Cancer Imaging最新文献

Background: Current diagnostic imaging modalities have limited ability to differentiate between malignant and benign pancreaticobiliary disease, and lack accuracy in detecting lymph node metastases. ¹⁸F-Prostate-Specific Membrane Antigen (PSMA) PET/CT is an imaging modality used for staging of prostate cancer, but has incidentally also identified PSMA-avid pancreatic lesions, histologically characterized as pancreatic ductal adenocarcinoma (PDAC). This phase I/II study aimed to assess the feasibility of ¹⁸F-PSMA PET/CT to detect PDAC.

Methods: Seventeen patients with clinically resectable PDAC underwent ¹⁸F-PSMA PET/CT prior to surgical resection. Images were analyzed both visually and (semi)quantitatively by deriving the maximum standardized uptake value (SUV_max) and tumor-to-background ratio (TBR). TBR was defined as the ratio between SUV_max of the primary tumor divided by SUV_max of the aortic blood pool. Finally, tracer uptake on PET was correlated to tissue expression of PSMA in surgical specimens.

Results: Out of 17 PSMA PET/CT scans, 13 scans demonstrated positive PSMA tracer uptake, with a mean SUV_max of 5.0 ± 1.3. The suspected primary tumor was detectable (TBR ≥ 2) with a mean TBR of 3.3 ± 1.3. For histologically confirmed PDAC, mean SUV_max and mean TBR were 4.9 ± 1.2 and 3.3 ± 1.5, respectively. Although eight patients had histologically confirmed regional lymph node metastases and two patients had distant metastases, none of these metastases demonstrated ¹⁸F-PSMA uptake. There was no correlation between ¹⁸F-PSMA PET/CT SUV_max and tissue expression of PSMA in surgical specimens.

Conclusions: ¹⁸F-PSMA PET/CT was able to detect several pancreaticobiliary cancers, including PDAC. However, uptake was generally low, not specific to PDAC and no tracer uptake was observed in lymph node or distant metastases. The added value of PSMA PET in this setting appears to be limited.

Trial registration: The trial is registered as PANSCAN-2 in the European Clinical Trials Database (EudraCT number: 2020-002185-14).

Background: Prostate cancer (PCa) is the leading cause of cancer-related morbidity and mortality in men worldwide. An early and accurate diagnosis is crucial for effective treatment and prognosis. Traditional invasive procedures such as image-guided prostate biopsy often cause discomfort and complications, deterring some patients from undergoing these necessary tests. This study aimed to explore the feasibility and clinical value of using ultrasound super-resolution imaging (US SRI) for non-invasively assessing the microvessel characteristics of prostate lesion.

Methods: This study included 127 patients with prostate lesion who presented at Renmin Hospital of Wuhan University between November 2023 and June 2024 were included in this study. All the patients underwent transrectal US (TRUS), contrast-enhanced US (CEUS), and US SRI. CEUS parameters of time-intensity curve (TIC): arrival time (AT), rising time (RT), time to peak (TTP), peak intensity (PKI), falling time (FT), mean transit time (MTT), ascending slope (AS), descending slope (DS), D/A slope ratio (SR), and area under the TIC (AUC). US SRI parameters: microvessel density (MVD), microvessel diameter (D), microvessel velocity (V), microvessel tortuosity (T), and fractal number (FN), were analyzed and compared between prostate benign and malignant lesion.

Results: The tumor markers of prostate in the malignant group were all higher than those in the benign group, and the differences were statistically significant (P < 0.001). The TIC parameters of CEUS revealed that the PKI, AS, DS, and AUC were significantly higher in the malignant group than in the benign group (P < 0.001), whereas the RT, TTP and FT in the malignant group were significantly lower (P < 0.001). Malignant lesion exhibited significantly higher MVD, larger D, faster V, greater T, and more complex FN than benign lesion (P < 0.001).

Conclusions: US SRI is a promising non-invasive imaging modality that can provide detailed microvessel characteristics of prostate lesion, offering an advancement in the differential diagnosis for prostate lesion. And, US SRI may be a valuable tool in clinical practice with its ability to display and quantify microvessel with high precision.

Purpose: To assess and compare the diagnostic efficiency of histogram analysis of monochromatic and iodine images derived from spectral CT in predicting Ki-67 expression in gastric gastrointestinal stromal tumors (gGIST).

Methods: Sixty-five patients with gGIST who underwent spectral CT were divided into a low-level Ki-67 expression group (LEG, Ki-67 < 10%, n = 33) and a high-level Ki-67 expression group (HEG, Ki-67 ≥ 10%, n = 32). Conventional CT features were extracted and compared. Histogram parameters were extracted from monochromatic and iodine images, respectively. The diagnostic efficiency of the histogram parameters from monochromatic and iodine images was assessed and compared between the two groups. Spearman's correlation analysis was used to correlate histogram parameters with Ki-67 expression.

