Insights into Imaging最新文献

Background: Percutaneous cryoablation (PCA), having shown effectiveness in treating liver, lung, prostate, breast, and kidney tumors, is now gaining attention for the treatment of soft tissue tumors. PCA functions by freezing tissue, which induces ice crystal formation and cell death without damaging collagen structures. Technical considerations include the selection and handling of cryoprobes and cryogenic agents, procedural duration, and choice of image guidance for precision. This review aims to synthesize the mechanisms, applications, and technical aspects of PCA in the treatment of soft tissue tumors.

Methods: Adhering to PRISMA 2020 guidelines, a review was conducted of studies published prior to March 2024 that investigated PCA of soft tissue tumors. The review focused on technical and procedural aspects of cryoablation, cryobiological principles, cellular and tissue responses to extreme cold, intra- and post-procedure physiological mechanisms during and post-procedure, and main clinical applications.

Results: PCA is efficient in treating soft tissue tumors, including desmoid tumors, vascular malformations, and abdominal wall endometriosis. Several cryobiological mechanisms are involved, notably ice crystal formation, cellular dehydration, osmotic effects, and the inflammatory response, all of which contribute to its efficacy. Key technical aspects include the choice of cryoprobes, cryogenic agents (argon gas or liquid nitrogen), and the duration and control of freezing/thawing cycles. PCA also frequently outperformed traditional treatments like surgery and radiotherapy in terms of pain reduction, tumor size reduction, and patient outcomes. Moreover, its nerve sideration properties make it effective under local anesthesia.

Conclusion: Demonstrating substantial pain reduction, tumor size decrease, and high technical success rates, PCA offers a promising and minimally invasive alternative for soft tissue tumor treatment.

Critical relevance statement: Percutaneous cryoablation provides a minimally invasive, precise alternative for soft tissue tumor management, advancing clinical radiology by offering effective treatment with reduced patient risk and enhanced outcomes through image-guided procedures.

Key points: Percutaneous cryoablation (PCA) offers a promising, minimally invasive alternative for managing soft tissue tumors. PCA employs image-guided techniques to accurately target and treat tumors, ensuring high precision and control. PCA preserves structures like collagen, reduces pain, decreases tumor size, and generally enhances patient outcomes.

Over the past decades, numerous large-scale neuroimaging projects that involved the collection and release of multimodal data have been conducted globally. Distinguished initiatives such as the Human Connectome Project, UK Biobank, and Alzheimer's Disease Neuroimaging Initiative, among others, stand as remarkable international collaborations that have significantly advanced our understanding of the brain. With the advancement of big data technology, changes in healthcare models, and continuous development in biomedical research, various types of large-scale projects are being established and promoted worldwide. For project leaders, there is a need to refer to common principles in project construction and management. Users must also adhere strictly to rules and guidelines, ensuring data safety and privacy protection. Organizations must maintain data integrity, protect individual privacy, and foster stakeholders' trust. Regular updates to legislation and policies are necessary to keep pace with evolving technologies and emerging data-related challenges. CRITICAL RELEVANCE STATEMENT: By reviewing global large-scale neuroimaging projects, we have summarized the standards and norms for establishing and utilizing their data, and provided suggestions and opinions on some ethical issues, aiming to promote higher-quality neuroimaging data development. KEY POINTS: Global neuroimaging projects are increasingly advancing but still face challenges. Constructing and utilizing neuroimaging projects should follow set rules and guidelines. Effective data management and governance should be developed to support neuroimaging projects.

Objectives: To investigate the value of vascular morphology on high frame rate contrast-enhanced ultrasound (H-CEUS) and CEUS Li-RADS in predicting microvascular invasion (MVI), Ki-67 expression and recurrence of hepatocellular carcinoma (HCC).

Methods: This retrospective study enrolled 78 patients with single HCC diagnosed by postoperative pathology between January 1, 2021, and June 30, 2022. All patients underwent ultrasound and H-CEUS examination before operation. H-CEUS image features and CEUS Li-RADS were compared in different MVI status and Ki-67 level. Multiple logistic regression analysis was performed to select independent variables for MVI. Differences in recurrence among different H-CEUS image features, MVI status and Ki-67 level were further analyzed.

