Cancer Imaging最新文献

Background and purpose: Radiomics offers little explainability. This study aims to develop a radiomics model (Rad-Score) using diffusion-weighted imaging (DWI) to predict high-risk patients for nodal metastasis or recurrence in endometrial cancer (EC) and corroborate with choline metabolism.

Materials and methods: From August 2015 to July 2018, 356 EC patients were enrolled. Rad-Score was developed using LASSO regression in a training cohort (n = 287) and validated in an independent test cohort (n = 69). MR spectroscopy (MRS) was also used in 230 patients. Nuclear MRS measured choline metabolites in 70 tissue samples. The performance was compared against European Society for Medical Oncology (ESMO) risk groups. A P < .05 denoted statistical significance.

Results: Rad-Score achieved 71.1% accuracy in the training and 71.0% in the testing cohorts. Incorporating clinical parameters of age, tumor type, size, and grade, Rad-Signature reached accuracies of 73.2% in training and 75.4% in testing cohorts, closely matching the performance to the post-operatively based ESMO's 70.7% and 78.3%. Rad-Score was significantly associated with increased total choline levels on MRS (P = .034) and tissue levels (P = .019).

Conclusions: Development of a preoperative radiomics risk score, comparable to ESMO clinical standard and associated with altered choline metabolism, shows translational relevance for radiomics in high-risk EC patients.

Trial registration: This study was registered in ClinicalTrials.gov on 2015-08-01 with Identifier NCT02528864.

Background: Stereotactic radiotherapy is the preferred treatment for managing patients with fewer than five brain metastases (BMs). However, some lesions recur after irradiation. The purpose of this study was to identify patients who are at a higher risk of failure, which can help in adjusting treatments and preventing recurrence.

Methods: In this retrospective multicenter study, we analyzed the predictive significance of a set of interpretable morphological features derived from contrast-enhanced (CE) T1-weighted MR images as imaging biomarkers using Kaplan-Meier analysis. The feature sets studied included the total and necrotic volumes, the surface regularity and the CE rim width. Additionally, we evaluated other nonmorphological variables and performed multivariate Cox analysis.

Results: A total of 183 lesions in 128 patients were included (median age 61 [31-95], 64 men and 64 women) treated with stereotactic radiotherapy (57% single fraction, 43% fractionated radiotherapy). None of the studied variables measured at diagnosis were found to have prognostic value. However, the total and necrotic volumes and the CE rim width measured at the first follow-up after treatment and the change in volume due to irradiation can be used as imaging biomarkers for recurrence. The optimal classification was achieved by combining the changes in tumor volume before and after treatment with the presence or absence of necrosis (p <  < 0.001).

Conclusion: This study demonstrated the prognostic significance of interpretable morphological features extracted from routine clinical MR images following irradiation in brain metastases, offering valuable insights for personalized treatment strategies.

Background: To evaluate and compare the diagnostic power of [¹⁸F]FLT-PET with ceMRI in patients with brain tumours or other focal lesions.

Methods: 121 patients with suspected brain tumour or those after brain tumour surgery were enroled in this retrospective study (61 females, 60 males, mean age 37.3 years, range 1-80 years). All patients underwent [¹⁸F]FLT-PET/MRI with gadolinium contrast agent application. In 118 of these patients, a final diagnosis was made, verified by histopathology or by follow-up. Agreement between ceMRI and [¹⁸F]FLT-PET of the whole study group was established. Further, sensitivity and specificity of ceMRI and [¹⁸F]FLT-PET were calculated for differentiation of high-grade vs. low-grade tumours, high-grade vs. low-grade tumours together with non-tumour lesions and for differentiation of high-grade tumours from all other verified lesions.

Results: [¹⁸F]FLT-PET and ceMRI findings were concordant in 119 cases (98%). On closer analysis of a subset of 64 patients with verified gliomas, the sensitivity and specificity of both PET and ceMRI were identical (90% and 84%, respectively) for differentiating low-grade from high-grade tumours, if the contrast enhancement and [¹⁸F]FLT uptake were considered as hallmarks of high-grade tumour. For differentiation of high-grade tumours from low-grade tumours and lesions of nontumorous aetiology (e.g., inflammatory lesions or post-therapeutic changes) in a subgroup of 93 patients by visual evaluation, the sensitivity of both PET and ceMRI was 90%, whereas the specificity of PET was slightly higher (61%) compared to ceMRI (57%). By receiver operating characteristic analysis, the sensitivity and specificity were 82% and 74%, respectively, when the threshold of SUVmax in the tumour was set to 0.9 g/ml.

Conclusion: We demonstrated a generally very high correlation of [¹⁸F]FLT accumulation with contrast enhancement visible on ceMRI and a comparable diagnostic yield in both modalities for differentiating high-grade tumours from low-grade tumours and lesions of other aetiology.

Objectives: This study aimed to investigate the intra- and inter-observer consistency of the Visually Accessible Rembrandt Images (VASARI) feature set before and after dichotomization, and the association between dichotomous VASARI features and the overall survival (OS) in glioblastoma (GBM) patients.

