Cancer Imaging最新文献

Follicular cell-derived thyroid carcinomas constitute the majority of thyroid malignancies. This heterogeneous group of tumours includes well differentiated, poorly differentiated, and undifferentiated forms, which have distinct pathological features, clinical behaviour, and prognosis. Positron emission tomography with 2-[¹⁸F]fluoro-2-deoxy-D-glucose combined with computed tomography ([¹⁸F]FDG PET/CT) is an imaging modality used in routine clinical practice for oncological patients. [¹⁸F]FDG PET/CT has emerged as a valuable tool for identifying patients at high risk of poor clinical outcomes and for facilitating individualized clinical decision-making. The aim of this comprehensive review is to summarize current knowledge regarding the role of [¹⁸F]FDG PET/CT in primary diagnosis, treatment, and follow-up of follicular cell-derived thyroid carcinomas considering the degree of differentiation. Controversial issues, including significance of accidentally detected [¹⁸F]FDG uptake in the thyroid, the role of [¹⁸F]FDG PET/CT in the early assessment of response to molecular targeted therapies, and its prognostic value are discussed in detail.

Background: Directly-injected therapies (DIT) include a broad range of agents within a developing research field in cancer immunotherapy, with encouraging clinical trial results in various tumour subtypes. Currently, the majority of such therapies are only available within clinical trials; however, more recently, talimogene laherparepvec (T-VEC, Imlygic) has been approved as the first oncolytic virus therapy in the USA and Europe. Our institution contributes to multiple different trials exploring the efficacy of DIT, the majority of which are performed by oncologists in clinic. However, specific, challenging cases - mainly neck tumours - require image-guided administration.

Main body: This review article addresses the technical and logistical factors relevant to the incorporation of image-guided DIT into an established ultrasound service. Image-guidance (usually with ultrasound) is frequently needed for certain targets that cannot be palpated or are in high-risk locations, e.g. adjacent to blood vessels. A multi-disciplinary approach is essential to facilitate a safe and efficient service, including careful case-selection. Certain protocols and guidance need to be followed when incorporating such a service into an established ultrasound practice to enhance efficiency and optimise safety. Key learning points are drawn from the literature and from our early experience at a tertiary cancer centre following image guided DIT for an initial cohort of 22 patients (including 11 with a neck mass), addressing trial protocols, pre-procedure work-up, organisation, planning, consent, technical aspects, procedure tolerability, technical success, and post-procedure considerations.

Conclusion: With appropriate planning and coordination, and application of the learning points discussed herein, image-guided administration of DIT can be safely and efficiently incorporated into an established procedural ultrasound list. This has relevance to cancer centres, radiology departments, individual radiologists, and other team members with a future role in meeting the emerging need for these procedures. This paper provides advice on developing such an imaging service, and offers certain insights into the evolving remit of radiologists within cancer care in the near future.

Background: To explore the value of dual-energy computed tomography (DECT) in differentiating pathological subtypes and the expression of immunohistochemical markers Ki-67 and thyroid transcription factor 1 (TTF-1) in patients with non-small cell lung cancer (NSCLC).

Methods: Between July 2022 and May 2024, patients suspected of lung cancer who underwent two-phase contrast-enhanced DECT were prospectively recruited. Whole-tumor volumetric and conventional spectral analysis were utilized to measure DECT parameters in the arterial and venous phase. The DECT parameters model, clinical-CT radiological features model, and combined prediction model were developed to discriminate pathological subtypes and predict Ki-67 or TTF-1 expression. Multivariate logistic regression analysis was used to identify independent predictors. The diagnostic efficacy was assessed by the area under the receiver operating characteristic curve (AUC) and compared using DeLong's test.

