Signal enhancement ratio of multi-phase contrast-enhanced MRI: an imaging biomarker for survival in pancreatic adenocarcinoma.

OBJECTIVES: To evaluate signal enhancement ratio (SER) for tissue characterization and prognosis stratification in pancreatic adenocarcinoma (PDAC), with quantitative histopathological analysis (QHA) as the reference standard. METHODS: This retrospective study included 277 PDAC patients who underwent multi-phase contrast-enhanced (CE) MRI and whole-slide imaging (WSI) from three centers (2015-2021). SER is defined as (SIlt - SIpre)/(SIea - SIpre), where SIpre, SIea, and SIlt represent the signal intensity of the tumor in pre-contrast, early-, and late post-contrast images, respectively. Deep-learning algorithms were implemented to quantify the stroma, epithelium, and lumen of PDAC on WSIs. Correlation, regression, and Bland-Altman analyses were utilized to investigate the associations between SER and QHA. The prognostic significance of SER on overall survival (OS) was evaluated using Cox regression analysis and Kaplan-Meier curves. RESULTS: The internal dataset comprised 159 patients, which was further divided into training, validation, and internal test datasets (n = 60, 41, and 58, respectively). Sixty-five and 53 patients were included in two external test datasets. Excluding lumen, SER demonstrated significant correlations with stroma (r = 0.29-0.74, all p < 0.001) and epithelium (r = -0.23 to -0.71, all p < 0.001) across a wide post-injection time window (range, 25-300 s). Bland-Altman analysis revealed a small bias between SER and QHA for quantifying stroma/epithelium in individual training, validation (all within ± 2%), and three test datasets (all within ± 4%). Moreover, SER-predicted low stromal proportion was independently associated with worse OS (HR = 1.84 (1.17-2.91), p = 0.009) in training and validation datasets, which remained significant across three combined test datasets (HR = 1.73 (1.25-2.41), p = 0.001). CONCLUSION: SER of multi-phase CE-MRI allows for tissue characterization and prognosis stratification in PDAC. CLINICAL RELEVANCE STATEMENT: The signal enhancement ratio of multi-phase CE-MRI can serve as a novel imaging biomarker for characterizing tissue composition and holds the potential for improving patient stratification and therapy in PDAC. KEY POINTS: Imaging biomarkers are needed to better characterize tumor tissue in pancreatic adenocarcinoma. Signal enhancement ratio (SER)-predicted stromal/epithelial proportion showed good agreement with histopathology measurements across three distinct centers. Signal enhancement ratio (SER)-predicted stromal proportion was demonstrated to be an independent prognostic factor for OS in PDAC.

The use of longitudinal CT-based radiomics and clinicopathological features predicts the pathological complete response of metastasized axillary lymph nodes in breast cancer.

BACKGROUND: Accurate assessment of axillary status after neoadjuvant therapy for breast cancer patients with axillary lymph node metastasis is important for the selection of appropriate subsequent axillary treatment decisions. Our objectives were to accurately predict whether the breast cancer patients with axillary lymph node metastases could achieve axillary pathological complete response (pCR). METHODS: We collected imaging data to extract longitudinal CT image features before and after neoadjuvant chemotherapy (NAC), analyzed the correlation between radiomics and clinicopathological features, and developed models to predict whether patients with axillary lymph node metastasis can achieve axillary pCR after NAC. The clinical utility of the models was determined via decision curve analysis (DCA). Subgroup analyses were also performed. Then, a nomogram was developed based on the model with the best predictive efficiency and clinical utility and was validated using the calibration plots. RESULTS: A total of 549 breast cancer patients with metastasized axillary lymph nodes were enrolled in this study. 42 independent radiomics features were selected from LASSO regression to construct a logistic regression model with clinicopathological features (LR radiomics-clinical combined model). The AUC of the LR radiomics-clinical combined model prediction performance was 0.861 in the training set and 0.891 in the testing set. For the HR + /HER2 - , HER2 + , and Triple negative subtype, the LR radiomics-clinical combined model yields the best prediction AUCs of 0.756, 0.812, and 0.928 in training sets, and AUCs of 0.757, 0.777 and 0.838 in testing sets, respectively. CONCLUSIONS: The combination of radiomics features and clinicopathological characteristics can effectively predict axillary pCR status in NAC breast cancer patients.

Preoperative Risk Stratification for Gastric Cancer: The Establishment of Dual-Energy CT-Based Radiomics Using Prospective Datasets at Two Centers.

