Image quality and detection efficacy of zero echo time magnetic resonance imaging on Lung-RADS 2 pulmonary ground-glass nodules in comparison to thin-slice fat-saturated T2-weighted imaging.

Background: Widely used computed tomography (CT) screening increases the detection of pulmonary pure ground-glass nodules (pGGNs), often classified as the second category of Lung Imaging Reporting and Data System (Lung-RADS 2). Despite their low malignancy risk, these nodules pose significant challenges and necessitate accurate assessment to minimize the risk of long-term follow-ups. This study investigated the detection efficacy of zero echo time (ZTE) magnetic resonance imaging (MRI) and thin-slice fat-saturated T2-weighted imaging (T2WI-FS) on 3.0 T MRI on the predictive accuracy of invasiveness for Lung-RADS 2 pGGNs. Methods: This prospective study enrolled 83 consecutive patients with 110 pGGNs who underwent preoperative CT and MRI scans. All CT images were assessed by artificial intelligence (AI) software and confirmed by a thoracic radiologist. Another two radiologists blind to pathology results assessed MRI for image quality (objective and subjective evaluations) and detection of pGGNs. Differences in nodule diameter, CT density and detection rate were compared within different pathological groups. The objective and subjective image quality scores were compared using the Wilcoxon signed rank test between ZTE and T2WI-FS. Interobserver agreement was calculated using the kappa coefficient. Receiver operating characteristic (ROC) curve analysis evaluated the diagnostic accuracy for distinguishing invasiveness. Results: Among the 110 pGGNs evaluated, T2WI-FS demonstrated a higher detection rate (80.0%) compared to ZTE (51.8%). ZTE showed a superior signal-to-noise ratio (SNR) in the lung parenchyma, aorta, and peripheral lung structures, whereas T2WI-FS more effectively delineated tracheal walls and pulmonary nodules. Both observers rated ZTE higher for vascular and bronchial visibility, while T2WI-FS was better in terms of lower noise and fewer artifacts. Notably, ZTE visibility varied with pathological results, exhibiting a range from 0% in atypical adenomatous hyperplasia (AAH) to 94.1% in invasive adenocarcinoma (IAC). The key indicators for distinguishing invasive pGGNs from non-invasive ones were nodule diameter [area under the curve (AUC) =0.874], ZTE visibility (AUC =0.740), followed by CT values (AUC =0.682) and T2WI-FS visibility (AUC =0.678). Conclusions: MRI has the potential to detect and predict the invasiveness of pGGN. Both T2WI-FS and ZTE demonstrate reliable image quality in pulmonary imaging, each displaying strengths in visualizing pGGN. Thin-slice T2WI-FS has a superior detection rate, while ZTE better predicts histological invasiveness.

Z-Spectral MRI Quantifies the Mass and Metabolic Activity of Adipose Tissues With Fat-Water-Fraction and Amide-Proton-Transfer Contrasts.

BACKGROUND: Brown adipose tissue (BAT) is metabolically activatable and plays an important role in obesity and metabolic diseases. With reduced fat-water-fraction (FWF) compared with white adipose tissue (WAT), BAT mass and its functional activation may be quantified with Z-spectra MRI, with built-in FWF and the metabolic amide proton transfer (APT) contrasts. PURPOSE: To investigate if Z-spectral MRI can quantify the mass and metabolic activity of adipose tissues. STUDY TYPE: Prospective. SUBJECTS: Seven groups of 8-week-old male rats, including two groups (n = 7 per group) for in vivo MRI study and five groups (n = 5 per group) for ex vivo validation; 12 young and healthy volunteers with 6 male and 6 female. FIELD STRENGTH/SEQUENCE: The 7 T small animal and 3 T clinical systems, T2-weighted imaging, Rapid Acquisition with Relaxation Enhancement (RARE) readout based chemical exchange saturation transfer (CEST) Z-spectral MRI sequence. ASSESSMENT: Quantified FWF and APT from Z-spectra in rats before and after norepinephrine (NE) stimulation and in healthy human subjects; ex vivo measurements of total proteins in BAT from rats. STATISTICAL TESTS: Two-tailed unpaired Student's t-tests and repeated measures ANOVA. P-value <0.05 was considered significant. RESULTS: Decreased FWF (from 39.6% ± 7.2% before NE injection to 16.4% ± 7.2% 120 minutes after NE injection, P < 0.0001) and elevated APT (from 1.1% ± 0.5% before NE injection to 2.9% ± 0.5% 120 minutes after NE injection, P < 0.0001) signals in BAT were observed with in vivo Z-spectral MRI in rats injected with NE at 7 T MRI. At clinical 3 T, Z-spectral MRI was used to quantify the FWF (58.5% ± 7.2% in BAT and 73.7% ± 6.5% in WAT with P < 0.0001) and APT (2.6% ± 0.8% in BAT and 0.9% ± 0.3% in WAT with P < 0.0001) signals in healthy volunteers. APT signals of BAT were negatively correlated with the BMI in humans (r = 0.71). DATA CONCLUSION: Endogenous Z-spectral MRI was demonstrated to simultaneously quantify BAT mass and function based on its FWF and APT contrasts. TECHNICAL EFFICACY STAGE: 1.