Results: This study included 119 patients (92 males and 27 females; mean age, 63.0 ± 9.4 years) who was diagnosed with NSCLC. When applying the DECT parameters model to differentiate between adenocarcinoma and squamous cell carcinoma, ROC curve analysis indicated superior diagnostic performance for conventional spectral analysis over volumetric spectral analysis (AUC, 0.801 vs. 0.709). Volumetric spectral analysis exhibited higher diagnostic efficacy in predicting immunohistochemical markers compared to conventional spectral analysis (both P < 0.05). For Ki-67 and TTF-1 expression, the combined prediction model demonstrated optimal diagnostic performance with AUC of 0.943 and 0.967, respectively.

Conclusions: The combined predictive model based on volumetric quantitative analysis in DECT offers valuable information to discriminate immunohistochemical expression status, facilitating clinical decision-making for patients with NSCLC.

Objective: This study aims to develop and validate a predictive model that integrates clinical features, MRI radiomics, and nutritional-inflammatory biomarkers to forecast progression-free survival (PFS) in cervical cancer (CC) patients undergoing concurrent chemoradiotherapy (CCRT). The goal is to identify high-risk patients and guide personalized treatment.

Methods: We performed a retrospective analysis of 188 patients from two centers, divided into training (132) and validation (56) sets. Clinical data, systemic inflammatory markers, and immune-nutritional indices were collected. Radiomic features from three MRI sequences were extracted and selected for predictive value. We developed and evaluated five models incorporating clinical features, nutritional-inflammatory indicators, and radiomics using C-index. The best-performing model was used to create a nomogram, which was validated through ROC curves, calibration plots, and decision curve analysis (DCA).

Results: Model 5, which integrates clinical features, Systemic Immune-Inflammation Index (SII), Prognostic Nutritional Index (PNI), and MRI radiomics, showed the highest performance. It achieved a C-index of 0.833 (95% CI: 0.792-0.874) in the training set and 0.789 (95% CI: 0.679-0.899) in the validation set. The nomogram derived from Model 5 effectively stratified patients into risk groups, with AUCs of 0.833, 0.941, and 0.973 for 1-year, 3-year, and 5-year PFS in the training set, and 0.812, 0.940, and 0.944 in the validation set.

Conclusions: The integrated model combining clinical features, nutritional-inflammatory biomarkers, and radiomics offers a robust tool for predicting PFS in CC patients undergoing CCRT. The nomogram provides precise predictions, supporting its application in personalized patient management.

Tumor vascular physiology is an important determinant of disease progression as well as the therapeutic outcome of cancer treatment. Angiogenesis or the lack of it provides crucial information about the tumor's blood supply and therefore can be used as an index for cancer growth and progression. While standalone anti-angiogenic therapy demonstrated limited therapeutic benefits, its combination with chemotherapeutic agents improved the overall survival of cancer patients. This could be attributed to the effect of vascular normalization, a dynamic process that temporarily reverts abnormal vasculature to the normal phenotype maximizing the delivery and intratumor distribution of chemotherapeutic agents. Longitudinal monitoring of vascular changes following antiangiogenic therapy can indicate an optimal window for drug administration and estimate the potential outcome of treatment. This review primarily focuses on the status of various imaging modalities used for the longitudinal characterization of vascular changes before and after anti-angiogenic therapies and their clinical prospects.

Purpose: To conduct a head-to-head comparison between deep learning (DL) and radiomics models across institutions for predicting microvascular invasion (MVI) in hepatocellular carcinoma (HCC) and to investigate the model robustness and generalizability through rigorous internal and external validation.

Methods: This retrospective study included 2304 preoperative images of 576 HCC lesions from two centers, with MVI status determined by postoperative histopathology. We developed DL and radiomics models for predicting the presence of MVI using B-mode ultrasound, contrast-enhanced ultrasound (CEUS) at the arterial, portal, and delayed phases, and a combined modality (B + CEUS). For radiomics, we constructed models with enlarged vs. original regions of interest (ROIs). A cross-validation approach was performed by training models on one center's dataset and validating the other, and vice versa. This allowed assessment of the validity of different ultrasound modalities and the cross-center robustness of the models. The optimal model combined with alpha-fetoprotein (AFP) was also validated. The head-to-head comparison was based on the area under the receiver operating characteristic curve (AUC).