Results: The HEG was more likely to present with an irregular shape and a larger size than the LEG (all p < 0.05). Regarding histogram parameters, the HEG showed higher maximum, mean, Perc.10, Perc.25, Perc.50, Perc.75, Perc.90, Perc.99, SD, variance, and CV of monochromatic images; higher maximum, Perc.99, and entropy of iodine images, compared with the LEG (all p < 0.003125). ROC analysis showed that significant histogram parameters of monochromatic and iodine images allowed for effective differentiation between LEG and HEG. DeLong's test showed that the diagnostic efficiency of histogram parameters in monochromatic images (Perc.90) was superior to that of iodine images (maximum) (p = 0.010). A positive correlation was observed between the significant histogram parameters and Ki-67 expression (all p < 0.05).

Conclusion: Both histogram analysis of monochromatic and iodine images derived from spectral CT can predict Ki-67 expression in gGIST, and the diagnostic efficacy of monochromatic images is superior to iodine images.

Objective: This study aims to evaluate the effectiveness of deep learning features derived from multi-sequence magnetic resonance imaging (MRI) in determining the O⁶-methylguanine-DNA methyltransferase (MGMT) promoter methylation status among glioblastoma patients.

Methods: Clinical, pathological, and MRI data of 356 glioblastoma patients (251 methylated, 105 unmethylated) were retrospectively examined from the public dataset The Cancer Imaging Archive. Each patient underwent preoperative multi-sequence brain MRI scans, which included T1-weighted imaging (T1WI) and contrast-enhanced T1-weighted imaging (CE-T1WI). Regions of interest (ROIs) were delineated to identify the necrotic tumor core (NCR), enhancing tumor (ET), and peritumoral edema (PED). The ET and NCR regions were categorized as intratumoral ROIs, whereas the PED region was categorized as peritumoral ROIs. Predictive models were developed using the Transformer algorithm based on intratumoral, peritumoral, and combined MRI features. The area under the receiver operating characteristic curve (AUC) was employed to assess predictive performance.

Results: The ROI-based models of intratumoral and peritumoral regions, utilizing deep learning algorithms on multi-sequence MRI, were capable of predicting MGMT promoter methylation status in glioblastoma patients. The combined model of intratumoral and peritumoral regions exhibited superior diagnostic performance relative to individual models, achieving an AUC of 0.923 (95% confidence interval [CI]: 0.890 - 0.948) in stratified cross-validation, with sensitivity and specificity of 86.45% and 87.62%, respectively.

Conclusion: The deep learning model based on MRI data can effectively distinguish between glioblastoma patients with and without MGMT promoter methylation.

Purpose: Staging of non-small cell lung cancer (NSCLC) is commonly based on [¹⁸F]FDG PET/CT, in particular to exclude distant metastases and guide local therapy approaches like resection and radiotherapy. Although it is hoped that PET/CT will increase the value of primary staging compared to conventional imaging, it is generally limited to the characterization of TNM. The first aim of this study was to evaluate the PET parameter metabolic tumor volume (MTV) above liver background uptake as a prognostic marker in lung cancer. The second aim was to investigate the possibility of incorporating MTV into the TNM classification system for disease prognosis in locally advanced NSCLC treated with chemoradiotherapy.

Methods: Retrospective evaluation of 235 patients with histologically proven, locally advanced NSCLC from the multi-centre randomized clinical PETPLAN trial and a clinical cohort from a hospital registry. The PET parameters SUVmax, SULpeak, MTV and TLG above liver background uptake were determined. Kaplan-Meier curves and stratified Cox proportional hazard regression models were used to investigate the prognostic value of PET parameters and TNM along with clinical variables. Subgroup analyses were performed to compare hazard ratios according to TNM, MTV, and the two variables combined.

Results: In the multivariable Cox regression analysis, MTV was associated with significantly worse overall survival independent of stage and other prognostic variables. In locally advanced disease stages treated with chemoradiotherapy, higher MTV was significantly associated with worse survival (median 17 vs. 32 months). Using simple cut-off values (45 ml for stage IIIa, 48 ml for stage IIIb, and 105 ml for stage IIIc), MTV was able to further predict differences in survival for stages IIIa-c. The combination of TNM and MTV staging system showed better discrimination for overall survival in locally advanced disease stages, compared to TNM alone.

Conclusion: Higher metabolic tumor volume is significantly associated with worse overall survival and combined with TNM staging, it provides more precise information about the disease prognosis in locally advanced NSCLC treated with chemoradiotherapy compared to TNM alone. As a PET parameter with volumetric information, MTV represents a useful addition to TNM.

