Misdiagnosis of a Drain-site Hernia Containing Fallopian Tube Fimbria on 18F-FDG PET/CT.

In a 55-year-old woman with sigmoid colon cancer, a subcutaneous mass in the left lower abdomen was incidentally found and gradually enlarged. For further diagnosis and staging, an 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography scan was performed, which revealed a subcutaneous mass in the left lower abdomen with mild uptake of 18F-FDG, suggesting the possibility of metastasis. However, post-surgery and pathological confirmation, this mass was diagnosed as a drain-site hernia containing fallopian tube fimbria, which is extremely rare but should be considered in the differential diagnosis of subcutaneous mass in the lower abdomen.

Jejunal Undifferentiated Spindle Cell Sarcoma with Intussusception Revealed by 18F-FDG PET/CT.

Spindle cell sarcoma is a malignant tumor with low incidence. They can occur in the soft tissue, bone, or viscera. The characteristics of morphology, density, and metabolism of spindle cell sarcoma are related to the location of the lesion. A 61-year-old woman presented with vomiting after eating for 2 weeks. Signs of peritoneal irritation were involved, but no response for symptomatic treatment included antiemetic and antispasmodic therapy. Abdominal computed tomography (CT) indicated a mass in the intestinal tract in the pelvic cavity. Then, 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/CT was performed, which interestingly detected a jejunal malignancy mass in the left upper abdomen with annular high uptake of 18F-FDG, which was complicated by intussusception and intestinal obstruction. Finally, the jejunal mass was pathologically clarified as an undifferentiated spindle cell sarcoma.

Isolated Diffuse Bone Marrow Metastases From Signet-ring Cell Gastric Carcinoma.

A 71-year-old female patient with a known history of signet-ring cell carcinoma presented with diffuse bone pain and anemic symptoms. An 18F-fluorodeoxyglucose (FDG) positron emission tomography/computed tomography study revealed diffuse heterogeneous hypermetabolic sclerotic lesions in the axial and proximal appendicular skeleton. No other 18F-FDG-avid lesions were detected. Subsequent bone marrow biopsy confirmed the diagnosis of metastatic carcinoma originating from the gastric primary site. Palliative treatment was initiated; however, the patient's condition deteriorated, and she succumbed to the disease two months later.

Cerebral hypometabolism in a pediatric patient with clinically resolved posterior reversible encephalopathy syndrome.

A 4-year-old boy with Nuclear factor-kappa B Essential Modulator deficiency syndrome presented with encephalopathy post haematopoietic stem cell transplantation. MRI demonstrated T2/FLAIR-hyperintensities in the posterior cerebral cortex concerning for posterior reversible encephalopathy syndrome. Clinical improvement was appreciated following withdrawal of the suspected offending pharmacological agent (Cyclosporine). An 18F-FDG PET/CT performed 2 months later to screen for post-transplant lymphoproliferative disease demonstrated markedly reduced FDG uptake in the posterior cerebral cortex, involving the parietal and occipital lobes. We describe, to the best of our knowledge, the first case of profound cerebral hypometabolism in a child with clinically resolved posterior reversible encephalopathy syndrome.

Comparison of the Performance of [18F]-FDG PET/CT and [18F]-FDG PET/MRI for Lymph Node Metastasis in Breast Cancer: A Systematic Review and Meta- Analysis.