Results: Tumor shape, vascular morphology, LR-M category, necrosis and AFP level were different between the MVI-positive group and MVI-negative group (p < 0.05). Vascular morphology and LR-M category were independent risk factors related to MVI (p < 0.05). Vascular morphology was also different between the high Ki-67 expression group and low Ki-67 expression group (p < 0.05). Vascular morphology, MVI status and Ki-67 expression were different between the recurrence group and no recurrence group (p < 0.05).

Conclusion: The vascular morphology of HCC on H-CEUS can indicate the risk of MVI status, Ki-67 expression and recurrence, which provides a feasible imaging technique for predicting the prognosis before operation.

Critical relevance statement: H-CEUS shows the different vascular morphology of HCC in arterial phase and indicates the risk of MVI, Ki-67 expression and recurrence, which provides a feasible imaging technique for clinician to judge the risk of MVI pre-operation and adopt appropriate treatment.

Key points: H-CEUS can clearly show different vascular morphology of HCC in arterial phase. Vascular morphology on H-CEUS is associated with MVI status, Ki-67 expression and HCC recurrence. Preoperative MVI and Ki-67 expression prediction could help surgeons choose a more appropriate treatment plan.

Purpose: To assess the diagnostic performance of prostate MRI by estimating the proportion of clinically significant prostate cancer (csPCa) in patients without prostate pathology.

Materials and methods: This three-center retrospective study included prostate MRI examinations performed for clinical suspicion of csPCa (Grade group ≥ 2) between 2018 and 2022. Examinations were divided into two groups: pathological diagnosis within 1 year after the MRI (post-MRI pathology) is present and absent. Risk prediction models were developed using the extracted eleven common predictive variables from the patients with post-MRI pathology. Then, the csPCa proportion in the patients without post-MRI pathology was estimated by applying the model. The area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and positive and negative predictive values (PPV/NPV) of prostate MRI in diagnosing csPCa were subsequently calculated for patients with and without post-MRI prostate pathology (estimated statistics) with a positive threshold of PI-RADS ≥ 3.

Results: Of 12,191 examinations enrolled (mean age, 65.7 years ± 8.4 [standard deviation]), PI-RADS 1-2 was most frequently assigned (55.4%) with the lowest pathological confirmation rate of 14.0-18.2%. Post-MRI prostate pathology was found in 5670 (46.5%) examinations. The estimated csPCa proportions across facilities were 12.6-15.3%, 18.4-31.4%, 45.7-69.9%, and 75.4-88.3% in PI-RADS scores of 1-2, 3, 4, and 5, respectively. The estimated (observed) performance statistics were as follows: AUC, 0.78-0.81 (0.76-0.79); sensitivity, 76.6-77.3%; specificity, 67.5-78.6%; PPV, 49.8-66.6% (52.0-67.7%); and NPV, 84.4-87.2% (82.4-86.6%).

Conclusion: We proposed a method to estimate the probabilities harboring csPCa for patients who underwent prostate MRI examinations, which allows us to understand the PI-RADS diagnostic performance with several metrics.

Clinical relevance statement: The reported estimated performance metrics are expected to aid in understanding the true diagnostic value of PI-RADS in the entire prostate MRI population performed with clinical suspicion of prostate cancer.

Key points: Calculating performance metrics only from patients who underwent prostate biopsy may be biased due to biopsy selection criteria, especially in PI-RADS 1-2. The estimated area under the receiver operating characteristic curve of PI-RADS in the entire prostate MRI population ranged from 0.78 to 0.81 at three facilities. The estimated statistics are expected to help us understand the true PI-RADS performance and serve as a reference for future studies.

Objectives: This study developed a deep learning radiomics (DLR) model utilizing baseline computed tomography enterography (CTE) to non-invasively predict stratified healing in Crohn's disease (CD) patients following infliximab (IFX) treatment.

