Cancer Imaging最新文献

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.

The Response Evaluation in Solid Tumors (RECIST) 1.1 provides key guidance for performing imaging response assessment and defines image-based outcome metrics in oncology clinical trials, including progression free survival. In this framework, tumors identified on imaging are designated as either target lesions, non-target disease or new lesions and a structured categorical response is assigned at each imaging time point. While RECIST provides definitions for these categories, it specifically and objectively defines only the target disease. Predefined thresholds of size change provide unbiased metrics for determining objective response and disease progression of the target lesions. However, worsening of non-target disease or emergence of new lesions is given the same importance in determining disease progression despite these being qualitatively assessed and less rigorously defined. The subjective assessment of non-target and new disease contributes to reader variability, which can impact the quality of image interpretation and even the determination of progression free survival. The RECIST Working Group has made significant efforts in developing RECIST 1.1 beyond its initial publication, particularly in its application to targeted agents and immunotherapy. A review of the literature highlights that the Working Group has occasionally employed or adopted objective measures for assessing non-target and new lesions in their evaluation of RECIST-based outcome measures. Perhaps a prospective evaluation of these more objective definitions for non-target and new lesions within the framework of RECIST 1.1 might improve reader interpretation. Ideally, these changes could also better align with clinically meaningful outcome measures of patient survival or quality of life.

Background: To develop an artificial intelligence (AI)-based model using Radiomics, deep learning (DL) features extracted from ¹⁸F-fluorodeoxyglucose (¹⁸F-FDG) Positron emission tomography/Computed Tomography (PET/CT) images of tumor and cervical lymph node with clinical feature for predicting cervical lymph node metastasis (CLNM) in patients with esophageal squamous cell carcinoma (ESCC).

Methods: The study included 300 ESCC patients from the First Affiliated Hospital of Zhengzhou University who were divided into a training cohort and an internal testing cohort with an 8:2 ratio. Another 111 patients from Shanghai Chest Hospital were included as the external cohort. For each sample, we extracted 428 PET/CT-based Radiomics features from the gross tumor volume (GTV) and cervical lymph node (CLN) delineated layer by layer and 256 PET/CT-based DL features from the maximum cross-section of GTV and CLN images We input these features into seven different machine learning algorithms and ultimately selected logistic regression (LR) as the model classifier. Subsequently, we evaluated seven models (Clinical, Radiomics, Radiomics-Clinical, DL-Clinical, DL-Radiomics, DL-Radiomics-Clinical) using Radiomics features, DL features and clinical feature.

Results: The DL-Radiomics-Clinical (DRC) model demonstrated higher AUC of 0.955 and 0.916 compared to the other six models in both internal and external testing cohorts respectively. The DRC model achieved the highest accuracy among the seven models in both the internal and external test sets, with scores of 0.951 and 0.892, respectively.

Conclusions: Through the combination of Radiomics features and DL features from PET/CT imaging and clinical feature, we developed a predictive model exhibiting exceptional classification capabilities. This model can be considered as a non-invasive method for predication of CLNM in patients with ESCC. It might facilitate decision-making regarding to the extend of lymph node dissection, and to select candidates for postoperative adjuvant therapy.

Background: This study aims to construct predicting models using radiomic and clinical features in predicting first-line vascular endothelial growth factor receptor-tyrosine kinase inhibitor (VEGFR-TKI) early resistance in metastatic clear cell renal cell carcinoma (mccRCC) patients. We also aim to explore the correlation of predicting models with short and long-term survival of mccRCC patients.

Materials and methods: In this retrospective study, 110 mccRCC patients from 2009 to 2019 were included and assigned into training and test sets. Radiomic features were extracted from tumor 3D-ROI of baseline enhanced CT images. Radiomic features were selected by Lasso method to construct a radiomic score. A combined nomogram was established using the combination of radiomic score and clinical factors. The discriminative abilities of the radiomic, clinical and combined nomogram were quantified using ROC curve. Cox regression analysis was used to test the correlation of nomogram score with progression-free survival (PFS) and overall survival (OS). PFS and OS were compared between different risk groups by log-rank test.

Results: The radiomic, clinical and combined nomogram demonstrated AUCs of 0.81, 0.75, and 0.83 in training set; 0.79, 0.77, and 0.88 in test set. Nomogram score ≥ 1.18 was an independent prognostic factor of PFS (HR 0.22 (0.10, 0.47), p < 0.001) and OS (HR 0.38 (0.20, 0.71), p = 0.002), in training set. PFS in low-risk group were significantly longer than high-risk group in training (p < 0.001) and test (p < 0.001) set, respectively. OS in low-risk group were significantly longer than high-risk group in training (p = 0.003) and test (p = 0.009) set, respectively.

Conclusion: A nomogram combining baseline radiomic signature and clinical factors helped detecting first-line VEGFR-TKI early resistance and predicting short and long-term prognosis in mccRCC patients.