OBJECTIVE: The primary objective of this study was to conduct a comparative analysis of the diagnostic efficacy of [18F]-FDG PET/CT and [18F]-FDG PET/MRI in the detection of breast cancer lymph node metastasis. METHODS: We conducted a comprehensive search on PubMed, Embase, and Web of Science databases, encompassing eligible articles until March 2023. The pooled sensitivity and specificity for [18F]-FDG PET/CT and [18F]-FDG PET/MRI have been reported as estimates with 95% Confidence Intervals (CIs) using a bivariate random-effect model. Utilizing the I square (I2) statistic, heterogeneity among pooled studies was evaluated. The quality assessment of the included studies was conducted using the Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) methodology. RESULTS: We included 18 studies (2057 patients). The sensitivity, specificity, and AUC (Area Under the Curve) values of [18F]-FDG PET/CT for overall lymph node metastasis in breast cancer have been found to be 0.58 (0.39 - 0.75), 0.83 (0.69-0.92), and 0.79 (0.75-0.82), respectively. Correspondingly, the values for [18F]-FDG PET/MRI were found to be 0.76 (0.60-0.88), 0.85 (0.77-0.91), and 0.89 (0.86-0.91), respectively. The sensitivity, specificity, and AUC values of [18F]-FDG PET/CT for axillary lymph node metastasis in breast cancer were 0.52 (0.37-0.67), 0.84 (0.68-0.92), and 0.73 (0.69-0.76), respectively. Correspondingly, the values for [18F]-FDG PET/MRI were 0.84 (0.76-0.89), 0.87 (0.75-0.94), and 0.86 (0.83-0.89), respectively. CONCLUSION: This study has suggested [18F]-FDG PET/MRI to have greater diagnostic power than [18F]-FDG PET/CT in detecting lymph node metastasis in breast cancer. However, the [18F]-FDG PET/MRI results have been obtained from a small sample size study, and more and larger prospective studies are needed for further confirmation on this issue.

Presentation of metastatic carotid body paraganglioma on F-18 FDG PET/CT: a rare disease.

Carotid body paraganglioma is a slow growing tumor of head and neck region. It can rarely be malignant in nature which is characterized by distant metastases on anatomical imaging. We share an interesting presentation of a malignant carotid body on F-18 FDG PET/CT in form of liver and skeletal metastases.

GWO+RuleFit: rule-based explainable machine-learning combined with heuristics to predict mid-treatment FDG PET response to chemoradiation for locally advanced non-small cell lung cancer.

OBJECTIVE: Vital rules learned from FDG-PET radiomics of tumor subregional response can provide clinical decision support for precise treatment adaptation. We combined a rule-based machine learning (ML) model (RuleFit) with a heuristic algorithm (Gray Wolf Optimizer, GWO) for mid-chemoradiation FDG-PET response prediction in patients with locally advanced non-small cell lung cancer. Approach: Tumors subregions were identified using K-means clustering. GWO+RuleFit consists of three main parts: (i) a random forest is constructed based on conventional features or radiomic features extracted from tumor regions or subregions in FDG-PET images, from which the initial rules are generated; (ii) GWO is used for iterative rule selection; (iii) the selected rules are fit to a linear model to make predictions about the target variable. Two target variables were considered: a binary response measure (∆SUVmean⩾20% decline) for classification and a continuous response measure (∆SUVmean) for regression. GWO+RuleFit was benchmarked against common ML algorithms and RuleFit, with leave-one-out cross-validated performance evaluated by the area under the receiver operating characteristic curve (AUC) in classification and root-mean-square error (RMSE) in regression. Main results: GWO+RuleFit selected 15 rules from the radiomic feature dataset of 23 patients. For treatment response classification, GWO+RuleFit attained numerically better cross-validated performance than RuleFit across tumor regions and sets of features (AUC:0.58-0.86 vs. 0.52-0.78, p=0.170-0.925). GWO+Rulefit also had the best or second-best performance numerically compared to all other algorithms for all conditions. For treatment response regression prediction, GWO+RuleFit (RMSE:0.162-0.192) performed better numerically for low-dimensional models (p=0.097-0.614) and significantly better for high-dimensional models across all tumor regions except one (RMSE:0.189-0.219, p<0.004). Significance: The GWO+RuleFit selected rules were interpretable, highlighting distinct radiomic phenotypes that modulated treatment response. GWO+Rulefit achieved parsimonious models while maintaining utility for treatment response prediction, which can aid clinical decisions for patient risk stratification, treatment selection, and biologically driven adaptation. Clinical trial: NCT02773238.

Relationships between plasma biomarkers, tau PET, FDG PET, and volumetric MRI in mild to moderate Alzheimer's disease patients.