Assessing Axillary Lymph Node Burden and Prognosis in cT1-T2 Stage Breast Cancer Using Machine Learning Methods: A Retrospective Dual-Institutional MRI Study.

BACKGROUND: Pathological axillary lymph node (pALN) burden is an important factor for treatment decision-making in clinical T1-T2 (cT1-T2) stage breast cancer. Preoperative assessment of the pALN burden and prognosis aids in the individualized selection of therapeutic approaches. PURPOSE: To develop and validate a machine learning (ML) model based on clinicopathological and MRI characteristics for assessing pALN burden and survival in patients with cT1-T2 stage breast cancer. STUDY TYPE: Retrospective. POPULATION: A total of 506 females (range: 24-83 years) with cT1-T2 stage breast cancer from two institutions, forming the training (N = 340), internal validation (N = 85), and external validation cohorts (N = 81), respectively. FIELD STRENGTH/SEQUENCE: This study used 1.5-T, axial fat-suppressed T2-weighted turbo spin-echo sequence and axial three-dimensional dynamic contrast-enhanced fat-suppressed T1-weighted gradient echo sequence. ASSESSMENT: Four ML methods (eXtreme Gradient Boosting [XGBoost], Support Vector Machine, k-Nearest Neighbor, Classification and Regression Tree) were employed to develop models based on clinicopathological and MRI characteristics. The performance of these models was evaluated by their discriminative ability. The best-performing model was further analyzed to establish interpretability and used to calculate the pALN score. The relationships between the pALN score and disease-free survival (DFS) were examined. STATISTICAL TESTS: Chi-squared test, Fisher's exact test, univariable logistic regression, area under the curve (AUC), Delong test, net reclassification improvement, integrated discrimination improvement, Hosmer-Lemeshow test, log-rank, Cox regression analyses, and intraclass correlation coefficient were performed. A P-value <0.05 was considered statistically significant. RESULTS: The XGB II model, developed based on the XGBoost algorithm, outperformed the other models with AUCs of 0.805, 0.803, and 0.818 in the three cohorts. The Shapley additive explanation plot indicated that the top variable in the XGB II model was the Node Reporting and Data System score. In multivariable Cox regression analysis, the pALN score was significantly associated with DFS (hazard ratio: 4.013, 95% confidence interval: 1.059-15.207). DATA CONCLUSION: The XGB II model may allow to evaluate pALN burden and could provide prognostic information in cT1-T2 stage breast cancer patients. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Development and validation of machine learning models for predicting HER2-zero and HER2-low breast cancers.

OBJECTIVES: To develop and validate machine learning models for human epidermal growth factor receptor 2 (HER2)-zero and HER2-low using MRI features pre-neoadjuvant therapy (NAT). METHODS: Five hundred and sixteen breast cancer patients post-NAT surgery were randomly divided into training (n = 362) and internal validation sets (n = 154) for model building and evaluation. MRI features (tumour diameter, enhancement type, background parenchymal enhancement, enhancement pattern, percentage of enhancement, signal enhancement ratio, breast oedema, and apparent diffusion coefficient) were reviewed. Logistic regression (LR), support vector machine (SVM), k-nearest neighbour (KNN), and extreme gradient boosting (XGBoost) models utilized MRI characteristics for HER2 status assessment in training and validation datasets. The best-performing model generated a HER2 score, which was subsequently correlated with pathological complete response (pCR) and disease-free survival (DFS). RESULTS: The XGBoost model outperformed LR, SVM, and KNN, achieving an area under the receiver operating characteristic curve (AUC) of 0.783 (95% CI, 0.733-0.833) and 0.787 (95% CI, 0.709-0.865) in the validation dataset. Its HER2 score for predicting pCR had an AUC of 0.708 in the training datasets and 0.695 in the validation dataset. Additionally, the low HER2 score was significantly associated with shorter DFS in the validation dataset (hazard ratio: 2.748, 95% CI, 1.016-7.432, P = .037). CONCLUSIONS: The XGBoost model could help distinguish HER2-zero and HER2-low breast cancers and has the potential to predict pCR and prognosis in breast cancer patients undergoing NAT. ADVANCES IN KNOWLEDGE: HER2-low-expressing breast cancer can benefit from the HER2-targeted therapy. Prediction of HER2-low expression is crucial for appropriate management. MRI features offer a solution to this clinical issue.