Background: Gastrointestinal stromal tumors (GISTs) are the most common mesenchymal tumors of the gastrointestinal tract. Recent advent of tyrosine kinase inhibitors (TKIs) has significantly improved the prognosis of GIST patients. However, responses to TKI therapy can vary depending on the specific gene mutation. D842V, which is the most common mutation in platelet-derived growth factor receptor alpha exon 18, shows no response to imatinib and sunitinib. Radiomics features based on venous-phase contrast-enhanced computed tomography (CECT) have shown potential in non-invasive prediction of GIST genotypes. This study sought to determine whether radiomics features could help distinguish GISTs with D842V mutations.

Methods: A total of 872 pathologically confirmed GIST patients with CECT data available from three independent centers were included and divided into the training cohort ( $n=487$ ) and the external validation cohort ( $n=385$ ). Clinical features including age, sex, tumor size and location were collected. Radiomics features on the largest axial image of venous-phase CECT were analyzed and a total of two radiomics features were selected after feature selection. Random forest models trained on non-radiomics features only (the non-radiomics model) and on both non-radiomics and radiomics features (the combined model) were compared.

Results: The combined model showed better average precision (0.250 vs. 0.102, p = 0.039) and F1 score (0.253 vs. 0.155, p = 0.012) than the non-radiomics model. There was no significant difference in ROC-AUC (0.728 vs. 0.737, p = 0.836) and geometric mean (0.737 vs. 0.681, p = 0.352).

Conclusions: This study demonstrated the potential of radiomics features based on venous-phase CECT images to identify D842V mutation in GISTs. Our model may provide an alternative approach to guide TKI therapy for patients inaccessible to sequence variant testing, potentially improving treatment outcomes for GIST patients especially in resource-limited settings.

Background: The complex interactions of the tumor micromilieu may be reflected by diffusion-weighted imaging (DWI) derived from the magnetic resonance imaging (MRI). The present study investigated the association between apparent diffusion coefficient (ADC) values and histopathologic features in uterine cervical cancer.

Methods: In this retrospective study, prebiopsy MRI was used to analyze histogram ADC-parameters. The biopsy specimens were stained for Ki-67, E-cadherin, vimentin and tumor-infiltrating lymphocytes (TIL, all CD45 positive cells). Tumor-stroma ratio (TSR) was calculated on routine H&E specimens. Spearman's correlation analysis and receiver-operating characteristics curves were used as statistical analyses.

Results: The patient sample comprised 70 female patients (age range 32-79 years; mean age 55.4 years) with squamous cell cervical carcinoma. The interreader agreement was high ranging from intraclass coefficient (ICC) = 0.71 for entropy to ICC = 0.96 for ADCmedian. Several ADC-histogram parameters correlated strongly with the TSR. The highest correlation coefficient achieved p10 (r = -0.81, p < 0.0001). ADCmean can predict tumors with high TSR, AUC: 0.91, sensitivity: 0.91 (95% CI 0.77;0.96), specificity: 0.91 (95% CI 0.78;0.97). Several ADC-histogram parameters correlated slightly with the proliferation index Ki-67. No associations were found with TIL, E-Cadherin and vimentin. In well and moderately differentiated cancers, ADC histogram values showed stronger correlations with Ki-67 and TSR than in poorly differentiated tumors.

Conclusion: ADC values are strongly associated with tumor-stroma ratio. The ADC mean can be used to predict tumors with high TSR. Associations between histopathology and ADC values depend on tumor differentiation. ADC values show only weak associations with Ki-67 and none with TIL, vimentin and E-cadherin.

Objective: To assess the utility of multiparametric MRI and clinical indicators in distinguishing nuclear grade and survival of clear cell renal cell carcinoma (ccRCC) complicated with venous tumor thrombus (VTT).

Materials and methods: This study included 105 and 27 patients in the training and test sets, respectively. Preoperative MRI, including intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI), was performed. Renal lesions were evaluated for IVIM-DWI metrics and conventional MRI features. All the patients had postoperative histologically proven ccRCC and VTT. An expert uropathologist reviewed all specimens to confirm the nuclear grade of the World Health Organization/ International Society of Urological Pathology (WHO/ISUP) of the tumor. Univariate and multivariable logistic regression analyses were used to select the preoperative imaging features and clinical indicators. The predictive ability of the logistic regression model was assessed using receiver operating characteristic (ROC) analysis. Survival curves were plotted using the Kaplan-Meier method.