INTRODUCTION: The "A/T/N" (amyloid/tau/neurodegeneration) framework provides a biological basis for Alzheimer's disease (AD) diagnosis and can encompass additional changes such as inflammation ("I"). A spectrum of T/N/I imaging and plasma biomarkers was acquired in a phase 2 clinical trial of rasagiline in mild to moderate AD patients. We evaluated these to understand biomarker distributions and relationships within this population. METHODS: Plasma biomarkers of pTau-181, neurofilament light chain (NfL), glial fibrillary acidic protein (GFAP), other inflammation-related proteins, imaging measures including fluorodeoxyglucose (FDG) positron emission tomography (PET), flortaucipir PET, and volumetric magnetic resonance imaging (MRI), and cognitive endpoints were analyzed to assess characteristics and relationships for the overall population (N = 47 at baseline and N = 21 for longitudinal cognitive comparisons) and within age-decade subgroups (57-69, 70-79, 80-90 years). RESULTS: Data demonstrate wide clinical and biomarker heterogeneity in this population influenced by age and sex. Plasma pTau-181 and GFAP correlate with tau PET, most strongly in left inferior temporal cortex (p = 0.0002, p = 0.0006, respectively). In regions beyond temporal cortex, tau PET uptake decreased with age for the same pTau-181 or GFAP concentrations. FDG PET and brain volumes correlate with tau PET in numerous regions (such as inferior temporal: p = 0.0007, p = 0.00001, respectively). NfL, GFAP, and all imaging modalities correlate with baseline MMSE; subsequent MMSE decline is predicted by baseline parahippocampal and lateral temporal tau PET (p = 0.0007) and volume (p = 0.0006). Lateral temporal FDG PET (p = 0.006) and volume (p = 0.0001) are most strongly associated with subsequent ADAS-cog decline. NfL correlates with FDG PET and baseline MMSE but not tau PET. Inflammation biomarkers are intercorrelated but correlated with other biomarkers in only the youngest group. DISCUSSION: Associations between plasma biomarkers, imaging biomarkers, and cognitive status observed in this study provide insight into relationships among biological processes in mild to moderate AD. Findings show the potential to characterize AD patients regarding likely tau pathology, neurodegeneration, prospective clinical decline, and the importance of covariates such as age. Highlights: Plasma pTau-181 and GFAP correlated with regional and global tau PET in mild to moderate AD.NfL correlated with FDG PET and cognitive endpoints but not plasma pTau-181 or tau PET.Volume and FDG PET showed strong relationships to tau PET, one another, and cognitive status.Temporal volumes most strongly predicted decline in both MMSE and ADAS-cog.Volume and plasma biomarkers can enrich for elevated tau PET with age a significant covariate.

Functional imaging in late-onset epilepsy: A focused review.

INTRODUCTION: About 25 % of new-onset epilepsies are diagnosed after age 65. Late-onset epilepsy (LOE) is predicted to become a major healthcare problem in the next 15 years as the global population increases and ages. Neurodegenerative disorders account for 10-20 % of LOE, while over 20 % of these patients have an unknown etiology. Established diagnostic tools such as FDG-PET and novel biomarkers of neurodegeneration including amyloid and tau PET hold a lot of promise in diagnosing and ruling out neurodegenerative disorders in these patients. METHODS: We conducted a literature search to identify articles involving LOE populations and using one or more functional neuroimaging techniques. RESULTS: A total of 5 studies were identified through Boolean searching and snowballing. These were highly heterogenous with respect to operational definitions of LOE, analyses and interpretation pipelines. CONCLUSION: While there is some evidence for feasibility and usefulness of FDG- and Amyloid PET in LOE, methodological heterogeneities in the available literature preclude any notable conclusions. Future research in this field will benefit from a consensus on epilepsy-specific analysis and interpretation guidelines for amyloid and tau PET.

Infective endocarditis associated with left atrial appendage occlusion device: a contemporary systematic review.

BACKGROUND: Data are limited regarding infective endocarditis (IE) complicating left atrial appendage occlusion (LAAO) device procedures. This systematic review examines the etiology, diagnosis, and management of infective endocarditis (IE) following LAAO. METHODS: A comprehensive search of six databases was performed between 2007 and 2022. In selecting studies, articles were included if they provided information about IE complicating LAAO, with relevant clinical and imaging details. Articles were excluded if they were editorials, study protocols, letters, or abstracts. RESULTS: We identified seven cases of IE complicating LAAO published between 2007 and 2022, from the United States and Europe, highlighting the exceedingly rare reported incidence of the condition. Staphylococcus aureus is the predominant organism implicated in LAAO infections. Echocardiography is a key imaging modality for diagnosing LAAO IE. Both antibiotic therapy and surgical intervention in appropriate patients are important for managing LAAO device-related IE. CONCLUSIONS: The limited data in the current literature regarding LAAO IE underscores the need for prospective clinical trials to establish evidence-based guidelines for infection prophylaxis, diagnosis, and management. Our findings emphasize the importance of vigilance for device-related infections, especially as the use of LAAO devices continues to grow worldwide.