Non-invasive mapping of brown adipose tissue activity with magnetic resonance imaging.

Thermogenic brown adipose tissue (BAT) has a positive impact on whole-body metabolism. However, in vivo mapping of BAT activity typically relies on techniques involving ionizing radiation, such as [18F]fluorodeoxyglucose ([18F]FDG) positron emission tomography (PET) and computed tomography (CT). Here we report a noninvasive metabolic magnetic resonance imaging (MRI) approach based on creatine chemical exchange saturation transfer (Cr-CEST) contrast to assess in vivo BAT activity in rodents and humans. In male rats, a single dose of the ß3-adrenoceptor agonist (CL 316,243) or norepinephrine, as well as cold exposure, triggered a robust elevation of the Cr-CEST MRI signal, which was consistent with the [18F]FDG PET and CT data and 1H nuclear magnetic resonance measurements of creatine concentration in BAT. We further show that Cr-CEST MRI detects cold-stimulated BAT activation in humans (both males and females) using a 3T clinical scanner, with data-matching results from [18F]FDG PET and CT measurements. This study establishes Cr-CEST MRI as a promising noninvasive and radiation-free approach for in vivo mapping of BAT activity.

FedDUS: Lung tumor segmentation on CT images through federated semi-supervised with dynamic update strategy.

BACKGROUND AND OBJECTIVE: Lung tumor annotation is a key upstream task for further diagnosis and prognosis. Although deep learning techniques have promoted automation of lung tumor segmentation, there remain challenges impeding its application in clinical practice, such as a lack of prior annotation for model training and data-sharing among centers. METHODS: In this paper, we use data from six centers to design a novel federated semi-supervised learning (FSSL) framework with dynamic model aggregation and improve segmentation performance for lung tumors. To be specific, we propose a dynamically updated algorithm to deal with model parameter aggregation in FSSL, which takes advantage of both the quality and quantity of client data. Moreover, to increase the accessibility of data in the federated learning (FL) network, we explore the FAIR data principle while the previous federated methods never involve. RESULT: The experimental results show that the segmentation performance of our model in six centers is 0.9348, 0.8436, 0.8328, 0.7776, 0.8870 and 0.8460 respectively, which is superior to traditional deep learning methods and recent federated semi-supervised learning methods. CONCLUSION: The experimental results demonstrate that our method is superior to the existing FSSL methods. In addition, our proposed dynamic update strategy effectively utilizes the quality and quantity information of client data and shows efficiency in lung tumor segmentation. The source code is released on (https://github.com/GDPHMediaLab/FedDUS).

A novel stratification scheme combined with internal arteries in CT imaging for guiding postoperative adjuvant transarterial chemoembolization in hepatocellular carcinoma: a retrospective cohort study.

