Cancer Imaging最新文献

Diffuse large B-cell lymphoma (DLBCL) is a highly heterogeneous hematological malignancy resulting in a range of outcomes, and the early prediction of these outcomes has important implications for patient management. Clinical scoring systems provide the most commonly used prognostic evaluation criteria, and the value of genetic testing has also been confirmed by in-depth research on molecular typing. [¹⁸F]-fluorodeoxyglucose positron emission tomography / computed tomography ([¹⁸F]FDG PET/CT) is an invaluable tool for predicting DLBCL progression. Conventional baseline image-based parameters and machine learning models have been used in prognostic FDG PET/CT studies of DLBCL; however, numerous studies have shown that combinations of baseline clinical scoring systems, molecular subtypes, and parameters and models based on baseline FDG PET/CT image may provide better predictions of patient outcomes and aid clinical decision-making in patients with DLBCL.

Background: Recent advancements in novel anti-human epidermal growth factor receptor 2 (HER2) antibody-drug conjugates (ADCs) have highlighted the emerging HER2-low breast cancer subtype with promising therapeutic efficacy. This study aimed to comparatively analyze the metabolic characteristics and prognostic stratification of HER2-low and HER2-zero breast cancer using baseline fluorine-18 fluorodeoxyglucose (¹⁸F-FDG) positron emission tomography/computed tomography (PET/CT) imaging.

Methods: Consecutive patients with newly diagnosed breast cancer who underwent ¹⁸F-FDG PET/CT prior to therapy in our hospital were retrospectively reviewed. The relationship between metabolic parameters (maximum standardized uptake value (SUVmax), tumor-to-liver SUV ratio (TLR), total lesion glycolysis (TLG), and metabolic tumor volume (MTV)) in primary lesions and HER2 expression was analyzed. The survival analyses were performed to identify the prognostic factors for disease-free survival (DFS) in patients with HER2-negative (HER2-low versus -zero).

Results: In total, 258 patients (mean age: 54 ± 12 years) were included. In hormone receptor (HR)-positive subgroup, SUVmax and TLR were significantly higher in HER2-low than in HER2-zero (P = 0.045 and 0.03, respectively). But in HR-negative subgroup, there was no significant metabolic difference between HER2-low and HER2-zero (All P > 0.05). The four metabolic parameters were significant predictors of DFS in HER2-negative patients (All P < 0.01), but there was no significant difference in DFS between HER2-low and -zero, regardless of tumor metabolism. Moreover, in HER2-zero patients, the DFS of patients with high metabolism was significantly shorter than that of patients with low metabolism (P_SUVmax = 0.002, P_MTV = 0.03, P_TLG= 0.005, P_TLR < 0.001, respectively), but without a similar finding in HER2-low patients.

Conclusion: Our study demonstrated the HR-positive HER2-low breast cancer exhibited a particularity in glucose metabolic profile. Additionally, HER2-zero patients with elevated metabolism were associated with inferior prognosis and warranted careful attention in clinical evaluations.

Background: It is difficult for radiologists, especially junior radiologists with limited experience to make differential diagnoses between mediastinal lymphomas and thymic epithelial tumors (TETs) due to the overlapping imaging features. The purpose of this study was to develop and validate a CT-based clinico-radiomics model for differentiating lymphomas from TETs and to investigate whether a human-machine hybrid system can assist junior radiologists in improving their diagnostic performance.

Methods: The patients who underwent contrast-enhanced chest CT and pathologically confirmed with lymphoma or TET at two centers from January 2011 to December 2019 and from January 2017 to December 2021 were retrospectively included and split as training/validation set and external test set, respectively. Clinical and radiomic signatures were pre-selected by elastic-net, and the models were established with the selected signatures using ensemble learning. Three radiologists independently reviewed CT images and assessed each case of the external test set with knowledge of the relevant clinical information. The diagnoses of reader 1, reader 2, and reader 3 were compared with those of the models in the external test set and further separately input to the model's ensemble process as a human-machine system to make final decisions in the external test set. The improvement of diagnostic performance of radiologists by human-machine system was evaluated by the area under the receiver operating characteristic curve and increase rate.

Results: A total of 95 patients (51 with lymphomas and 44 with TETs) at Center 1 and 94 (52 with lymphomas and 42 with TETs) at Center 2 were enrolled and divided into training/validation sets and external test set, respectively. The diagnostic performance of the clinico-radiomics model has outperformed the junior radiologists and senior radiologist in AUC (clinico-radiomics model: 0.85 (0.76,0.92); reader 2: 0.70 (0.60,0.80); reader 3: 0.60 (0.49,0.71), reader 1: 0.76 (0.66,0.86), respectively) in the external test set. The human-machine hybrid system demonstrated significant increases in AUC (reader 1 + model: 0.87 (0.79,0.94), an increase of 14%; reader 2 + model: 0.86 (0.77,0.93), an increase of 23%; reader 3 + model: 0.84 (0.76,0.91), an increase of 40%), compared to the human performance alone.