Prognostic Value of the 18F-FDG PET/CT and Haematological Parameters in Head and Neck Cancer.

INTRODUCTION: Fluorine 18-fluoro-glucose positron emission tomography/computed tomography (18F-FDG PET/CT) is commonly used for the staging of head and neck cancer. This study aimed to evaluate the correlation between 18F-FDG PET/CT, haematological parameters and prognosis in patients with advanced head and neck cancer. METHODS: This was a single-institutional retrospective study of 83 patients with advanced head and neck squamous cell carcinoma (HNSCC) who underwent 18F-FDG PET/CT imaging before initial treatment between 2014 and 2018. 18F-FDG PET/CT after treatment was performed in 57 patients. The prognostic parameters of the pre- and post-treatment maximum standardised uptake value (SUVmax), metabolic tumour volume (MTV), total lesion glycolysis (TLG) of primary tumours and haematological parameters were analysed to evaluate the association between overall survival (OS) and progression-free survival (PFS). RESULTS: Pre-MTV, pre-TLG and post-SUVmax were significantly associated with poor OS and PFS (p < 0.05). Haematological parameters, including pretreatment neutrophil/lymphocyte ratio and C-reactive protein/albumin ratio, were associated with 18F-FDG PET/CT parameters. In multivariate analysis, post-SUVmax was an independent prognostic factor for OS and PFS. CONCLUSION: A correlation between PET/CT metabolic and haematological parameters was observed. The volume and intensity of 18F-FDG uptake region, in addition to haematological parameters, are feasible markers for predicting the progression of HNSCC in daily practice. Further, post-SUVmax could be an independent parameter for predicting poor survival.

The clinical and predictive value of 18F-FDG PET/CT metabolic patterns in a clinical Chinese cohort with autoimmune encephalitis.

AIMS: To investigate the diagnostic and predictive role of 18F-FDG PET/CT in patients with autoimmune encephalitis (AE) as a whole group. METHODS: Thrty-five patients (20 females and 15 males) with AE were recruited. A voxel-to-voxel semi-quantitative analysis based on SPM12 was used to analyze 18F-FDG PET/CT imaging data compared to healthy controls. Further comparison was made in different prognostic groups categorized by modified Rankin Scale (mRS). RESULTS: In total, 24 patients (68.6%) were tested positive neuronal antibodies in serum and/or CSF. Psychiatric symptoms and seizure attacks were major clinical symptoms. In the acute stage, 13 patients (37.1%) demonstrated abnormal brain MRI results, while 33 (94.3%) presented abnormal metabolism patterns. 18F-FDG PET/CT was more sensitive than MRI (p < 0.05). Patients with AE mainly presented mixed metabolism patterns compared to the matched controls, demonstrating hypermetabolism mainly in the cerebellum, BG, MTL, brainstem, insula, middle frontal gyrus, and relatively hypometabolism in the frontal cortex, occipital cortex, temporal gyrus, right parietal gyrus, left cingulate gyrus (p < 0.05, FWE corrected). After a median follow-up of 26 months, the multivariable analysis identified a decreased level of consciousness as an independent risk factor associated with poor outcome of AE (HR = 3.591, p = 0.016). Meanwhile, decreased metabolism of right superior frontal gyrus along with increased metabolism of the middle and upper brainstem was more evident in patients with poor outcome (p < 0.001, uncorrected). CONCLUSION: 18F-FDG PET/CT was more sensitive than MRI to detect neuroimaging abnormalities of AE. A mixed metabolic pattern, characterized by large areas of cortical hypometabolism with focal hypermetabolism was a general metabolic pattern. Decreased metabolism of right superior frontal gyrus with increased metabolism of the middle and upper brainstem may predict poor long-term prognosis of AE.