BACKGROUND: Although postoperative adjuvant transarterial chemoembolization (PA-TACE) improves survival outcomes in a subset of patients with resected hepatocellular carcinoma (HCC), the lack of reliable biomarkers for patient selection remains a significant challenge. The present study aimed to evaluate whether computed tomography imaging can provide more value for predicting benefits from PA-TACE and to establish a new scheme for guiding PA-TACE benefits. METHODS: In this retrospective study, patients with HCC who had undergone preoperative contrast-enhanced computed tomography and curative hepatectomy were evaluated. Inverse probability of treatment weight was performed to balance the difference of baseline characteristics. Cox models were used to test the interaction among PA-TACE, imaging features, and pathological indicators. An HCC imaging and pathological classification (HIPC) scheme incorporating these imaging and pathological indicators was established. RESULTS: This study included 1488 patients [median age, 52 years (IQR, 45-61 years); 1309 male]. Microvascular invasion (MVI) positive, and diameter >5 cm tumors achieved a higher recurrence-free survival (RFS), and overall survival (OS) benefit, respectively, from PA-TACE than MVI negative, and diameter ≤5 cm tumors. Patients with internal arteries (IA) positive benefited more than those with IA-negative in terms of RFS ( P =0.016) and OS ( P =0.018). PA-TACE achieved significant RFS and OS improvements in HIPC3 (IA present and diameter >5 cm, or two or three tumors) patients but not in HIPC1 (diameter ≤5 cm, MVI negative) and HIPC2 (other single tumor) patients. Our scheme may decrease the number of patients receiving PA-TACE by ~36.5% compared to the previous suggestion. CONCLUSIONS: IA can provide more value for predicting the benefit of PA-TACE treatment. The proposed HIPC scheme can be used to stratify patients with and without survival benefits from PA-TACE.

Sonographic and clinicopathologic features of metaplastic breast carcinoma and infiltrating ductal carcinoma: a comparative single-center cohort study.

Background: The rarity of metaplastic breast carcinoma (MBC) has resulted in limited sonographic data. Given the inferior prognosis of MBC compared to invasive ductal carcinoma (IDC), accurate preoperative differentiation between the two is imperative for effective treatment planning and prognostic prediction. The objective of this study was to assess the diagnostic accuracy of MBC and differentiate it from IDC by analyzing sonographic and clinicopathologic features. Methods: In this retrospective cohort study, 197 women comprising 200 IDC lesions were enrolled between January 2012 and December 2021 and 20 women comprising 20 pure MBC lesions were enrolled between January 2019 and December 2019. A comparison was made between the sonographic and clinicopathologic characteristics of MBC and IDC. Results: The results indicated that patients with MBC had a higher proportion of tumor grade 3 (95.0% vs. 32.5%; P<0.001), high Ki-67 expression (100.0% vs. 75.0%; P<0.001), and the triple-negative subtype (90.0% vs. 13.0%; P<0.001) as compared to those with IDC. On ultrasound (US) findings, MBC lesions tended to have a larger size (≥5 cm: 45.0% vs. 1.5%; P<0.001), regular shape (45.0% vs. 1.5%, P<0.001), circumscribed margin (40.0% vs. 0.5%, P<0.001), a complex cystic and solid echo pattern (50.0% vs. 3.5%; P<0.001), and posterior acoustic enhancement (95.0% vs. 14.5%; P<0.001). Additionally, MBC was more likely to be misinterpreted as a benign lesion by sonographers than was IDC (30.0% vs. 4.5%; P<0.001). Multilayer perceptron analysis revealed posterior acoustic enhancement, circumscribed margins, and size as distinguishing factors between these two tumor types. The estimated rates of local recurrence, distant metastasis, and 5-year overall survival in 19 cases with MBC were found to be 10.5%, 31.6%, and 65.0%, respectively. Conclusions: MBC typically presents as a large breast mass with more benign US features in older women, findings which may facilitate its accurate diagnosis and differentiation from other breast masses.

Development of a machine learning-based radiomics signature for estimating breast cancer TME phenotypes and predicting anti-PD-1/PD-L1 immunotherapy response.

