Cancer Imaging最新文献

Background: Radiotherapy is a major therapeutic approach in patients with brain tumors. However, it leads to cognitive impairments. To improve the management of radiation-induced brain sequalae, deformation-based morphometry (DBM) could be relevant. Here, we analyzed the significance of DBM using Jacobian determinants (JD) obtained by non-linear registration of MRI images to detect local vulnerability of healthy cerebral tissue in an animal model of brain irradiation.

Methods: Rats were exposed to fractionated whole-brain irradiation (WBI, 30 Gy). A multiparametric MRI (anatomical, diffusion and vascular) study was conducted longitudinally from 1 month up to 6 months after WBI. From the registration of MRI images, macroscopic changes were analyzed by DBM and microscopic changes at the cellular and vascular levels were evaluated by quantification of cerebral blood volume (CBV) and diffusion metrics including mean diffusivity (MD). Voxel-wise comparisons were performed on the entire brain and in specific brain areas identified by DBM. Immunohistology analyses were undertaken to visualize the vessels and astrocytes.

Results: DBM analysis evidenced time-course of local macrostructural changes; some of which were transient and some were long lasting after WBI. DBM revealed two vulnerable brain areas, namely the corpus callosum and the cortex. DBM changes were spatially associated to microstructural alterations as revealed by both diffusion metrics and CBV changes, and confirmed by immunohistology analyses. Finally, matrix correlations demonstrated correlations between JD/MD in the early phase after WBI and JD/CBV in the late phase both in the corpus callosum and the cortex.

Conclusions: Brain irradiation induces local macrostructural changes detected by DBM which could be relevant to identify brain structures prone to radiation-induced tissue changes. The translation of these data in patients could represent an added value in imaging studies on brain radiotoxicity.

Background: To explore the pulmonary-vascular-stump filling-defect on CT and investigate its association with cancer progression.

Methods: Records in our institutional database from 2018 to 2022 were retrospectively analyzed to identify filling-defects in the pulmonary-vascular-stump after lung cancer resection and collect imaging and clinical data of patients.

Results: Among the 1714 patients analyzed, 95 cases of filling-defects in the vascular stump after lung cancer resection were identified. After excluding lost-to-follow-up cases, a total of 77 cases were included in the final study. Morphologically, the filling-defects were dichotomized as 46 convex-shape and 31 concave-shape cases. Concave defects exhibited a higher incidence of increase compared to convex defects (51.7% v. 9.4%, P = 0.001). Among 61 filling defects in the pulmonary arterial stump, four (6.5%) increasing concave defects showed the nuclide concentration on PET and extravascular extension. The progression-free survival (PFS) time differed significantly among the concave, convex, and non-filling-defect groups (log-rank P < 0.0001), with concave defects having the shortest survival time. Multivariate Cox proportional hazards analysis indicated that the shape of filling-defects independently predicted PFS in early onset on CT (HR: 0.46; 95% CI: 0.39-1.99; P = 0.04). In follow-ups, the growth of filling-effects was an independent predictor of PFS (HR: 0.26; 95% CI: 0.11-0.65; P = 0.004).

Conclusions: Certain filling-defects in the pulmonary-arterial-stump post lung tumor resection exhibit malignant growth. In the early onset of filling-defects on CT, the concave-shape independently predicted cancer-progression, while during the subsequent follow-up, the growth of filling-defects could be used independently to forecast cancer-progression.

Background: Dynamic contrast-enhanced-MRI (DCE-MRI) is able to study bone marrow angiogenesis in patients with multiple myeloma (MM) and asymptomatic precursor diseases but its role in the management of MM has not yet been established. The aims of this prospective study was to compare DCE-MRI-based parameters between all monoclonal plasma cell disease stages in order to find out discriminatory parameters and to seek correlations with other diffusion-weighted MRI and positron emission tomography (PET)-based biomarkers in a hybrid simultaneous whole-body-2-[18F]fluorodeoxyglucose (FDG)-PET/MRI (WB-2-[18F]FDG-PET/MRI) imaging approach.

Methods: Patients with newly diagnosed Monoclonal gammopathy of undetermined significance (MGUS), smoldering multiple myeloma (SMM) or symptomatic MM according to international myeloma working group and underwent WB-2-[18F]FDG-PET/MRI imaging including bone marrow DCE sequences at the Nantes University Hospital were prospectively enrolled in this study before receiving treatment.

