Cancer Imaging最新文献

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.

Background: Recently, the application of deep learning (DL) has made great progress in various fields, especially in cancer research. However, to date, the bibliometric analysis of the application of DL in cancer is scarce. Therefore, this study aimed to explore the research status and hotspots of the application of DL in cancer.

Methods: We retrieved all articles on the application of DL in cancer from the Web of Science database Core Collection database. Biblioshiny, VOSviewer and CiteSpace were used to perform the bibliometric analysis through analyzing the numbers, citations, countries, institutions, authors, journals, references, and keywords.

Results: We found 6,016 original articles on the application of DL in cancer. The number of annual publications and total citations were uptrend in general. China published the greatest number of articles, USA had the highest total citations, and Saudi Arabia had the highest centrality. Chinese Academy of Sciences was the most productive institution. Tian, Jie published the greatest number of articles, while He Kaiming was the most co-cited author. IEEE Access was the most popular journal. The analysis of references and keywords showed that DL was mainly used for the prediction, detection, classification and diagnosis of breast cancer, lung cancer, and skin cancer.

Conclusions: Overall, the number of articles on the application of DL in cancer is gradually increasing. In the future, further expanding and improving the application scope and accuracy of DL applications, and integrating DL with protein prediction, genomics and cancer research may be the research trends.

Background: This study aimed to quantitatively reveal contributing factors to airway navigation failure during radial probe endobronchial ultrasound (R-EBUS) by using geometric analysis in a three-dimensional (3D) space and to investigate the clinical feasibility of prediction models for airway navigation failure.

Methods: We retrospectively reviewed patients who underwent R-EBUS between January 2017 and December 2018. Geometric quantification was analyzed using in-house software built with open-source python libraries including the Vascular Modeling Toolkit ( http://www.vmtk.org ), simple insight toolkit ( https://sitk.org ), and sci-kit image ( https://scikit-image.org ). We used a machine learning-based approach to explore the utility of these significant factors.

Results: Of the 491 patients who were eligible for analysis (mean age, 65 years +/- 11 [standard deviation]; 274 men), the target lesion was reached in 434 and was not reached in 57. Twenty-seven patients in the failure group were matched with 27 patients in the success group based on propensity scores. Bifurcation angle at the target branch, the least diameter of the last section, and the curvature of the last section are the most significant and stable factors for airway navigation failure. The support vector machine can predict airway navigation failure with an average area under the curve of 0.803.

Conclusions: Geometric analysis in 3D space revealed that a large bifurcation angle and a narrow and tortuous structure of the closest bronchus from the lesion are associated with airway navigation failure during R-EBUS. The models developed using quantitative computer tomography scan imaging show the potential to predict airway navigation failure.

Background: 3D reconstruction of Wilms' tumor provides several advantages but are not systematically performed because manual segmentation is extremely time-consuming. The objective of our study was to develop an artificial intelligence tool to automate the segmentation of tumors and kidneys in children.

Methods: A manual segmentation was carried out by two experts on 14 CT scans. Then, the segmentation of Wilms' tumor and neoplastic kidney was automatically performed using the CNN U-Net and the same CNN U-Net trained according to the OV²ASSION method. The time saving for the expert was estimated depending on the number of sections automatically segmented.

Results: When segmentations were performed manually by two experts, the inter-individual variability resulted in a Dice index of 0.95 for tumor and 0.87 for kidney. Fully automatic segmentation with the CNN U-Net yielded a poor Dice index of 0.69 for Wilms' tumor and 0.27 for kidney. With the OV²ASSION method, the Dice index varied depending on the number of manually segmented sections. For the segmentation of the Wilms' tumor and neoplastic kidney, it varied respectively from 0.97 to 0.94 for a gap of 1 (2 out of 3 sections performed manually) to 0.94 and 0.86 for a gap of 10 (1 section out of 6 performed manually).

Conclusion: Fully automated segmentation remains a challenge in the field of medical image processing. Although it is possible to use already developed neural networks, such as U-Net, we found that the results obtained were not satisfactory for segmentation of neoplastic kidneys or Wilms' tumors in children. We developed an innovative CNN U-Net training method that makes it possible to segment the kidney and its tumor with the same precision as an expert while reducing their intervention time by 80%.