BACKGROUNDS: Since breast cancer patients respond diversely to immunotherapy, there is an urgent need to explore novel biomarkers to precisely predict clinical responses and enhance therapeutic efficacy. The purpose of our present research was to construct and independently validate a biomarker of tumor microenvironment (TME) phenotypes via a machine learning-based radiomics way. The interrelationship between the biomarker, TME phenotypes and recipients' clinical response was also revealed. METHODS: In this retrospective multi-cohort investigation, five separate cohorts of breast cancer patients were recruited to measure breast cancer TME phenotypes via a radiomics signature, which was constructed and validated by integrating RNA-seq data with DCE-MRI images for predicting immunotherapy response. Initially, we constructed TME phenotypes using RNA-seq of 1089 breast cancer patients in the TCGA database. Then, parallel DCE-MRI images and RNA-seq of 94 breast cancer patients obtained from TCIA were applied to develop a radiomics-based TME phenotypes signature using random forest in machine learning. The repeatability of the radiomics signature was then validated in an internal validation set. Two additional independent external validation sets were analyzed to reassess this signature. The Immune phenotype cohort (n = 158) was divided based on CD8 cell infiltration into immune-inflamed and immune-desert phenotypes; these data were utilized to examine the relationship between the immune phenotypes and this signature. Finally, we utilized an Immunotherapy-treated cohort with 77 cases who received anti-PD-1/PD-L1 treatment to evaluate the predictive efficiency of this signature in terms of clinical outcomes. RESULTS: The TME phenotypes of breast cancer were separated into two heterogeneous clusters: Cluster A, an "immune-inflamed" cluster, containing substantial innate and adaptive immune cell infiltration, and Cluster B, an "immune-desert" cluster, with modest TME cell infiltration. We constructed a radiomics signature for the TME phenotypes ([AUC] = 0.855; 95% CI 0.777-0.932; p < 0.05) and verified it in an internal validation set (0.844; 0.606-1; p < 0.05). In the known immune phenotypes cohort, the signature can identify either immune-inflamed or immune-desert tumor (0.814; 0.717-0.911; p < 0.05). In the Immunotherapy-treated cohort, patients with objective response had higher baseline radiomics scores than those with stable or progressing disease (p < 0.05); moreover, the radiomics signature achieved an AUC of 0.784 (0.643-0.926; p < 0.05) for predicting immunotherapy response. CONCLUSIONS: Our imaging biomarker, a practicable radiomics signature, is beneficial for predicting the TME phenotypes and clinical response in anti-PD-1/PD-L1-treated breast cancer patients. It is particularly effective in identifying the "immune-desert" phenotype and may aid in its transformation into an "immune-inflamed" phenotype.

SwinHR: Hemodynamic-powered hierarchical vision transformer for breast tumor segmentation.

Accurate and automated segmentation of breast tumors in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a critical role in computer-aided diagnosis and treatment of breast cancer. However, this task is challenging, due to random variation in tumor sizes, shapes, appearances, and blurred boundaries of tumors caused by inherent heterogeneity of breast cancer. Moreover, the presence of ill-posed artifacts in DCE-MRI further complicate the process of tumor region annotation. To address the challenges above, we propose a scheme (named SwinHR) integrating prior DCE-MRI knowledge and temporal-spatial information of breast tumors. The prior DCE-MRI knowledge refers to hemodynamic information extracted from multiple DCE-MRI phases, which can provide pharmacokinetics information to describe metabolic changes of the tumor cells over the scanning time. The Swin Transformer with hierarchical re-parameterization large kernel architecture (H-RLK) can capture long-range dependencies within DCE-MRI while maintaining computational efficiency by a shifted window-based self-attention mechanism. The use of H-RLK can extract high-level features with a wider receptive field, which can make the model capture contextual information at different levels of abstraction. Extensive experiments are conducted in large-scale datasets to validate the effectiveness of our proposed SwinHR scheme, demonstrating its superiority over recent state-of-the-art segmentation methods. Also, a subgroup analysis split by MRI scanners, field strength, and tumor size is conducted to verify its generalization. The source code is released on (https://github.com/GDPHMediaLab/SwinHR).

Decoding tumor stage by peritumoral and intratumoral radiomics in resectable esophageal squamous cell carcinoma.

PURPOSE: To evaluate the potential application of radiomics in predicting Tumor-Node-Metastasis (TNM) stage in patients with resectable esophageal squamous cell carcinoma (ESCC). METHODS: This retrospective study included 122 consecutive patients (mean age, 57 years; 27 women). Corresponding tumor of interest was identified on axial arterial-phase CT images with manual annotation. Radiomics features were extracted from intra- and peritumoral regions. Features were pruned to train LASSO regression model with 93 patients to construct a radiomics signature, whose performance was validated in a test set of 29 patients. Prognostic value of radiomics-predicted TNM stage was estimated by survival analysis in the entire cohort. RESULTS: The radiomics signature incorporating one intratumoral and four peritumoral features was significantly associated with TNM stage. This signature discriminated tumor stage with an area under curve (AUC) of 0.823 in the training set, with similar performance in the test set (AUC 0.813). Recurrence-free survival (RFS) was significantly different between different radiomics-predicted TNM stage groups (Low-risk vs high-risk, log-rank P = 0.004). Univariate and multivariate Cox regression analyses revealed that radiomics-predicted TNM stage was an independent preoperative factor for RFS. CONCLUSIONS: The proposed radiomics signature combing intratumoral and peritumoral features was predictive of TNM stage and associated with prognostication in ESCC.