Methods: The study included 246 CD patients diagnosed at three hospitals. From the first two hospitals, 202 patients were randomly divided into a training cohort (n = 141) and a testing cohort (n = 61) in a 7:3 ratio. The remaining 44 patients from the third hospital served as the validation cohort. Radiomics and deep learning features were extracted from both the active lesion wall and mesenteric adipose tissue. The most valuable features were selected using univariate analysis and least absolute shrinkage and selection operator (LASSO) regression. Multivariate logistic regression was then employed to construct the radiomics, deep learning, and DLR models. Model performance was evaluated using receiver operating characteristic (ROC) curves.

Results: The DLR model achieved an area under the ROC curve (AUC) of 0.948 (95% CI: 0.916-0.980), 0.889 (95% CI: 0.803-0.975), and 0.938 (95% CI: 0.868-1.000) in the training, testing, and validation cohorts, respectively in predicting mucosal healing (MH). Furthermore, the diagnostic performance of DLR model in predicting transmural healing (TH) was 0.856 (95% CI: 0.776-0.935).

Conclusions: We have developed a DLR model based on the radiomics and deep learning features of baseline CTE to predict stratified healing (MH and TH) in CD patients following IFX treatment with high accuracies in both testing and external cohorts.

Critical relevance statement: The deep learning radiomics model developed in our study, along with the nomogram, can intuitively, accurately, and non-invasively predict stratified healing at baseline CT enterography.

Key points: Early prediction of mucosal and transmural healing in Crohn's Disease patients is beneficial for treatment planning. This model demonstrated excellent performance in predicting mucosal healing and had a diagnostic performance in predicting transmural healing of 0.856. CT enterography images of active lesion walls and mesenteric adipose tissue exhibit an association with stratified healing in Crohn's disease patients.

Objectives: Chest computed tomography (CT) can diagnose and assess the severity of pulmonary contusions. However, in cases of severe lung contusion, the total lung volume ratio may not accurately predict severity. This study investigated the association between life-threatening hemoptysis and chest CT imaging data on arrival at the emergency department in patients with pulmonary contusions or lacerations due to blunt chest injury.

Methods: The records of 277 patients with lung contusions or lacerations treated at a trauma center between 2018 and 2022 were retrospectively reviewed. The ratio of the local lung contusion volume to lobe volume in each lobe was calculated from chest CT images. The maximal ratio in the Hounsfield unit (HU) range was defined as the highest ratio value within the HU range among five lobes.

Results: The median patient age was 41 years, and 68.6% were male. Life-threatening hemoptysis occurred in 39 patients. The area under the receiver operating characteristic curve for the maximal ratio at -500 HU to 100 HU was 96.52%. The cutoff value was 45.49%. Multivariate analysis showed a high maximal chest CT ratio ≥ 45.49% at -500 HU to 100 HU (adjusted odds ratio [aOR]: 104.66, 95% confidence interval [CI]: 21.81-502.16, p < 0.001), hemopneumothorax (aOR: 5.18, 95% CI: 1.25-21.47, p = 0.023), and chest abbreviated injury scale (AIS, aOR: 5.58, 95% CI: 1.68-18.57, p = 0.005) were associated with life-threatening hemoptysis.

Conclusions: Maximal chest CT ratios ≥ 45.49% at -500 HU to 100 HU, hemopneumothorax, and high chest AIS scores are associated with life-threatening hemoptysis in patients with blunt chest trauma.

Critical relevance statement: The present study provides an objective index derived from chest CT images to predict the occurrence of life-threatening hemoptysis. This information helps screen high-risk patients in need of more intensive monitoring for early intervention to improve outcomes.

Key points: Emergency department CT helps predict life-threatening hemoptysis in patients with lung contusions. Maximal CT ratios ≥ 45.49% (-500 HU to 100 HU, either lung lobe) are associated with life-threatening hemoptysis. High chest abbreviated injury scale scores and hemopneumothorax also predict life-threatening hemoptysis.

Objectives: This study aimed to develop and validate a deep-learning radiomics model using CT, T2, and DWI images for predicting pathological complete response (pCR) in patients with esophageal squamous cell carcinoma (ESCC) undergoing neoadjuvant chemoradiotherapy (nCRT).