Methods: This retrospective study included 351 patients with pathologically confirmed IDH1 wild-type GBM between January 2016 and June 2022. Firstly, VASARI features were assessed by four radiologists with varying levels of experience before and after dichotomization. Cohen's kappa coefficient (κ) was calculated to measure the intra- and inter-observer consistency. Then, after adjustment for confounders using propensity score matching, Kaplan-Meier curves were used to compare OS differences for each dichotomous VASARI feature. Next, patients were randomly stratified into a training set (n = 211) and a test set (n = 140) in a 3:2 ratio. Based on the training set, Cox proportional hazards regression analysis was adopted to develop combined and clinical models to predict OS, and the performance of the models was evaluated with the test set.

Results: Eleven VASARI features with κ value of 0.61-0.8 demonstrated almost perfect agreement after dichotomization, with the range of κ values across all readers being 0.874-1.000. Seven VASARI features were correlated with GBM patient OS. For OS prediction, the combined model outperformed the clinical model in both training set (C-index, 0.762 vs. 0.723) and test set (C-index, 0.812 vs. 0.702).

Conclusion: The dichotomous VASARI features exhibited excellent inter- and intra-observer consistency. The combined model outperformed the clinical model for OS prediction.

Background: The hyperinflammatory condition and lymphoproliferation due to Epstein-Barr virus (EBV)-associated hemophagocytic lymphohistiocytosis (HLH) affect the detection of lymphomas by ¹⁸F-FDG PET/CT. We aimed to improve the diagnostic capabilities of ¹⁸F-FDG PET/CT by combining laboratory parameters.

Methods: This retrospective study involved 46 patients diagnosed with EBV-positive HLH, who underwent ¹⁸F-FDG PET/CT before beginning chemotherapy within a 4-year timeframe. These patients were categorized into two groups: EBV-associated HLH (EBV-HLH) (n = 31) and EBV-positive lymphoma-associated HLH (EBV + LA-HLH) (n = 15). We employed multivariable logistic regression and regression tree analysis to develop diagnostic models and assessed their efficacy in diagnosis and prognosis.

Results: A nomogram combining the SUVmax ratio, copies of plasma EBV-DNA, and IFN-γ reached 100% sensitivity and 81.8% specificity, with an AUC of 0.926 (95%CI, 0.779-0.988). Importantly, this nomogram also demonstrated predictive power for mortality in EBV-HLH patients, with a hazard ratio of 4.2 (95%CI, 1.1-16.5). The high-risk EBV-HLH patients identified by the nomogram had a similarly unfavorable prognosis as patients with lymphoma.

Conclusions: The study found that while ¹⁸F-FDG PET/CT alone has limitations in differentiating between lymphoma and EBV-HLH in patients with active EBV infection, the integration of a nomogram significantly improves the diagnostic accuracy and also exhibits a strong association with prognostic outcomes.

Extensive efforts have been dedicated to exploring the impact of tumor heterogeneity on cancer treatment at both histological and genetic levels. To accurately measure intra-tumoral heterogeneity, a non-invasive imaging technique, known as habitat imaging, was developed. The technique quantifies intra-tumoral heterogeneity by dividing complex tumors into distinct sub- regions, called habitats. This article reviews the following aspects of habitat imaging in cancer treatment, with a focus on radiotherapy: (1) Habitat imaging biomarkers for assessing tumor physiology; (2) Methods for habitat generation; (3) Efforts to combine radiomics, another imaging quantification method, with habitat imaging; (4) Technical challenges and potential solutions related to habitat imaging; (5) Pathological validation of habitat imaging and how it can be utilized to evaluate cancer treatment by predicting treatment response including survival rate, recurrence, and pathological response as well as ongoing open clinical trials.

Background: With the development of immune checkpoint inhibitors for the treatment of non-small cell lung cancer, the need for new functional imaging techniques and early response assessments has increased to account for new response patterns and the high cost of treatment. The present study was designed to assess the prognostic impact of dynamic contrast-enhanced computed tomography (DCE-CT) on survival outcomes in non-small cell lung cancer patients treated with immune checkpoint inhibitors.

Methods: Thirty-three patients with inoperable non-small-cell lung cancer treated with immune checkpoint inhibitors were prospectively enrolled for DCE-CT as part of their follow-up. A single target lesion at baseline and subsequent follow-up examinations were enclosed in the DCE-CT. Blood volume deconvolution (BV_decon), blood flow deconvolution (BF_decon), blood flow maximum slope (BF_{Max slope}) and permeability were assessed using overall survival (OS) and progression-free survival (PFS) as endpoints in Kaplan Meier and Cox regression analyses.

Results: High baseline Blood Volume (BV_decon) (> 12.97 ml × 100 g^-1) was associated with a favorable OS (26.7 vs 7.9 months; p = 0.050) and PFS (14.6 vs 2.5 months; p = 0.050). At early follow-up on day seven a higher relative increase in BF_decon (> 24.50% for OS and > 12.04% for PFS) was associated with an unfavorable OS (8.7 months vs 23.1 months; p < 0.025) and PFS (2.5 vs 13.7 months; p < 0.018). The relative change in BF_decon (categorical) on day seven was a predictor of OS (HR 0.26, CI95: 0.06 to 0.93 p = 0.039) and PFS (HR 0.27, CI95: 0.09 to 0.85 p = 0.026).

