake pattern.ConclusionThis study shows significant lower sternal 18F-FDG at 55 weeks compared to 5 weeks post-sternotomy however elevated uptake patterns may persist.
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Project description:Early detection of gastrointestinal stromal tumor (GIST) liver metastases is crucial for the management and prognosis. In our experience, GIST liver metastases can display hypermetabolism on 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) and marked enhancement on magnetic resonance imaging (MRI), which are uncommon in other tumors before treatment. Most literature focus on the imaging evaluation, prognosis after treatment and less is known about imaging features on both imaging methods before treatment. This study analyzes the imaging features of newly diagnosed GIST liver metastases on 18F-FDG PET/CT and MRI, with goal of improving diagnostic accuracy. This retrospective study included 55 patients with pathological or radiographical confirmed GIST liver metastases who underwent PET/CT (n=29), MRI (n=22), or both methods (n=4). PET/CT and MRI interpretation including lesion's morphologic features, number, density or signal intensity, hemorrhage, cystic changes or necrosis, maximum standardized uptake value (SUVmax) of liver metastases and liver background on PET imaging, degree and pattern of enhancement on MRI were obtained by two experienced nuclear medicine physicians and two radiologists respectively. Data are presented as numbers, percentages, means ± standard deviations or median (interquartile range). The correlation between diameter and SUVmax of metastases, and primary tumor SUVmax and synchronous liver metastases SUVmax were analyzed by Spearman's rank test. On PET/CT visual analysis, 38.9%, 23.9%, and 37.2% of lesions showed significant hypermetabolism, slightly higher metabolism, and equal or lower metabolism than liver, respectively. There was a weak correlation between the diameter and SUVmax of liver metastases (rs =0.370, P<0.001), and a moderate correlation between SUVmax of synchronous liver metastases and the primary tumors (rs =0.492, P<0.001). On contrast-enhanced MRI, 90.8% of lesions showed heterogeneous enhancement in the arterial phase with the variable presentation, and 74.3% had different enhancement patterns between margins and intratumoral parenchyma. Liver lesions in GIST displaying significant, slight hypermetabolism on 18F-FDG PET/CT, marked or heterogeneous gradual enhancement within the intratumoral parenchyma with ring-like enhancement on MRI may denote the diagnosis of liver metastasis. However, GIST liver metastases may also display equal or lower metabolism than liver parenchyma on PET, making small lesions more difficult to diagnose.
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Project description:We propose a fully automatic multi-task Bayesian model, named Bayesian Sequential Network (BSN), for predicting high-grade (Gleason ≥ 8) prostate cancer (PCa) prognosis using pre-prostatectomy FDG-PET/CT images and clinical data. BSN performs one classification task and five survival tasks: predicting lymph node invasion (LNI), biochemical recurrence-free survival (BCR-FS), metastasis-free survival, definitive androgen deprivation therapy-free survival, castration-resistant PCa-free survival, and PCa-specific survival (PCSS). Experiments are conducted using a dataset of 295 patients. BSN outperforms widely used nomograms on all tasks except PCSS, leveraging multi-task learning and imaging data. BSN also provides automated prostate segmentation, uncertainty quantification, personalized feature-based explanations, and introduces dynamic predictions, a novel approach that relies on short-term outcomes to refine long-term prognosis. Overall, BSN shows great promise in its ability to exploit imaging and clinicopathological data to predict poor outcome patients that need treatment intensification with loco-regional or systemic adjuvant therapy for high-risk PCa.
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