Materials and methods: Patients with ESCC undergoing nCRT followed by surgery were retrospectively enrolled from three institutions and divided into training and testing cohorts. Both traditional and deep-learning radiomics features were extracted from pre-treatment CT, T2, and DWI. Multiple radiomics models were developed, both single modality and integrated, using machine learning algorithms. The models' performance was assessed using receiver operating characteristic curve analysis, with the area under the curve (AUC) as a primary metric, alongside sensitivity and specificity from the cut-off analysis.

Results: The study involved 151 patients, among whom 63 achieved pCR. The training cohort consisted of 89 patients from Institution 1 (median age 62, 73 males) and the testing cohort included 52 patients from Institution 2 (median age 62, 41 males), and 10 in a clinical trial from Institution 3 (median age 69, 9 males). The integrated model, combining traditional and deep learning radiomics features from CT, T2, and DWI, demonstrated the best performance with an AUC of 0.868 (95% CI: 0.766-0.959), sensitivity of 88% (95% CI: 73.9-100), and specificity of 78.4% (95% CI: 63.6-90.2) in the testing cohort. This model outperformed single-modality models and the clinical model.

Conclusion: A multimodality deep learning radiomics model, utilizing CT, T2, and DWI images, was developed and validated for accurately predicting pCR of ESCC following nCRT.

Critical relevance statement: Our research demonstrates the satisfactory predictive value of multimodality deep learning radiomics for the response of nCRT in ESCC and provides a potentially helpful tool for personalized treatment including organ preservation strategy.

Key points: After neoadjuvant chemoradiotherapy, patients with ESCC have pCR rates of about 40%. The multimodality deep learning radiomics model, could predict pCR after nCRT with high accuracy. The multimodality radiomics can be helpful in personalized treatment of esophageal cancer.

Objectives: To investigate whether the signal intensity (SI) ratio of residual fibroid (RF) to myometrium using Contrast-Enhanced Magnetic Resonance Imaging (CE-MRI) could predict fibroid regrowth after high-intensity focused ultrasound (HIFU) treatment.

Materials and methods: A retrospective analysis was conducted among 164 patients with uterine fibroids who underwent HIFU. To predict the RF regrowth, the SI perfusion parameters were quantified using the RF-myometrium SI ratio on CE-MRI on day 1 post-HIFU and then compared with the fibroid-myometrium SI ratio on the T2-weighted image (T2WI) and Funaki classification 1 year later. Thirty cases from another center were used as an external validation set to evaluate the performance of RF-myometrium SI ratio.

Results: The predictive performance of the RF-myometrium SI ratio on CE-MRI on day 1 post-HIFU (Area Under Curve, AUC: 0.869) was superior to that of the preoperative and postoperative fibroid-myometrium SI ratios on the T2WI (AUC: 0.724, 0.696) and Funaki classification (AUC: 0.663, 0.623). Multivariate analysis showed that the RF- myometrium SI ratio and RF thickness were independent factors. The RF-myometrium SI ratio reflects the long-term rate of re-intervention (r = 0.455, p < 0.001).

Conclusion: The RF-myometrium SI ratio on CE-MRI exhibits greater accuracy in predicting RF regrowth compared to the SI classification and the SI ratio on T2WI.

Critical relevance statement: The ratio of residual uterine fibroid to myometrial signal intensity on contrast-enhanced (CE)-MRI can reflect residual blood supply, predict regrowth of fibroids, and thus reflect long-term re-intervention rate and recovery situation of clinical high-intensity focused ultrasound (HIFU) treatment.

Key points: Contrast-enhanced (CE)-MRI can indicate the blood supply of residual uterine fibroids after high-intensity focused ultrasound (HIFU) treatment. The predictive capability of CE-MRI ratio surpasses T2WI ratio and the Funaki. Residual fibroids can serve as a measure of the long-term efficacy of HIFU.