Symptomatic Myeloma: PET, Whole-Body MR Imaging with Diffusion-Weighted Imaging or Both.

According to international guidelines, patients with suspected myeloma should primarily undergo low-dose whole-body computed tomography (CT) for diagnostic purposes. To optimize sensitivity and specificity and enable treatment response assessment, whole-body MR (WB-MR) imaging should include diffusion-weighted imaging with apparent diffusion coefficient maps and T1-weighted Dixon sequences with bone marrow Fat Fraction Quantification. At baseline WB-MR imaging shows greater sensitivity for the detecting focal lesions and diffuse bone marrow infiltration pattern than 18F-fluorodeoxyglucose PET-CT, which is considered of choice for evaluating response to treatment and minimal residual disease and imaging of extramedullary disease.

Review on radiomic analysis in 18F-fluorodeoxyglucose positron emission tomography for prediction of melanoma outcomes.

Over the past decade, several strategies have revolutionized the clinical management of patients with cutaneous melanoma (CM), including immunotherapy and targeted tyrosine kinase inhibitor (TKI)-based therapies. Indeed, immune checkpoint inhibitors (ICIs), alone or in combination, represent the standard of care for patients with advanced disease without an actionable mutation. Notably BRAF combined with MEK inhibitors represent the therapeutic standard for disease disclosing BRAF mutation. At the same time, FDG PET/CT has become part of the routine staging and evaluation of patients with cutaneous melanoma. There is growing interest in using FDG PET/CT measurements to predict response to ICI therapy and/or target therapy. While semiquantitative values such as standardized uptake value (SUV) are limited for predicting outcome, new measures including tumor metabolic volume, total lesion glycolysis and radiomics seem promising as potential imaging biomarkers for nuclear medicine. The aim of this review, prepared by an interdisciplinary group of experts, is to take stock of the current literature on radiomics approaches that could improve outcomes in CM.

18F-FDG PET/CT Radiomics-Based Multimodality Fusion Model for Preoperative Individualized Noninvasive Prediction of Peritoneal Metastasis in Advanced Gastric Cancer.

PURPOSE: This study was designed to develop and validate a machine learning-based, multimodality fusion (MMF) model using 18F-fluorodeoxyglucose (FDG) PET/CT radiomics and kernelled support tensor machine (KSTM), integrated with clinical factors and nuclear medicine experts' diagnoses to individually predict peritoneal metastasis (PM) in advanced gastric cancer (AGC). METHODS: A total of 167 patients receiving preoperative PET/CT and subsequent surgery were included between November 2006 and September 2020 and were divided into a training and testing cohort. The PM status was confirmed via laparoscopic exploration and postoperative pathology. The PET/CT signatures were constructed by classic radiomic, handcrafted-feature-based model and KSTM self-learning-based model. The clinical nomogram was constructed by independent risk factors for PM. Lastly, the PET/CT signatures, clinical nomogram, and experts' diagnoses were fused using evidential reasoning to establish the MMF model. RESULTS: The MMF model showed excellent performance in both cohorts (area under the curve [AUC] 94.16% and 90.84% in training and testing), and demonstrated better prediction accuracy than clinical nomogram or experts' diagnoses (net reclassification improvement p < 0.05). The MMF model also had satisfactory generalization ability, even in mucinous adenocarcinoma and signet ring cell carcinoma which have poor uptake of 18F-FDG (AUC 97.98% and 89.71% in training and testing). CONCLUSIONS: The 18F-FDG PET/CT radiomics-based MMF model may have significant clinical implications in predicting PM in AGC, revealing that it is necessary to combine the information from different modalities for comprehensive prediction of PM.

Respective contribution of baseline clinical data, tumour metabolism and tumour blood-flow in predicting pCR after neoadjuvant chemotherapy in HER2 and Triple Negative breast cancer.