In vivo visualization of tumor-associated macrophages re-education by photoacoustic/fluorescence dual-modal imaging with a metal-organic frames-based caspase-1 nanoreporter.

Tumor-associated macrophages (TAMs) are vital in the tumor microenvironment, contributing to immunosuppression and therapy tolerance. Despite their importance, the precise re-education of TAMs in vivo continues to present a formidable challenge. Moreover, the lack of real-time and efficient methods to comprehend the spatiotemporal kinetics of TAMs repolarization remains a significant hurdle, severely hampering the accurate assessment of treatment efficacy and prognosis. Herein, we designed a metal-organic frameworks (MOFs) based Caspase-1 nanoreporter (MCNR) that can deliver a TLR7/8 agonist to the TAMs and track time-sensitive Caspase-1 activity as a direct method to monitor the initiation of immune reprogramming. This nanosystem exhibits excellent TAMs targeting ability, enhanced tumor accumulation, and stimuli-responsive behavior. By inducing the reprogramming of TAMs, they were able to enhance T-cell infiltration in tumor tissue, resulting in inhibited tumor growth and improved survival in mice model. Moreover, MCNR also serves as an activatable photoacoustic and fluorescent dual-mode imaging agent through Caspase-1-mediated specific enzyme digestion. This feature enables non-invasive and real-time antitumor immune activation monitoring. Overall, our findings indicate that MCNR has the potential to be a valuable tool for tumor immune microenvironment remodeling and noninvasive quantitative detection and real-time monitoring of TAMs repolarization to immunotherapy in the early stage.

Multimodal Imaging Features and Prognosis of Metaplastic Breast Carcinoma.

RATIONALE AND OBJECTIVES: Metaplastic breast carcinoma (MBC) is an infrequent malignancy with an unfavorable prognosis, and there is a paucity of research on the multimodal imaging features of MBC. This study aimed to provide a comprehensive analysis of the multimodal imaging features, clinicopathological characteristics, and prognosis of MBC. MATERIALS AND METHODS: A total of 36 patients with histologically confirmed MBC from 2012 to 2021 were included in the study. We analyzed the pre-treatment multimodal imaging features, including mammography, ultrasonography (US), and magnetic resonance imaging (MRI), as well as clinicopathology and prognosis of MBC. Follow-up data included local recurrence, distant metastasis, and overall survival (OS) rate. RESULTS: MBC patients had a median age of 51 years at diagnosis. The most common histologic subtype was squamous cell carcinoma, with 86.1% of MBC being histological grade 3 and triple negative. The most common mammographic findings were irregular shape, non-calcification, and high density. The predominant US findings included irregular shape, parallel orientation, posterior acoustic enhancement, and hypoecho. On MRI, most masses exhibited irregular shape, spiculate margin, heterogeneous enhancement, Type II time intensity curve, and diffusion restriction on diffusion weighted images determined by apparent diffusion coefficient. According to breast imaging reporting and data system, mammography suggested malignancy in 50% of cases, US indicated a moderate to high suspicion of malignancy in 77.8% of cases, MRI revealed malignancy in all cases. At a median follow-up time of 48 months (range, 8-122 months) for 35 MBC patients, the local recurrence, distant metastasis, and OS rates were 11.4%, 28.6%, and 67.4%, respectively. CONCLUSION: The benign features of MBC on mammography and US may cause misinterpretation. However, the inclusion of malignant features observed on MRI can improve diagnostic accuracy.

Guidelines for the Diagnosis and Treatment of Primary Liver Cancer (2022 Edition).

Background: Primary liver cancer, of which around 75-85% is hepatocellular carcinoma in China, is the fourth most common malignancy and the second leading cause of tumor-related death, thereby posing a significant threat to the life and health of the Chinese people. Summary: Since the publication of Guidelines for Diagnosis and Treatment of Primary Liver Cancer in China in June 2017, which were updated by the National Health Commission in December 2019, additional high-quality evidence has emerged from researchers worldwide regarding the diagnosis, staging, and treatment of liver cancer, that requires the guidelines to be updated again. The new edition (2022 Edition) was written by more than 100 experts in the field of liver cancer in China, which not only reflects the real-world situation in China but also may reshape the nationwide diagnosis and treatment of liver cancer. Key Messages: The new guideline aims to encourage the implementation of evidence-based practice and improve the national average 5-year survival rate for patients with liver cancer, as proposed in the "Health China 2030 Blueprint."