Results: High WHO/ISUP nuclear grade was confirmed in 69 of 105 patients (65.7%) in the training set and 19 of 27 patients (70.4%) in the test set, respectively (P = 0.647). D_{p_ROI_Low}, tumor size, serum albumin, platelet count, and lymphocyte count were independently related to high WHO/ISUP nuclear grade in the training set. The model identified high WHO/ISUP nuclear grade well, with an AUC of 0.817 (95% confidence interval [CI]: 0.735-0.899), a sensitivity of 70.0%, and a specificity of 77.8% in the training set. In the independent test set, the model demonstrated an AUC of 0.766 (95% CI, 0.567-0.966), a sensitivity of 79.0%, and a specificity of 75.0%. Kaplan-Meier analysis showed that the predicted high WHO/ISUP nuclear grade group had poorer progression-free survival than the low WHO/ISUP nuclear grade group in both the training and test sets (P = 0.001 and P = 0.021).

Conclusions: IVIM-DWI-derived parameters and clinical indicators can be used to differentiate nuclear grades and predict progression-free survival of ccRCC and VTT.

Background: To verify overall survival predictions made with residual convolutional neural network-determined morphological response (ResNet-MR) in patients with unresectable synchronous liver-only metastatic colorectal cancer (mCRC) treated with bevacizumab-based chemotherapy (BBC).

Methods: A retrospective review of liver-only mCRC patients treated with BBC from December 2011 to Apr 2021 was performed. Patients who had metachronous liver metastases or received locoregional treatment before the initiation of BBC were excluded. The percentage of downstaging to curative treatment and overall survival (OS) were recorded. Two abdominal radiologists evaluated portal venous phase CT images based on the morphological criteria and divided the images into Groups 1, 2, and 3. These images were used to establish the radiologists-determined morphological response (RD-MR), which classified patients into responders and non-responders based on the morphological change 3 months after the initiation of BBC. Then, the Group 1 and 3 images classified by the radiologists were input into ResNet as the training dataset. The trained ResNet then redivided the Group 2 images into Groups 1, 2 and 3. The ResNet-MR was determined on the basis of these redivided images and the initial Group 1 and 3 images classified by the radiologists.

Results: Eighty-four patients were included in this study (53 males and 31 females, with a median age of 60.0 years). The follow-up time ranged from 10 to 86 months. A total of 407 CT images were input into ResNet as the training dataset. Both RD-MR and ResNet-MR correlated with OS (p value = 0.0167 and 0.0225, respectively). Regarding discriminatory ability for mortality, ResNet-MR had higher area under curve than RD-MR at both 1 year and 2 years and showed a significant difference in discriminatory ability (p-value = 0.0321) at 2 years. RD-MR classified 28 patients (33.3%) as responders, and ResNet-MR classified an additional 16 patients (19.0%) as responders; these 16 patients had longer OS than the remaining non-responders in the RD-MR group (27.49 versus 21.20 months, p value = 0.043) and had a higher percentage of downstaging (37.5% versus 17.5%, p value = 0.1610).

Conclusions: In CRC patients with liver metastases treated with BBC, prediction of survival can be improved with the aid of ResNet, enabling optimized individualized treatment.

Objective: To develop a multimodal predictive model, Radiomics Integrated TLSs System (RAITS), based on preoperative CT radiomic features for the identification of TLSs in stage I lung adenocarcinoma patients and to evaluate its potential in prognosis stratification and guiding personalized treatment.

Methods: The most recent preoperative chest CT thin-slice scans and postoperative hematoxylin and eosin-stained pathology sections of patients diagnosed with stage I LUAD were retrospectively collected. Tumor segmentation was achieved using an automatic virtual adversarial training segmentation algorithm based on a three-dimensional U-shape convolutional neural network (3D U-Net). Radiomic features were extracted from the tumor and peritumoral areas, with extensions of 2 mm, 4 mm, 6 mm, and 8 mm, respectively, and deep learning image features were extracted through a convolutional neural network. Subsequently, the RAITS was constructed. The performance of RAITS was then evaluated in both the train and validation cohorts.

Results: RAITS demonstrated superior AUC, sensitivity, and specificity in both the training and external validation cohorts, outperforming traditional unimodal models. In the validation cohort, RAITS achieved an AUC of 0.78 (95% CI, 0.69-0.88) and showed higher net benefits across most threshold ranges. RAITS exhibited strong discriminative ability in risk stratification, with p < 0.01 in the training cohort and p = 0.02 in the validation cohort, consistent with the actual predictive performance of TLSs, where TLS-positive patients had significantly higher recurrence-free survival (RFS) compared to TLS-negative patients (p = 0.04 in the training cohort, p = 0.02 in the validation cohort).

Conclusion: As a multimodal predictive model based on preoperative CT radiomic features, RAITS demonstrated excellent performance in identifying TLSs in stage I LUAD and holds potential value in clinical decision-making.