BACKGROUND: The aim of this study is to investigate the added value of combining tumour blood flow (BF) and metabolism parameters, including texture features, with clinical parameters to predict, at baseline, the pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) in patients with newly diagnosed breast cancer (BC). METHODS: One hundred and twenty-eight BC patients underwent a 18F-FDG PET/CT before any treatment. Tumour BF and metabolism parameters were extracted from first-pass dynamic and delayed PET images, respectively. Standard and texture features were extracted from BF and metabolic images. Prediction of pCR was performed using logistic regression, random forest and support vector classification algorithms. Models were built using clinical (C), clinical and metabolic (C+M) and clinical, metabolic and tumour BF (C+M+BF) information combined. Algorithms were trained on 80% of the dataset and tested on the remaining 20%. Univariate and multivariate features selections were carried out on the training dataset. A total of 50 shuffle splits were performed. The analysis was carried out on the whole dataset (HER2 and Triple Negative (TN)), and separately in HER2 (N=76) and TN (N=52) tumours. RESULTS: In the whole dataset, the highest classification performances were observed for C+M models, significantly (p-value<0.01) higher than C models and better than C+M+BF models (mean balanced accuracy of 0.66, 0.61, and 0.64 respectively). For HER2 tumours, equal performances were noted for C and C+M models, with performances higher than C+M+BF models (mean balanced accuracy of 0.64, and 0.61 respectively). Regarding TN tumours, the best classification results were reported for C+M models, with better performances than C and C+M+BF models but not significantly (mean balanced accuracy of 0.65, 0.63, and 0.62 respectively). CONCLUSION: Baseline clinical data combined with global and texture tumour metabolism parameters assessed by 18F-FDG PET/CT provide a better prediction of pCR after NAC in patients with BC compared to clinical parameters alone for TN, and HER2 and TN tumours together. In contrast, adding BF parameters to the models did not improve prediction, regardless of the tumour subgroup analysed.

PET-based radiomic feature based on the cross-combination method for predicting the mid-term efficacy and prognosis in high-risk diffuse large B-cell lymphoma patients.

Objectives: This study aims to develop 7×7 machine-learning cross-combinatorial methods for selecting and classifying radiomic features used to construct Radiomics Score (RadScore) of predicting the mid-term efficacy and prognosis in high-risk patients with diffuse large B-cell lymphoma (DLBCL). Methods: Retrospectively, we recruited 177 high-risk DLBCL patients from two medical centers between October 2012 and September 2022 and randomly divided them into a training cohort (n=123) and a validation cohort (n=54). We finally extracted 110 radiomic features along with SUVmax, MTV, and TLG from the baseline PET. The 49 features selection-classification pairs were used to obtain the optimal LASSO-LASSO model with 11 key radiomic features for RadScore. Logistic regression was employed to identify independent RadScore, clinical and PET factors. These models were evaluated using receiver operating characteristic (ROC) curves and calibration curves. Decision curve analysis (DCA) was conducted to assess the predictive power of the models. The prognostic power of RadScore was assessed using cox regression (COX) and Kaplan-Meier plots (KM). Results: 177 patients (mean age, 63 ± 13 years,129 men) were evaluated. Multivariate analyses showed that gender (OR,2.760; 95%CI:1.196,6.368); p=0.017), B symptoms (OR,4.065; 95%CI:1.837,8.955; p=0.001), SUVmax (OR,2.619; 95%CI:1.107,6.194; p=0.028), and RadScore (OR,7.167; 95%CI:2.815,18.248; p<0.001) independently contributed to the risk factors for predicting mid-term outcome. The AUC values of the combined models in the training and validation groups were 0.846 and 0.724 respectively, outperformed the clinical model (0.714;0.556), PET based model (0.664; 0.589), NCCN-IPI model (0.523;0.406) and IPI model (0.510;0.412) in predicting mid-term treatment outcome. DCA showed that the combined model incorporating RadScore, clinical risk factors, and PET metabolic metrics has optimal net clinical benefit. COX indicated that the high RadScore group had worse prognosis and survival in progression-free survival (PFS) (HR, 2.1737,95%CI: 1.2983, 3.6392) and overall survival (OS) (HR,2.1356,95%CI: 1.2561, 3.6309) compared to the low RadScore group. KM survival analysis also showed the same prognosis prediction as Cox results. Conclusion: The combined model incorporating RadScore, sex, B symptoms and SUVmax demonstrates a significant enhancement in predicting medium-term efficacy and prognosis in high-risk DLBCL patients. RadScore using 7×7 machine learning cross-combinatorial methods for selection and classification holds promise as a potential method for evaluating medium-term treatment outcome and prognosis in high-risk DLBCL patients.