RATIONALE AND OBJECTIVES: To evaluate the performance of dual-energy CT (DECT)-based radiomics models for identifying high-risk histopathologic phenotypes-serosal invasion (pT4a), lymph node metastasis (LNM), lymphovascular invasion (LVI) and perineural invasion (PNI) in gastric cancer. MATERIAL AND METHODS: This prospective bi-center study recruited histologically confirmed gastric adenocarcinoma patients who underwent triple-phase enhanced DECT before gastrectomy between January 2021 and July 2023. Radiomics features were extracted from polychromatic/monochromatic (40 keV, 100 keV)/iodine images at arterial/venous/delay phase, respectively. Predictive features were selected in the training dataset using logistic regression classifier, and trained models were applied to the external validation dataset. Performances of clinical models, conventional contrast enhanced CT (CECT) models and DECT models were evaluated using areas under the receiver operating characteristic curve (AUCs). RESULTS: In total, 503 patients were recruited: 396 at training dataset (60.1 ± 10.8 years, 110 females, 286 males) and 107 at validation dataset (61.4 ± 9.5 years, 29 females, 78 males). DECT models dichotomizing pT4a, LNM, LVI, and PNI achieved AUCs of 0.891, 0.817, 0.834, and 0.889, respectively, in the validation dataset, similar with the CECT models. In the training dataset, compared to the CECT model, the DECT model provided increased performance for identifying pT4a, LNM, LVI (all P＜0.05), and similar performance for stratifying PNI (P = 0.104). The DECT models was associated with patient disease-free survival (all P＜0.05). CONCLUSION: DECT radiomics can stratify patients preoperatively according to high-risk histopathologic phenotypes for gastric cancer and are associated with patient disease-free survival in the training dataset.

Predicting pathologic response to neoadjuvant chemotherapy in locally advanced gastric cancer: The establishment of a spectral CT-based nomogram from prospective datasets.

BACKGROUND: To establish a spectral CT-based nomogram for predicting early neoadjuvant chemotherapy (NAC) response for locally advanced gastric cancer (LAGC). METHODS: This study prospectively recruited 222 cases (177 male and 45 female patients, 9.59 ± 9.54 years) receiving NAC and radical gastrectomy. Triple enhanced spectral CT scans were performed before NAC initiation. According to post-operative tumor regression grade (TRG), patients were classified into responders (TRG = 0 + 1) or non-responders (TRG = 2 + 3), and split into a primary (156) and validation (66) dataset at 7:3 ratio chronologically. We compared clinicopathological data, follow-up information, iodine concentration (IC), normalized ICs (nICs) in arterial/venous/delayed phases (AP/VP/DP) between responders and non-responders. Independent risk factors of response were screened by multivariable logistic regression and adopted for model construction. Model was visualized by nomograms and its capability was determined through receiver operating characteristic (ROC) curves. Log-rank survival analysis was conducted to explore associations between TRG, nomogram and patients' survival. RESULTS: This work identified Borrmann classification, ICDP, and nICDP were independent risk factors of response outcomes. A spectral CT-based nomogram was built accordingly and achieved an area under the curve (AUC) of 0.797 (0.692-0.879) and 0.741(0.661-0.811) for the primary and validation dataset, respectively, higher than AUC of individual parameters alone. The nomogram was related to disease-free survival in the validation dataset (Hazard ratio (HR): 5.19 [1.18-12.93], P = 0.02). CONCLUSIONS: The spectral CT-based nomogram provides an efficient tool for predicting the pathologic response outcomes of GC after NAC and disease-free survival risk stratification.

Spectral CT vs. diffusion-weighted imaging for the quantitative prediction of pathologic response to neoadjuvant chemotherapy in locally advanced gastric cancer.