Conclusion: DCE-CT-identified parameters may serve as potential prognostic biomarkers at baseline and during early treatment in patients with NSCLC treated with immune checkpoint inhibitor therapy.

Background: To explore the capability of diffusion-based virtual MR elastography (vMRE) in the preoperative prediction of recurrence in hepatocellular carcinoma (HCC) and to investigate the underlying relevant histopathological characteristics.

Methods: Between August 2015 and December 2016, patients underwent preoperative MRI examination with a dedicated DWI sequence (b-values: 200,1500 s/mm²) were recruited. The ADC values and diffusion-based virtual shear modulus (μ_diff) of HCCs were calculated and MR morphological features were also analyzed. The Cox proportional hazards model was used to identify the risk factors associated with tumor recurrence. A preoperative radiologic model and postoperative model including pathological features were built to predict tumor recurrence after hepatectomy.

Results: A total of 87 patients with solitary surgically confirmed HCCs were included in this study. Thirty-five patients (40.2%) were found to have tumor recurrence after hepatectomy. The preoperative model included higher μ_diff and corona enhancement, while the postoperative model included higher μ_diff, microvascular invasion, and histologic tumor grade. These factors were identified as significant prognostic factors for recurrence-free survival (RFS) (all p < 0.05). The HCC patients with μ_diff values > 2.325 kPa showed poorer 5-year RFS after hepatectomy than patients with μ_diff values ≤ 2.325 kPa (p < 0.001). Moreover, the higher μ_diff values was correlated with the expression of CK19 (3.95 ± 2.37 vs. 3.15 ± 1.77, p = 0.017) and high Ki-67 labeling index (4.22 ± 1.63 vs. 2.72 ± 2.12, p = 0.001).

Conclusions: The μ_diff values related to the expression of CK19 and Ki-67 labeling index potentially predict RFS after hepatectomy in HCC patients.

Objective: To develop preoperative nomograms using risk factors based on clinicopathological and MRI for predicting the risk of positive surgical margin (PSM) after radical prostatectomy (RP).

Patients and methods: This study retrospectively enrolled patients who underwent prostate MRI before RP at our center between January 2015 and November 2022. Preoperative clinicopathological factors and MRI-based features were recorded for analysis. The presence of PSM (overall PSM [oPSM]) at pathology and the multifocality of PSM (mPSM) were evaluated. LASSO regression was employed for variable selection. For the final model construction, logistic regression was applied combined with the bootstrap method for internal verification. The risk probability of individual patients was visualized using a nomogram.

Results: In all, 259 patients were included in this study, and 76 (29.3%) patients had PSM, including 40 patients with mPSM. Final multivariate logistic regression revealed that the independent risk factors for oPSM were tumor diameter, frank extraprostatic extension, and annual surgery volume (all p < 0.05), and the nomogram for oPSM reached an area under the curve (AUC) of 0.717 in development and 0.716 in internal verification. The independent risk factors for mPSM included the percentage of positive cores, tumor diameter, apex depth, and annual surgery volume (all p < 0.05), and the AUC of the nomogram for mPSM was 0.790 in both development and internal verification. The calibration curve analysis showed that these nomograms were well-calibrated for both oPSM and mPSM.

Conclusions: The proposed nomograms showed good performance and were feasible in predicting oPSM and mPSM, which might facilitate more individualized management of prostate cancer patients who are candidates for surgery.

Objectives: To develop and validate a radiomics nomogram combining radiomics features and clinical factors for preoperative evaluation of Ki-67 expression status and prognostic prediction in clear cell renal cell carcinoma (ccRCC).

Methods: Two medical centers of 185 ccRCC patients were included, and each of them formed a training group (n = 130) and a validation group (n = 55). The independent predictor of Ki-67 expression status was identified by univariate and multivariate regression, and radiomics features were extracted from the preoperative CT images. The maximum relevance minimum redundancy (mRMR) and the least absolute shrinkage and selection operator algorithm (LASSO) were used to identify the radiomics features that were most relevant for high Ki-67 expression. Subsequently, clinical model, radiomics signature (RS), and radiomics nomogram were established. The performance for prediction of Ki-67 expression status was validated using area under curve (AUC), calibration curve, Delong test, decision curve analysis (DCA). Prognostic prediction was assessed by survival curve and concordance index (C-index).

Results: Tumour size was the only independent predictor of Ki-67 expression status. Five radiomics features were finally identified to construct the RS (AUC: training group, 0.821; validation group, 0.799). The radiomics nomogram achieved a higher AUC (training group, 0.841; validation group, 0.814) and clinical net benefit. Besides, the radiomics nomogram provided a highest C-index (training group, 0.841; validation group, 0.820) in predicting prognosis for ccRCC patients.

Conclusions: The radiomics nomogram can accurately predict the Ki-67 expression status and exhibit a great capacity for prognostic prediction in patients with ccRCC and may provide value for tailoring personalized treatment strategies and facilitating comprehensive clinical monitoring for ccRCC patients.