Results: One hundred and sixty-seven patients (N = 167, mean age: 64 years ± 11 [Standard deviation], 66 males) were considered for the analysis. DCE-MRI-based Peak Enhancement Intensity (PEI), Time to PEI (TPEI) and their maximum intensity time ratio (MITR: PEI/TPEI) values were significantly different between the different monoclonal plasma cell disease stages, PEI values increasing and TPEI values decreasing progressively along the spectrum of plasma cell disorders, from MGUS stage to symptomatic multiple myeloma. PEI values were significantly higher in patients with diffuse bone marrow involvement (either in PET or in MRI images) than in those without diffuse bone marrow involvement, unlike TPEI values. PEI and TPEI values were not significantly different between patients with or without focal bone lesions.

Conclusion: Different DCE-MRI-based parameters (PEI, TPEI, MITR) could significantly differentiate all monoclonal plasma cell disease stages and complemented conventional MRI and PET-based biomarkers.

Background: Exploring the value of baseline and early ¹⁸F-FDG PET/CT evaluations in prediction PFS in ER+/HER2- metastatic breast cancer patients treated with a cyclin-dependent kinase inhibitor in combination with an endocrine therapy.

Methods: Sixty-six consecutive breast cancer patients who underwent a pre-therapeutic ¹⁸F-FDG PET/CT and a second PET/CT within the first 6 months of treatment were retrospectively included. Metabolic tumour volume (MTV) and total lesion glycolysis (TLG) and D_max, which represents tumour dissemination and is defined as the distance between the two most distant lesions, were computed. The variation in these parameters between baseline and early evaluation PET as well as therapeutic evaluation using PERCIST were assessed as prognosticators of PFS at 18 months.

Results: The median follow-up was equal to 22.5 months. Thirty progressions occurred (45.4%). The average time to event was 17.8 ± 10.4 months. At baseline, D_max was the only predictive metabolic parameter. Patients with a baseline D_max ≤ 18.10 cm had a significantly better 18 m-PFS survival than the others: 69.2% (7.7%) versus 36.7% (8.8%), p = 0.017. There was no association between PERCIST evaluation and 18 m-PFS status (p = 0.149) and there was no difference in 18 m-PFS status between patients classified as complete, partial metabolic responders or having stable metabolic disease.

Conclusion: Disease spread at baseline PET, as assessed by D_max, is predictive of an event occurring within 18 months. In the absence of early metabolic progression, which occurs in 15% of patients, treatment should be continued regardless of the quality of the initial response to treatment.

Background: High b-value diffusion-weighted images (DWI) are used for detection of clinically significant prostate cancer (csPCa). This study qualitatively and quantitatively compares synthesized DWI (sDWI) to acquired (aDWI) for detection of csPCa.

Methods: One hundred fifty-one consecutive patients who underwent prostate MRI and biopsy were included in the study. Axial DWI with b = 0, 500, 1000, and 2000 s/mm² using a 3T clinical scanner using a 32-channel phased-array body coil were acquired. We retrospectively synthesized DWI for b = 2000 s/mm² via extrapolation based on mono-exponential decay, using b = 0 and b = 500 s/mm² (sDWI₅₀₀) and b = 0, b = 500 s/mm², and b = 1000 s/mm² (sDWI₁₀₀₀). Differences in signal intensity between sDWI and aDWI were evaluated within different regions of interest (prostate alone, prostate plus 5 mm, 30 mm and 70 mm margin and full field of view). The maximum DWI value within each ROI was evaluated for prediction of csPCa. Classification accuracy was compared to Restriction Spectrum Imaging restriction score (RSIrs), a previously validated biomarker based on multi-exponential DWI. Discrimination of csPCa was evaluated via area under the receiver operating characteristic curve (AUC).

Results: Within the prostate, mean ± standard deviation of percent mean differences between sDWI and aDWI signal were -46 ± 35% for sDWI₁₀₀₀ and -67 ± 24% for sDWI₅₀₀. AUC for aDWI, sDWI_500, sDWI₁₀₀₀, and RSIrs within the prostate 0.62[95% confidence interval: 0.53, 0.71], 0.63[0.54, 0.72], 0.65[0.56, 0.73] and 0.78[0.71, 0.86], respectively.