Conclusions: The clinico-radiomics model outperformed three radiologists in differentiating lymphomas from TETs on CT. The use of the human-machine hybrid system significantly improved the performance of radiologists, especially junior radiologists. It provides a real-time decision tool to reduce bias and mistakes in radiologist diagnosis and enhances the diagnostic confidence of junior radiologists. This attempt may lead to more human-machine hybrid systems being explored in the diagnosis of different diseases to drive future clinical applications.

Background: Although many well-known factors affect the maximum standardized uptake value (SUVmax), it remains the most requested and used parameter, especially among clinicians, despite other parameters, such as the standardized uptake value corrected for lean body mass and the metabolic tumor volume, being proven to be less sensitive to the same factors, more robust, and eventually more informative. This study intends to provide robust evidence regarding the diagnostic and prognostic value of SUVmax in a large cohort of subjects with suspected malignant lung nodules imaged by [¹⁸F]FDG PET/CT.

Materials and methods: We performed a retrospective analysis of patients with suspected/confirmed primary lung tumours undergoing [18F]FDG PET/CT. The sample size was 567 patients. Demographics, imaging, surgical, histological, and follow-up data were collected. SUVmax was analysed according to histology, stage, scanner, and outcome. The impact on measured values of different reconstruction protocols was assessed. All potential predictors of patients' outcome were assessed.

Results: 91% cases were primary lung tumours. Lung benign nodules or metastases accounted for 5% and 4% of cases. Most patients presented with adenocarcinoma (70%) and stage I disease (51%); 144 patients relapsed and 55 died. SUVmax failed to effectively differentiate benign lesions from primary tumours or metastases. Stage I patients presented lower SUVmax. SUVmax significantly correlated with patient weight, injected [¹⁸F]FDG activity, and lesion size and differed between reconstructions' protocols. Survival analyses revealed no independent prognostic significance for SUVmax in progression-free after adjusting for other variables. SUVmax correlated with overall survival, disease stage and tumour histotype.

Conclusion: Our study confirms that SUVmax, though widely employed, present relevant limitations in discriminating between benign lesion and lung cancer, in classifying cancer histotypes, and in predicting patient outcomes independently. Known influencing factors significantly impact on numerical values, thus SUV values should be regarded with caution in clinical practice.

Background: Identifying DNA mismatch repair deficiency (MMRd) is important for prognosis risk stratification in patients with early-stage endometrial cancer (EC), but there is a notable absence of cost-effective and non-invasive preoperative assessment techniques. The study explored the co-reactivity pattern of glucose metabolism and blood perfusion in EC based on hybrid [¹⁸F]fluorodeoxyglucose ([¹⁸F]FDG) PET/dynamic contrast enhanced (DCE)-MRI to provide an imaging biomarker for identifying MMRd.

Methods: Patients with a history of postmenopausal bleeding and initially diagnosed with EC on ultrasound were recruited to perform a PET/DCE-MRI scan. Glucose metabolism parameters were calculated on PET, and blood perfusion parameters were calculated semi-automatically by the DCE-Tofts pharmacokinetic model. The MMRd of early-stage EC was evaluated by immunohistochemistry. The synchronous variation of PET and DCE-MRI parameters was compared between the MMRd and mismatch repair proficiency (MMRp). The association between PET/DCE-MRI and MMRd was analyzed by logistic regression to establish the digital biomarker for predicting MMRd. Receiver operating characteristic curve, decision curve analysis, and the net reclassification index (NRI) were used to evaluate the value of the digital biomarker in identifying MMRd.

Results: Eighty-six early-stage EC cases (58.92 ± 10.13 years old, 34 MMRd) were enrolled. The max/mean standardized uptake value (SUV_max/SUV_mean), metabolic tumor volume, total lesion glycolysis, transfer constant (K_trans), and efflux rate (K_ep) were higher in MMRd than those in MMRp (P < 0.001, < 0.001, 0.002, 0.004, < 0.001, and 0.005, respectively). The correlations between glucose metabolism and blood perfusion were different between the MMRd and MMRp subgroups. SUV_max was correlated with K_ep (r = 0.36) in the MMRd. SUV_mean (odds ratio [OR] = 1.32, P = 0.006) and K_trans (OR = 1.90, P = 0.021) were independent risk factors for MMRd. And the digital biomarker that combined SUV_mean and K_trans outperformed in identifying MMRd in early-stage EC more than DCE-MRI (AUC: 0.83 vs. 0.78, NRI = 13%).

Conclusion: A potential digital biomarker based on [¹⁸F]FDG PET/DCE-MRI can identify MMRd for prognosis risk stratification in early-stage EC.

Background: Bone biopsy is the gold standard for diagnosing bone metastases. However, there is no clinical consensus regarding the optimal imaging test for determining the puncture site.

Methods: We compared the performance of [¹⁸F]FDG PET/CT with CT in detecting bone metastases to achieve the highest biopsy efficiency. This registered prospective study enrolled 273 patients with bone lesions who were treated between January 2020 and March 2021. Patients were randomly assigned to undergo [¹⁸F]FDG PET/CT or CT to determine the puncture site before bone biopsy. The accuracy, sensitivity, specificity, second biopsy rate, diagnostic time and cost-effectiveness of the two imaging tests were compared.