OBJECTIVES: To compare the performance of spectral CT and diffusion-weighted imaging (DWI) for predicting pathologic response after neoadjuvant chemotherapy (NAC) in locally advanced gastric cancer (LAGC). MATERIALS AND METHODS: This was a retrospective analysis drawn from a prospective dataset. Sixty-five patients who underwent baseline concurrent triple-phase enhanced spectral CT and DWI-MRI and standard NAC plus radical gastrectomy were enrolled, and those with poor images were excluded. The tumor regression grade (TRG) was the reference standard, and patients were classified as responders (TRG 0 + 1) or non-responders (TRG 2 + 3). Quantitative iodine concentration (IC), normalized IC (nIC), and apparent diffusion coefficient (ADC) were measured by placing a freehand region of interest manually on the maximal two-dimensional plane. Their differences between responders and non-responders were compared. The performances of significant parameters were evaluated by the receiver operating characteristic analysis. The correlations between parameters and TRG status were explored through Spearman correlation coefficient test. Kaplan-Meier survival analysis was adopted to analyze their relationship with patient survival. RESULTS: nICDP and ADC were associated with the TRG and yielded comparable performances for predicting TRG categories, with area under the curve (AUC) of 0.674 and 0.673, respectively. Their combination achieved a significantly increased AUC of 0.770 (p ; 0.05) and was associated with patient disease-free survival, with hazard ratio of 2.508 (1.043-6.029). CONCLUSION: Spectral CT and DWI were equally useful imaging techniques for predicting pathologic response to NAC in LAGC. The combination of nICDP and ADC gained significant incremental benefits and was related to patient disease-free survival. CLINICAL RELEVANCE STATEMENT: Spectral CT and DWI-based quantitative measurements are effective markers for predicting the pathologic regression outcomes of locally advanced gastric cancer patients after neoadjuvant chemotherapy. KEY POINTS: • The pathologic tumor regression grade, the standard criteria for treatment response after neoadjuvant chemotherapy in gastric cancer patients, is difficult to predict early. • The quantitative parameters of normalized iodine concentration at delay phase and apparent diffusion coefficients were correlated with pathologic response; their combination demonstrated incremental benefits and was associated with patient disease-free survival. • Spectral CT and DWI are equally useful imaging modalities for predicting tumor regression grade after neoadjuvant chemotherapy in patients with locally advanced gastric cancer.

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).

A Specific, Measurable, Achievable, Relevant, Timed (SMART) Nursing Project Reduces Anxiety, Depression, and Post-Traumatic Stress Disorder in Parents of Childhood or Adolescent Osteosarcoma Patients.

Specific, measurable, achievable, relevant, timed (SMART) principle improves the nursing utility by setting individual goals for participants and helping them to achieve these goals. Our study intended to investigate the impact of a SMART nursing project on reducing mental stress and post-traumatic stress disorder (PTSD) in parents of childhood or adolescent osteosarcoma patients. In this randomized, controlled study, 66 childhood or adolescent osteosarcoma patients and 126 corresponding parents were enrolled and divided into SMART or normal care (NC) groups at a 1:1 ratio. All parents received a 3-month corresponding intervention and a 6-month interview. Our study revealed that the self-rating anxiety scale score at the 3rd month (M3) (P < 0.05) and the 6th month (M6) (P < 0.01), and anxiety rate at M3 (P < 0.05) and M6 (P < 0.05) were lower in parents in SMART group vs. NC group. The self-rating depression scale score at M3 and M6, and depression rate at M3 and M6 were lower in parents in SMART group vs. NC group (all P < 0.05). Impact of events scale-revised score at the 1st month (M1) (P < 0.05), M3 (P < 0.05), and M6 (P < 0.01) were lower in parents in SMART group vs. NC group. By subgroup analyses, the SMART nursing project showed better impacts on decreasing anxiety, depression, and PTSD in parents with an undergraduate education or above than in those with a high school education or less. Conclusively, SMART nursing project reduces anxiety, depression, and PTSD in parents of childhood or adolescent osteosarcoma patients, which is more effective in those with higher education.

The MRI radiomics signature can predict the pathologic response to neoadjuvant chemotherapy in locally advanced esophageal squamous cell carcinoma.

OBJECTIVES: To investigate the MRI radiomics signatures in predicting pathologic response among patients with locally advanced esophageal squamous cell carcinoma (ESCC), who received neoadjuvant chemotherapy (NACT). METHODS: Patients who underwent NACT from March 2015 to October 2019 were prospectively included. Each patient underwent esophageal MR scanning within one week before NACT and within 2-3 weeks after completion of NACT, prior to surgery. Radiomics features extracted from T2-TSE-BLADE were randomly split into the training and validation sets at a ratio of 7:3. According to the progressive tumor regression grade (TRG), patients were stratified into two groups: good responders (GR, TRG 0 + 1) and poor responders (non-GR, TRG 2 + 3). We constructed the Pre/Post-NACT model (Pre/Post-model) and the Delta-NACT model (Delta-model). Kruskal-Wallis was used to select features, logistic regression was used to develop the final model. RESULTS: A total of 108 ESCC patients were included, and 3/2/4 out of 107 radiomics features were selected for constructing the Pre/Post/Delta-model, respectively. The selected radiomics features were statistically different between GR and non-GR groups. The highest area under the curve (AUC) was for the Delta-model, which reached 0.851 in the training set and 0.831 in the validation set. Among the three models, Pre-model showed the poorest performance in the training and validation sets (AUC, 0.466 and 0.596), and the Post-model showed better performance than the Pre-model in the training and validation sets (AUC, 0.753 and 0.781). CONCLUSIONS: MRI-based radiomics models can predict the pathological response after NACT in ESCC patients, with the Delta-model exhibiting optimal predictive efficacy. CLINICAL RELEVANCE STATEMENT: MRI radiomics features could be used as a useful tool for predicting the efficacy of neoadjuvant chemotherapy in esophageal carcinoma patients, especially in selecting responders among those patients who may be candidates to benefit from neoadjuvant chemotherapy. KEY POINTS: • The MRI radiomics features based on T2WI-TSE-BLADE could potentially predict the pathologic response to NACT among ESCC patients. • The Delta-model exhibited the best predictive ability for pathologic response, followed by the Post-model, which similarly had better predictive ability, while the Pre-model performed less well in predicting TRG.