Developments in the multidisciplinary treatment of rectal cancer with advances in preoperative magnetic resonance imaging (MRI), surgical techniques, neoadjuvant chemoradiotherapy, and adjuvant chemotherapy have had a significant impact on patient outcomes, increasing the rates of curative surgeries and reducing pelvic recurrence. Patients with pelvic recurrence have worse prognoses, with an impact on morbidity and mortality. Although local recurrence is more frequent within 2 years of surgical resection of the primary tumor, late recurrence may occur. Clinical manifestations can vary from asymptomatic, nonspecific symptoms, to pelvic pain, bleeding, and fistulas. Synchronous metastatic disease occurs in approximately 50% of patients diagnosed with local recurrence. MRI plays a crucial role in posttreatment follow-up, whether by identifying viable neoplastic tissues or acting as a tool for therapeutic planning and assessing the resectability of these lesions. Locally recurrent tissues usually have a higher signal intensity than muscle on T2-weighted imaging. Thus, attention is required for focal heterogeneous lesions, marked contrast enhancement, early invasive behavior, and asymmetric appearance, which are suspicious for local recurrence. However, postsurgical inflammatory changes related to radiotherapy and fibrosis make it difficult to detect initial lesions. This study therefore aimed to review the main imaging patterns of pelvic recurrence and their implications for the surgical decision-making process. CRITICAL RELEVANCE STATEMENT: MRI plays a crucial role in the posttreatment follow-up of rectal cancer, whether by identifying viable neoplastic tissues or by acting as a tool for therapeutic planning. This study reviewed the main imaging patterns of pelvic recurrence. KEY POINTS: MRI aids in surgical planning and the detection of pelvic recurrence and postoperative complications. Being familiar with surgical techniques enables radiologists to identify expected MRI findings. Patterns of rectal cancer recurrence have been categorized by pelvic compartments. Neoplastic tissue may mimic postsurgical and postradiotherapy changes. Resectability of pelvic recurrence is highly related to lesion location.

Background: To evaluate the utility of glucose chemical exchange saturation transfer (glucoCEST) MRI with non-contrast injection in predicting the histological grade of rectal cancer.

Methods: This prospective analysis included 60 patients with preoperative rectal cancer who underwent pelvic glucoCEST, amide proton transfer-weighted imaging (APTWI), and diffusion-weighted imaging (DWI). In total, 21 low-grade and 39 high-grade cases were confirmed by postoperative pathology. The MTRasym (1.2 ppm), MTRasym (3.5 ppm), and apparent diffusion coefficient (ADC) values of lesions between the low-grade and high-grade groups were compared. The area under the receiver operating characteristic curve (AUC) was generated to evaluate the diagnostic performance of each technique. Logistic regression (LR) analysis was applied to determine independent predictors and for multi-parameter combined diagnosis.

Results: Elevated MTRasym (1.2 ppm), MTRasym (3.5 ppm) values and lower ADC values were observed in the high-grade group compared with low-grade cases (all p < 0.01). The AUCs of MTRasym (1.2 ppm), MTRasym (3.5 ppm), and ADC for differentiating between low- and high-grade rectal cancer cases were 0.792, 0.839, and 0.855, respectively. The diagnostic performance of the combination of the three indexes was improved (AUC, 0.969; sensitivity, 95.24%; specificity, 87.18%). The good consistency and reliability of the combination of independent predictors were demonstrated by calibration curve analysis and DCA.

Conclusion: The glucoCEST MRI without contrast injection, APTWI, and DWI all facilitate the assessment of histological grade in rectal cancer, and the combination of the three can effectively discriminate between high- and low-grade rectal cancer, which is expected to be a promising imaging marker.

Critical relevance statement: The glucose chemical exchange saturation transfer MRI method facilitates the assessment of histological grade in rectal cancer and offers additional information to improve the diagnostic performance of amide proton transfer-weighted imaging, and diffusion-weighted imaging.

Key points: Glucose chemical exchange saturation transfer imaging could differentiate histological grade. Amide proton transfer-weighted and diffusion-weighted were associated with histological grade. The combination of different parameters showed the best diagnostic performance.