Conclusion: sDWI is qualitatively comparable to aDWI within the prostate. However, hyperintense artifacts are introduced with sDWI in the surrounding pelvic tissue that interfere with quantitative cancer detection and might mask metastases. In the prostate, RSIrs yields superior quantitative csPCa detection than sDWI or aDWI.

Background: The aim of the study were as below. (1) To investigate the feasibility of intravoxel incoherent motion (IVIM)-based virtual magnetic resonance elastography (vMRE) to provide quantitative estimates of tissue stiffness in pulmonary neoplasms. (2) To verify the diagnostic performance of shifted apparent diffusion coefficient (sADC) and reconstructed virtual stiffness values in distinguishing neoplasm nature.

Methods: This study enrolled 59 patients (37 males, 22 females) with one pulmonary neoplasm who underwent computed tomography-guided percutaneous transthoracic needle biopsy (PTNB) with pathological diagnosis (26 adenocarcinoma, 10 squamous cell carcinoma, 3 small cell carcinoma, 4 tuberculosis and 16 non-specific benign; mean age, 60.81 ± 9.80 years). IVIM was performed on a 3 T magnetic resonance imaging scanner before biopsy. sADC and virtual shear stiffness maps reflecting lesion stiffness were reconstructed. sADC and virtual stiffness values of neoplasm were extracted, and the diagnostic performance of vMRE in distinguishing benign and malignant and detailed pathological type were explored.

Results: Compared to benign neoplasms, malignant ones had a significantly lower sADC and a higher virtual stiffness value (P < 0.001). Subsequent subtype analyses showed that the sADC values of adenocarcinoma and squamous cell carcinoma groups were significantly lower than non-specific benign group (P = 0.013 and 0.001, respectively). Additionally, virtual stiffness values of the adenocarcinoma and squamous cell carcinoma subtypes were significantly higher than non-specific benign group (P = 0.008 and 0.001, respectively). However, no significant correlation was found among other subtype groups.

Conclusions: Non-invasive vMRE demonstrated diagnostic efficiency in differentiating the nature of pulmonary neoplasm. vMRE is promising as a new method for clinical diagnosis.

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.

Purpose: To develop a radiomics-based model using [⁶⁸Ga]Ga-PSMA PET/CT to predict postoperative adverse pathology (AP) in patients with biopsy Gleason Grade Group (GGG) 1-2 prostate cancer (PCa), assisting in the selection of patients for active surveillance (AS).

Methods: A total of 75 men with biopsy GGG 1-2 PCa who underwent radical prostatectomy (RP) were enrolled. The patients were randomly divided into a training group (70%) and a testing group (30%). Radiomics features of entire prostate were extracted from the [⁶⁸Ga]Ga-PSMA PET scans and selected using the minimum redundancy maximum relevance algorithm and the least absolute shrinkage and selection operator regression model. Logistic regression analyses were conducted to construct the prediction models. Receiver operating characteristic (ROC) curve, decision curve analysis (DCA), and calibration curve were employed to evaluate the diagnostic value, clinical utility, and predictive accuracy of the models, respectively.

Results: Among the 75 patients, 30 had AP confirmed by RP. The clinical model showed an area under the curve (AUC) of 0.821 (0.695-0.947) in the training set and 0.795 (0.603-0.987) in the testing set. The radiomics model achieved AUC values of 0.830 (0.720-0.941) in the training set and 0.829 (0.624-1.000) in the testing set. The combined model, which incorporated the Radiomics score (Radscore) and free prostate-specific antigen (FPSA)/total prostate-specific antigen (TPSA), demonstrated higher diagnostic efficacy than both the clinical and radiomics models, with AUC values of 0.875 (0.780-0.970) in the training set and 0.872 (0.678-1.000) in the testing set. DCA showed that the net benefits of the combined model and radiomics model exceeded those of the clinical model.

Conclusion: The combined model shows potential in stratifying men with biopsy GGG 1-2 PCa based on the presence of AP at final pathology and outperforms models based solely on clinical or radiomics features. It may be expected to aid urologists in better selecting suitable patients for AS.