Using the non-distortion IVIM to reduce the need for contrast agents in nasopharyngeal MRI.

BACKGROUND: Patients with nasopharyngeal carcinoma (NPC) who undergo longitudinal follow-up contrast-enhanced MRI are at risk of developing gadolinium deposition in their neural tissue, which may potentially harm them. Therefore, for these patients, a non-contrast-enhanced method is potentially beneficial as an alternative approach to predict enhancement in T1-weighted imaging (CE-T1WI). The traditional intravoxel incoherent motion (IVIM) is one of the non-contrast-enhanced methods; however, the severe distortion and signal loss limit its application in patients with NPC. The present study aimed to investigate whether non-distortion IVIM could reduce the need of CE-T1WI in the follow-up of patients with NPC. METHODS: The patients with NPC underwent Turbo Spin-echo MVXD diffusion-weighted imaging-based IVIM (non-distortion IVIM) from November 2021 to May 2022. Firstly, thirty patients with NPC were underwent both non-distortion IVIM and traditional IVIM. The distortion rate (DR) of the non-distortion IVIM was compared with the traditional IVIM. Then, twenty-one NPC patients with tumors (areas >50mm2) were included and correlation coefficient analysis was used to assess the relationship between their non-distortion IVIM and CE-T1WI. Linear regression analysis was performed to determine whether non-distortion IVIM predictors could predict CE-T1WI. RESULTS: The correlation was observed between the parameter f of the non-distortion IVIM and the enhancement ratio of CE-T1WI (r = 0.543, P = 0.011). Moreover, the linear regression analysis revealed that f was an independent IVIM predictor of CE-T1WI in patients with NPC (P = 0.011). The DR of the non-distortion IVIM was significantly smaller than that of the traditional IVIM (0.12 ± 0.05 vs 0.48 ± 0.16, P < 0.001). CONCLUSIONS: In patients with NPC, non-distortion IVIM showed potential clinical benefits to reduce the need for contrast agents, and it can independently predict the enhancement ratio.

Value of mammographic microcalcifications and MRI-enhanced lesions in the evaluation of residual disease after neoadjuvant therapy for breast cancer.

Background: Microcalcifications persist even if a patient with breast cancer achieves pathologic complete response (pCR) as confirmed by surgery after neoadjuvant treatment (NAT). In practice, surgeons tend to remove all the microcalcifications. This study aimed to explore the correlation between changes in the extent of microcalcification after NAT and pathological tumor response and compare the accuracy of mammography (MG) and magnetic resonance imaging (MRI) in predicting the size of residual tumors. Methods: This was a retrospective study which included a consecutive series of patients in Guangdong Provincial People's Hospital. Between January 2010 and January 2020, 127 patients with breast cancer and Breast Imaging Reporting and Data System (BI-RADS) 4-5 microcalcifications were included in this study. The maximum diameter of the microcalcifications on MG and lesion enhancement on MRI pre- and post-NAT were measured. The correlations between the changes in residual microcalcifications on MG and pCR were analyzed. Intraclass correlation coefficients (ICCs) were computed between the extent of the residual microcalcifications, residual enhancement, and residual tumor size. Results: There were no statistically significant differences in the changes in microcalcifications after NAT according to the RECIST criteria on MRI (P=0.09) and Miller-Payne grade (P=0.14). MRI showed a higher agreement than did residual microcalcifications on MG in predicting residual tumor size (ICC: 0.771 vs. 0.097). Conclusions: MRI is more accurate for evaluating residual tumor size in breast cancer. In our study, the extent of microcalcifications on MG after NAT had nearly no correlation with the pathological size of the residual tumor. Therefore, residual tumors with microcalcifications may not necessarily be a contraindication to breast-conserving surgery.

MRI-based Quantification of Intratumoral Heterogeneity for Predicting Treatment Response to Neoadjuvant Chemotherapy in Breast Cancer.