A case of Muir-Torre syndrome on [18F]FDG PET/CT.

Muir-Torre syndrome (MTS) is a rare genetic disorder, considered a subtype of Lynch syndrome, that causes sebaceous cutaneous tumors and increases the risk of internal visceral tumors. We present a case of a 63-year-old male with a history of MTS with sebaceous tumors, colorectal, and urothelial cancers who underwent fluorine-18-deoxyglucose positron emission tomography/ computed tomography [18F]FDG PET/CT to follow-up on multiple [18F]FDG avid skin lesions and right pelvic lymph nodes. Although few reports are available detailing the utility of [18F]FDG PET/CT in this rare disease, this modality appears useful, and superior, to computed tomography in the diagnosis and follow-up of MTS.

A radiomics-boosted deep-learning for risk assessment of synchronous peritoneal metastasis in colorectal cancer.

OBJECTIVES: Synchronous colorectal cancer peritoneal metastasis (CRPM) has a poor prognosis. This study aimed to create a radiomics-boosted deep learning model by PET/CT image for risk assessment of synchronous CRPM. METHODS: A total of 220 colorectal cancer (CRC) cases were enrolled in this study. We mapped the feature maps (Radiomic feature maps (RFMs)) of radiomic features across CT and PET image patches by a 2D sliding kernel. Based on ResNet50, a radiomics-boosted deep learning model was trained using PET/CT image patches and RFMs. Besides that, we explored whether the peritumoral region contributes to the assessment of CRPM. In this study, the performance of each model was evaluated by the area under the curves (AUC). RESULTS: The AUCs of the radiomics-boosted deep learning model in the training, internal, external, and all validation datasets were 0.926 (95% confidence interval (CI): 0.874-0.978), 0.897 (95% CI: 0.801-0.994), 0.885 (95% CI: 0.795-0.975), and 0.889 (95% CI: 0.823-0.954), respectively. This model exhibited consistency in the calibration curve, the Delong test and IDI identified it as the most predictive model. CONCLUSIONS: The radiomics-boosted deep learning model showed superior estimated performance in preoperative prediction of synchronous CRPM from pre-treatment PET/CT, offering potential assistance in the development of more personalized treatment methods and follow-up plans. CRITICAL RELEVANCE STATEMENT: The onset of synchronous colorectal CRPM is insidious, and using a radiomics-boosted deep learning model to assess the risk of CRPM before treatment can help make personalized clinical treatment decisions or choose more sensitive follow-up plans. KEY POINTS: Prognosis for patients with CRPM is bleak, and early detection poses challenges. The synergy between radiomics and deep learning proves advantageous in evaluating CRPM. The radiomics-boosted deep-learning model proves valuable in tailoring treatment approaches for CRC patients.

Is 18F-FDG-PET/CT an Optimal Imaging Modality for Detecting Immune-Related Adverse Events after Immune-Checkpoint Inhibitor Therapy? Pros and Cons.

Immunotherapy with immune checkpoint inhibitors (ICIs) has revolutionized contemporary oncology, presenting efficacy in various solid tumors and lymphomas. However, ICIs may potentially overstimulate the immune system, leading to immune-related adverse events (irAEs). IrAEs may affect multiple organs, such as the colon, stomach, small intestine, kidneys, skin, lungs, joints, liver, lymph nodes, bone marrow, brain, heart, and endocrine glands (e.g., pancreas, thyroid, or adrenal glands), exhibiting autoimmune inflammation. 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG PET/CT) is commonly used in oncology for staging and assessment of therapy responses, but it may also serve as a tool for detecting irAEs. This review aims to present various patterns of metabolic activation associated with irAEs due to ICI treatment, identifiable through 18F-FDG PET/CT. It describes the advantages of early detection of irAEs, but also presents the challenges in differentiating them from tumor progression. It also delves into aspects of molecular response assessment within the context of pseudoprogression and hyperprogression, along with typical imaging findings related to these phenomena. Lastly, it summarizes the role of functional PET imaging in oncological immunotherapy, speculating on its future significance and limitations.