Results: The accuracy and sensitivity of [¹⁸F]FDG PET/CT group in detecting bone metastases were significantly higher than CT group(97.08% vs. 90.44%, 98.76% vs. 92.22%, P < 0.05). The second biopsy rate was significantly lower in the [¹⁸F]FDG PET/CT group (2.19% vs. 5.15%; P < 0.05). The diagnostic time of [¹⁸F]FDG PET/CT was 18.33 ± 2.08 days, which was significantly shorter than 21.28 ± 1.25 days in CT group ( P < 0.05). The cost of [¹⁸F] FDG PETCT is 11428.35 yuan, and the cost of CT is 13287.52 yuan; the incremental cost is 1859.17 yuan. SUVmax > 6.3 combined with ALP > 103 U/L showed a tendency for tumor metastases with an AUC of 0.901 (95%CI 0.839 to 0.946, P < 0.001).

Conclusion: [¹⁸F]FDG PET/CT has better performance and cost-effectiveness than CT in determining the bone biopsy site for suspect bone metastases.

Trial registration: The prospective study was registered on 2018-04-10, and the registration number is ChiCTR1800015540.

Advances in cancer diagnosis and treatment have substantially improved patient outcomes and survival in recent years. However, up to 75% of cancer patients and survivors, including those with non-central nervous system (non-CNS) cancers, suffer from "brain fog" or impairments in cognitive functions such as attention, memory, learning, and decision-making. While we recognize the impact of cancer-related cognitive impairment (CRCI), we have not fully investigated and understood the causes, mechanisms and interplays of various involving factors. Consequently, there are unmet needs in clinical oncology in assessing the risk of CRCI and managing patients and survivors with this condition in order to make informed treatment decisions and ensure the quality of life for cancer survivors. The state-of-the-art neuroimaging technologies, particularly clinical imaging modalities like magnetic resonance imaging (MRI) and positron emission tomography (PET), have been widely used to study neuroscience questions, including CRCI. However, in-depth applications of these functional and molecular imaging methods in CRCI and their clinical implementation for CRCI management are largely limited. This scoping review provides the current understanding of contributing neurological factors to CRCI and applications of the state-of-the-art multi-modal neuroimaging methods in investigating the functional and structural alterations related to CRCI. Findings from these studies and potential imaging-biomarkers of CRCI that can be used to improve the assessment and characterization of CRCI as well as to predict the risk of CRCI are also highlighted. Emerging issues and perspectives on future development and applications of neuroimaging tools to better understand CRCI and incorporate neuroimaging-based approaches to treatment decisions and patient management are discussed.

There is an unmet need for a more accurate molecular imaging radiotracer in the field of non-seminomatous germ cell tumors (NSGCT). The clinical problem is that no single imaging modality is able to differentiate teratoma from necrotic tissue in NSGCTs, which the nuclear medicine techniques are no exception. The exponential growth in the list of potentially promising radiotracers may hold promise in the future for imaging of NSGCTs. Here, we have reviewed the past efforts and potential future advances in this field.

Background: Two-deoxy-2-[fluorine-18]-fluoro-d-glucose (¹⁸F-FDG) positron emission tomography (PET) is useful for detecting malignant lesions; however, the clinical significance of cardiac ¹⁸F-FDG uptake in patients with cancer remains unclear. This preliminary study explored the relationship between cardiac ¹⁸F-FDG uptake and advanced diseases such as cancer cachexia in non-small cell lung cancer (NSCLC).

Methods: Forty-three patients with advanced NSCLC who underwent ¹⁸F-FDG PET and complained of weight loss before the first-line systemic therapy were retrospectively included in this study. Visual assessment using a 5-point scale based on ¹⁸F-FDG uptake was performed; a cut-off score of 3 was determined, a low score was 1, 2, or 3, and a high score was 4 or 5).

Results: High and low visual cardiac ¹⁸F-FDG uptakes were observed in 27 (62.8%) and 16 (37.2%) patients, respectively. Of the 43 patients, 17 (39.5%) definitely had cachexia, and 26 (60.5%) did not. A low visual score and standardized uptake value_max for cardiac ¹⁸F-FDG uptake were significantly associated with high metabolic tumor activity (p = 0.009, and p = 0.009, respectively) and a high neutrophil-to-lymphocyte ratio (p = 0.016, and p = 0.047, respectively), whereas a low visual score for cardiac ¹⁸F-FDG uptake and high metabolic tumor activity were significantly associated with cachexia (p = 0.004). The amount of cardiac ¹⁸F-FDG accumulation depicted a close relationship with body mass index, low weight loss, and inflammation. The combination of cachexia and low visual cardiac ¹⁸F-FDG uptake was identified as a significant predictor for poor overall survival (OS) (p = 0.034).

Conclusion: Decreased visual cardiac ¹⁸F-FDG uptake was associated with poor nutritional status and OS, and cachexia in patients with advanced NSCLC.