Background Breast cancer is highly heterogeneous, resulting in different treatment responses to neoadjuvant chemotherapy (NAC) among patients. A noninvasive quantitative measure of intratumoral heterogeneity (ITH) may be valuable for predicting treatment response. Purpose To develop a quantitative measure of ITH on pretreatment MRI scans and test its performance for predicting pathologic complete response (pCR) after NAC in patients with breast cancer. Materials and Methods Pretreatment MRI scans were retrospectively acquired in patients with breast cancer who received NAC followed by surgery at multiple centers from January 2000 to September 2020. Conventional radiomics (hereafter, C-radiomics) and intratumoral ecological diversity features were extracted from the MRI scans, and output probabilities of imaging-based decision tree models were used to generate a C-radiomics score and ITH index. Multivariable logistic regression analysis was used to identify variables associated with pCR, and significant variables, including clinicopathologic variables, C-radiomics score, and ITH index, were combined into a predictive model for which performance was assessed using the area under the receiver operating characteristic curve (AUC). Results The training data set was comprised of 335 patients (median age, 48 years [IQR, 42-54 years]) from centers A and B, and 590, 280, and 384 patients (median age, 48 years [IQR, 41-55 years]) were included in the three external test data sets. Molecular subtype (odds ratio [OR] range, 4.76-8.39 [95% CI: 1.79, 24.21]; all P < .01), ITH index (OR, 30.05 [95% CI: 8.43, 122.64]; P < .001), and C-radiomics score (OR, 29.90 [95% CI: 12.04, 81.70]; P < .001) were independently associated with the odds of achieving pCR. The combined model showed good performance for predicting pCR to NAC in the training data set (AUC, 0.90) and external test data sets (AUC range, 0.83-0.87). Conclusion A model that combined an index created from pretreatment MRI-based imaging features quantitating ITH, C-radiomics score, and clinicopathologic variables showed good performance for predicting pCR to NAC in patients with breast cancer. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Rauch in this issue.

SMILE: Cost-sensitive multi-task learning for nuclear segmentation and classification with imbalanced annotations.

High throughput nuclear segmentation and classification of whole slide images (WSIs) is crucial to biological analysis, clinical diagnosis and precision medicine. With the advances of CNN algorithms and the continuously growing datasets, considerable progress has been made in nuclear segmentation and classification. However, few works consider how to reasonably deal with nuclear heterogeneity in the following two aspects: imbalanced data distribution and diversified morphology characteristics. The minority classes might be dominated by the majority classes due to the imbalanced data distribution and the diversified morphology characteristics may lead to fragile segmentation results. In this study, a cost-Sensitive MultI-task LEarning (SMILE) framework is conducted to tackle the data heterogeneity problem. Based on the most popular multi-task learning backbone in nuclei segmentation and classification, we propose a multi-task correlation attention (MTCA) to perform feature interaction of multiple high relevant tasks to learn better feature representation. A cost-sensitive learning strategy is proposed to solve the imbalanced data distribution by increasing the penalization for the error classification of the minority classes. Furthermore, we propose a novel post-processing step based on the coarse-to-fine marker-controlled watershed scheme to alleviate fragile segmentation when nuclei are with large size and unclear contour. Extensive experiments show that the proposed method achieves state-of-the-art performances on CoNSeP and MoNuSAC 2020 datasets. The code is available at: https://github.com/panxipeng/nuclear_segandcls.

Homogeneous-resolution photoacoustic microscopy for ultrawide field-of-view neurovascular imaging in Alzheimer's disease.

Neurovascular imaging is essential for investigating neurodegenerative diseases. However, the existing neurovascular imaging technology suffers from a trade-off between a field of view (FOV) and resolution in the whole brain, resulting in an inhomogeneous resolution and lack of information. Here, homogeneous-resolution arched-scanning photoacoustic microscopy (AS-PAM), which has an ultrawide FOV to cover the entire mouse cerebral cortex, was developed. Imaging of the neurovasculature was performed with a homogenous resolution of 6.9 µm from the superior sagittal sinus to the middle cerebral artery and caudal rhinal vein in an FOV of 12 × 12 mm2. Moreover, using AS-PAM, vascular features of the meninges and cortex were quantified in early Alzheimer's disease (AD) and wild-type (WT) mice. The results demonstrated high sensitivity to the pathological progression of AD on tortuosity and branch index. The high-fidelity imaging capability in large FOV enables AS-PAM to be a promising tool for precise brain neurovascular visualization and quantification.