Background: The metabolic response of primary central nervous system lymphoma (PCNSL) patients has yet to be evaluated. This study aimed to assess the prognostic value of a novel scoring scale, the intracranial metabolic score (IMS), in PCNSL patients receiving end-of-therapy ¹⁸F-FDG PET/CT (EOT-PCT) and PET/MR (EOT-PMR).

Methods: The IMS was determined based on the metabolism of normal intracranial structures, including gray matter, white matter, and cerebrospinal fluid. The EOT-PCT cohort was evaluated using the IMS and commonly used Deauville score (DS). Another cohort of patients who underwent the EOT-PMR was used to validate the accuracy of the IMS.

Results: In total, 83 patients were included in the study (38 in PET/CT cohort, and 45 in PET/MR cohort). The area under the curve (AUC) values of the IMS for predicting PFS and OS were superior to those of the DS. When patients in the PET/CT cohort were stratified into five groups (respectively labeled IMS 1-5), three groups (IMS1-2, IMS 3-4, and IMS 5), or two groups (IMS1-3 and IMS4-5; IMS 1-4 and IMS 5), a higher IMS score was significantly correlated with poorer PFS and OS (p < 0.001). Similar results were observed for PFS in the PET/MR cohort (p < 0.001). The IMS and DS scale were found to be independent prognostic indicators for PFS and OS in the PET/CT cohort, and the IMS was identified as the sole independent prognostic indicator for PFS in the PET/MR cohort.

Conclusion: The IMS as a novel and effective prognostic tool for PCNSL patients, showing superior predictive value for patients' outcomes compared to the DS when assessed with EOT-PET scans.

Objective: The present study aims to investigate the ultrasound and magnetic resonance imaging (MRI) characteristics of kaposiform hemangioen dothelioma (KHE).

Methods: A retrospective analysis was conducted on the clinical data of children diagnosed with KHE through postoperative pathology. Patients were divided into two groups: the KHE group and the KHE with Kasabach-Merritt Phenomenon (KMP) group (KMP group). Laboratory indicators, ultrasound, and MRI data were collected and analyzed statistically to summarize the imaging characteristics of the disease.

Results: The levels of platelets and fibrinogen in the KHE group were significantly higher than those in the KMP group, while D-dimer levels, prothrombin time, and activated partial thromboplastin time were lower (P < 0.05). Ultrasound characteristics comparison revealed that lesions extending to the fat layer (42.47% vs. 54.24%) and invading the muscle layer (38.36% vs. 69.49%) were less common in the KHE group compared to the KMP group, with the lesion diameter being smaller in the KHE group (P < 0.05). The Adler grading predominantly showed Grade II (45.21%) in the KHE group, whereas Grade III (93.22%) was more prevalent in the KMP group (P < 0.05). MRI analysis indicated that the incidence of lesions invading the muscle layer and the presence of flow voids were lower in the KHE group compared to the KMP group (P < 0.05).

Conclusion: KHE patients with KMP exhibit lesions that are more prone to extending into the fat layer and invading the muscle layer, with larger diameters and abundant blood flow. Additionally, the MRI images of the lesions may exhibit flow voids.

Background: Postoperative progressive cerebral edema and hemorrhage (PPCEH) are major complications after meningioma resection, yet their preoperative predictive studies are limited. The aim is to develop and validate a multiparametric MRI machine learning model to predict PPCEH after meningioma resection.

Methods: This retrospective study included 148 patients with meningioma. A stratified three-fold cross-validation was used to split the dataset into training and validation sets. Radiomics features from the tumor enhancement (TE) and peritumoral brain edema (PTBE) regions were extracted from T1WI, T2WI, and ADC maps. Support vector machine constructed different radiomics models, and logistic regression explored clinical risk factors. Prediction models, integrating clinical and radiomics features, were evaluated using the area under the curve (AUC), visualized in a nomogram.

Results: The radiomics model based on TE and PTBE regions (training set mean AUC: 0.85 (95% CI: 0.78-0.93), validation set mean AUC: 0.77 (95%CI: 0.63-0.90)) outperformed the model with TE region solely (training set mean AUC: 0.83 (95% CI: 0.76-0.91), validation set mean AUC: 0.73 (95% CI: 0.58-0.87)). Furthermore, the combined model incorporating radiomics features, and clinical features of preoperative peritumoral edema and tumor boundary adhesion, had the best predictive performance, with AUC values of 0.87 (95% CI: 0.80-0.94) and 0.84 (95% CI: 0.72-0.95) for the training and validation set.

Conclusions: We developed a novel model based on clinical characteristics and multiparametric radiomics features derived from TE and PTBE regions, which can accurately and non-invasively predict PPCEH after meningioma resection. Additionally, our findings suggest the crucial role of PTBE radiomics features in understanding the potential mechanisms of PPCEH.