Background: Numerous studies have shown that magnetic resonance imaging (MRI)-targeted biopsy approaches are superior to traditional systematic transrectal ultrasound guided biopsy (TRUS-Bx). The optimal number of biopsy cores to be obtained per lesion identified on multiparametric MRI (mpMRI) images, however, remains a matter of debate. The aim of this study was to evaluate the incremental value of additional biopsy cores in an MRI-targeted "in-bore"-biopsy (MRI-Bx) setting.

Patients and methods: Two hundred and forty-five patients, who underwent MRI-Bx between June 2014 and September 2021, were included in this retrospective single-center analysis. All lesions were biopsied with at least five biopsy cores and cumulative detection rates for any cancer (PCa) as well as detection rates of clinically significant cancers (csPCa) were calculated for each sequentially labeled biopsy core. The cumulative per-core detection rates are presented as whole numbers and as proportion of the maximum detection rate reached, when all biopsy cores were considered. CsPCa was defined as Gleason Score (GS) ≥ 7 (3 + 4).

Results: One hundred and thirty-two of 245 Patients (53.9%) were diagnosed with prostate cancer and csPCa was found in 64 (26.1%) patients. The first biopsy core revealed csPCa/ PCa in 76.6% (49/64)/ 81.8% (108/132) of cases. The second, third and fourth core found csPCa/ PCa not detected by previous cores in 10.9% (7/64)/ 8.3% (11/132), 7.8% (5/64)/ 5.3% (7/132) and 3.1% (2/64)/ 3% (4/132) of cases, respectively. Obtaining one or more cores beyond the fourth biopsy core resulted in an increase in detection rate of 1.6% (1/64)/ 1.5% (2/132).

Conclusion: We found that obtaining five cores per lesion maximized detection rates. If, however, future research should establish a clear link between the incidence of serious complications and the number of biopsy cores obtained, a three-core biopsy might suffice as our results suggest that about 95% of all csPCa are detected by the first three cores.

Objective: This study was based on MRI features and number of tumor-infiltrating CD8 + T cells in post-operative pathology, in predicting meningioma recurrence risk.

Methods: Clinical, pathological, and imaging data of 102 patients with surgically and pathologically confirmed meningiomas were retrospectively analyzed. Patients were divided into recurrence and non-recurrence groups based on follow-up. Tumor-infiltrating CD8 + T cells in tissue samples were quantitatively assessed with immunohistochemical staining. Apparent diffusion coefficient (ADC) histogram parameters from preoperative MRI were quantified in MaZda. Considering the high correlation between ADC histogram parameters, we only chose ADC histogram parameter that had the best predictive efficacy for COX regression analysis further. A visual nomogram was then constructed and the recurrence probability at 1- and 2-years was determined. Finally, subgroup analysis was performed with the nomogram.

Results: The risk factors for meningioma recurrence were ADCp1 (hazard ratio [HR] = 0.961, 95% confidence interval [95% CI]: 0.937 ~ 0.986, p = 0.002) and CD8 + T cells (HR = 0.026, 95%CI: 0.001 ~ 0.609, p = 0.023). The resultant nomogram had AUC values of 0.779 and 0.784 for 1- and 2-years predicted recurrence rates, respectively. The survival analysis revealed that patients with low CD8 + T cells counts or ADCp1 had higher recurrence rates than those with high CD8 + T cells counts or ADCp1. Subgroup analysis revealed that the AUC of nomogram for predicting 1-year and 2-year recurrence of WHO grade 1 and WHO grade 2 meningiomas was 0.872 (0.652) and 0.828 (0.751), respectively.

Conclusions: Preoperative ADC histogram parameters and tumor-infiltrating CD8 + T cells may be potential biomarkers in predicting meningioma recurrence risk.

Clinical relevance statement: The findings will improve prognostic accuracy for patients with meningioma and potentially allow for targeted treatment of individuals who have the recurrent form.