Results: Thirteen DL models and 25 radiomics models using different ultrasound modalities were constructed and compared. B + CEUS was the optimal modality for both DL and radiomics models. The DL model achieved AUCs of 0.802-0.818 internally and 0.667-0.688 externally across the two centers, whereas radiomics achieved AUCs of 0.749-0.869 internally and 0.646-0.697 externally. The radiomics models showed overall improvement with enlarged ROIs (P < 0.05 for both CEUS and B + CEUS modalities). The DL models showed good cross-institutional robustness (P > 0.05 for all modalities, 1.6-2.1% differences in AUC for the optimal modality), whereas the radiomics models had relatively limited robustness across the two centers (12% drop-off in AUC for the optimal modality). Adding AFP improved the DL models (P < 0.05 externally) and well maintained the robustness, but did not benefit the radiomics model (P > 0.05).

Conclusion: Cross-institutional validation indicated that DL demonstrated better robustness than radiomics for preoperative MVI prediction in patients with HCC, representing a promising solution to non-standardized ultrasound examination procedures.

Purpose: To explore the application value of a multimodal deep learning radiomics (MDLR) model in predicting the risk status of postoperative progression in solid stage I non-small cell lung cancer (NSCLC).

Materials and methods: A total of 459 patients with histologically confirmed solid stage I NSCLC who underwent surgical resection in our institution from January 2014 to September 2019 were reviewed retrospectively. At another medical center, 104 patients were reviewed as an external validation cohort according to the same criteria. A univariate analysis was conducted on the clinicopathological characteristics and subjective CT findings of the progression and non-progression groups. The clinicopathological characteristics and subjective CT findings that exhibited significant differences were used as input variables for the extreme learning machine (ELM) classifier to construct the clinical model. We used the transfer learning strategy to train the ResNet18 model, used the model to extract deep learning features from all CT images, and then used the ELM classifier to classify the deep learning features to obtain the deep learning signature (DLS). A MDLR model incorporating clinicopathological characteristics, subjective CT findings and DLS was constructed. The diagnostic efficiencies of the clinical model, DLS model and MDLR model were evaluated by the area under the curve (AUC).

Results: Univariate analysis indicated that size (p = 0.004), neuron-specific enolase (NSE) (p = 0.03), carbohydrate antigen 19 - 9 (CA199) (p = 0.003), and pathological stage (p = 0.027) were significantly associated with the progression of solid stage I NSCLC after surgery. Therefore, these clinical characteristics were incorporated into the clinical model to predict the risk of progression in postoperative solid-stage NSCLC patients. A total of 294 deep learning features with nonzero coefficients were selected. The DLS in the progressive group was (0.721 ± 0.371), which was higher than that in the nonprogressive group (0.113 ± 0.350) (p < 0.001). The combination of size、NSE、CA199、pathological stage and DLS demonstrated the superior performance in differentiating postoperative progression status. The AUC of the MDLR model was 0.885 (95% confidence interval [CI]: 0.842-0.927), higher than that of the clinical model (0.675 (95% CI: 0.599-0.752)) and DLS model (0.882 (95% CI: 0.835-0.929)). The DeLong test and decision in curve analysis revealed that the MDLR model was the most predictive and clinically useful model.

Conclusion: MDLR model is effective in predicting the risk of postoperative progression of solid stage I NSCLC, and it is helpful for the treatment and follow-up of solid stage I NSCLC patients.

Background: To compare the performance between one-slice two-dimensional (2D) and whole-volume three-dimensional (3D) computed tomography (CT)-based radiomics models in the prediction of lymphovascular invasion (LVI) status in esophageal squamous cell carcinoma (ESCC).

Methods: Two hundred twenty-four patients with ESCC (158 LVI-absent and 66 LVI-present) were enrolled in this retrospective study. The enrolled patients were randomly split into the training and testing sets with a 7:3 ratio. The 2D and 3D radiomics features were derived from the primary tumors' 2D and 3D regions of interest (ROIs) using 1.0 mm thickness contrast-enhanced CT (CECT) images. The 2D and 3D radiomics features were screened using inter-/intra-class correlation coefficient (ICC) analysis, Wilcoxon rank-sum test, Spearman correlation test, and the least absolute shrinkage and selection operator, and the radiomics models were built by multivariate logistic stepwise regression. The performance of 2D and 3D radiomics models was assessed by the area under the receiver operating characteristic (ROC) curve. The actual clinical utility of the 2D and 3D radiomics models was evaluated by decision curve analysis (DCA).

Results: There were 753 radiomics features from 2D ROIs and 1130 radiomics features from 3D ROIs, and finally, 7 features were retained to construct 2D and 3D radiomics models, respectively. ROC analysis revealed that in both the training and testing sets, the 3D radiomics model exhibited higher AUC values than the 2D radiomics model (0.930 versus 0.852 and 0.897 versus 0.851, respectively). The 3D radiomics model showed higher accuracy than the 2D radiomics model in the training and testing sets (0.899 versus 0.728 and 0.788 versus 0.758, respectively). In addition, the 3D radiomics model has higher specificity and positive predictive value, while the 2D radiomics model has higher sensitivity and negative predictive value. The DCA indicated that the 3D radiomics model provided higher actual clinical utility regarding overall net benefit than the 2D radiomics model.

Conclusions: Both 2D and 3D radiomics features can be employed as potential biomarkers to predict the LVI in ESCC. The performance of the 3D radiomics model is better than that of the 2D radiomics model for the prediction of the LVI in ESCC.

Objective: To analyze the characteristics of high-frame-rate contrast-enhanced ultrasound (H-CEUS) in solid renal tumors using qualitative and quantitative methods.

Methods: Seventy-five patients who underwent preoperative conventional ultrasound (US), conventional contrast-enhanced ultrasound (C-CEUS), and H-CEUS examination of renal tumors were retrospectively analyzed, with a total of 89 renal masses. The masses were divided into the benign (30 masses) and malignant groups (59 masses) based on the results of enhanced computer tomography and pathology. The location, diameter, shape, border, calcification, and color doppler blood flow imaging (CDFI) of the lesions were observed by US, and the characteristics of the C-CEUS and H-CEUS images were qualitatively and quantitatively analyzed. The χ² test or Fisher's exact probability method was used to compare the US image characteristics between the benign and malignant groups, and the image characteristics of C-CEUS and H-CEUS between the benign and malignant groups. Moreover, the nonparametric Mann-Whitney test was used to compare the differences in C-CEUS and H-CEUS time-intensity curve (TIC) parameters.

Results: Significant differences in gender, surgical approach, echogenicity, and CDFI were observed between the malignant and benign groups (p = 0.003, < 0.001, < 0.001, = 0003). Qualitative analysis also revealed significant differences in the mode of wash-out and fill-in direction between C-CEUS and H-CEUS in the malignant group (p = 0.041, 0.002). In addition, the homogeneity of enhancement showed significant differences between the two contrast models in the benign group (p = 0.009). Quantitative analysis indicated that the TIC parameters peak intensity (PI), deceleration time (DT) /2, area under the curve (AUC), and mean transition time (MTT) were significantly lower in the H-CEUS model compared to the C-CEUS model in both the benign and malignant groups. (all p < 0.001). In contrast, ascending slope of rise curve (AS) was significantly higher in the H-CEUS model compared to the C-CEUS model in the malignant group (p = 0.048).

Conclusions: In renal tumors, H-CEUS shows clearer internal enhancement of the mass and the changes in the wash-out period. The quantitative TIC parameters PI, DT/2, AUC, and MTT were lower in H-CEUS compared to C-CEUS. Both the quantitative and qualitative analyses indicated that H-CEUS better displays the characteristics of solid renal masses compared with C-CEUS.