CT radiomics in the identification of preoperative understaging in patients with clinical stage T1-2N0 esophageal squamous cell carcinoma.

Background: Predicting preoperative understaging in patients with clinical stage T1-2N0 (cT1-2N0) esophageal squamous cell carcinoma (ESCC) is critical to customizing patient treatment. Radiomics analysis can provide additional information that reflects potential biological heterogeneity based on computed tomography (CT) images. However, to the best of our knowledge, no studies have focused on identifying CT radiomics features to predict preoperative understaging in patients with cT1-2N0 ESCC. Thus, we sought to develop a CT-based radiomics model to predict preoperative understaging in patients with cT1-2N0 esophageal cancer, and to explore the value of the model in disease-free survival (DFS) prediction. Methods: A total of 196 patients who underwent radical surgery for cT1-2N0 ESCC were retrospectively recruited from two hospitals. Among the 196 patients, 134 from Peking University Cancer Hospital were included in the training cohort, and 62 from Henan Cancer Hospital were included in the external validation cohort. Radiomics features were extracted from patients' CT images. Least absolute shrinkage and selection operator (LASSO) regression was used for feature selection and model construction. A clinical model was also built based on clinical characteristics, and the tumor size [the length, thickness and the thickness-to-length ratio (TLR)] was evaluated on the CT images. A radiomics nomogram was established based on multivariate logistic regression. The diagnostic performance of the models in predicting preoperative understaging was assessed by the area under the receiver operating characteristic curve (AUC). Kaplan-Meier curves with the log-rank test were employed to analyze the correlation between the nomogram and DFS. Results: Of the patients, 50.0% (67/134) and 51.6% (32/62) were understaged in the training and validation groups, respectively. The radiomics scores and the TLRs of the tumors were included in the nomogram. The AUCs of the nomogram for predicting preoperative understaging were 0.874 [95% confidence interval (CI): 0.815-0.933] in the training cohort and 0.812 (95% CI: 0.703-0.912) in the external validation cohort. The diagnostic performance of the nomogram was superior to that of the clinical model (P<0.05). The nomogram was an independent predictor of DFS in patients with cT1-2N0 ESCC. Conclusions: The proposed CT-based radiomics model could be used to predict preoperative understaging in patients with cT1-2N0 ESCC who have undergone radical surgery.

Biopsy-free AI-aided precision MRI assessment in prediction of prostate cancer biochemical recurrence.

BACKGROUND: To investigate the predictive ability of high-throughput MRI with deep survival networks for biochemical recurrence (BCR) of prostate cancer (PCa) after prostatectomy. METHODS: Clinical-MRI and histopathologic data of 579 (train/test, 463/116) PCa patients were retrospectively collected. The deep survival network (iBCR-Net) is based on stepwise processing operations, which first built an MRI radiomics signature (RadS) for BCR, and predicted the T3 stage and lymph node metastasis (LN+) of tumour using two predefined AI models. Subsequently, clinical, imaging and histopathological variables were integrated into iBCR-Net for BCR prediction. RESULTS: RadS, derived from 2554 MRI features, was identified as an independent predictor of BCR. Two predefined AI models achieved an accuracy of 82.6% and 78.4% in staging T3 and LN+. The iBCR-Net, when expressed as a presurgical model by integrating RadS, AI-diagnosed T3 stage and PSA, can match a state-of-the-art histopathological model (C-index, 0.81 to 0.83 vs 0.79 to 0.81, p > 0.05); and has maximally 5.16-fold, 12.8-fold, and 2.09-fold (p < 0.05) benefit to conventional D'Amico score, the Cancer of the Prostate Risk Assessment (CAPRA) score and the CAPRA Postsurgical score. CONCLUSIONS: AI-aided iBCR-Net using high-throughput MRI can predict PCa BCR accurately and thus may provide an alternative to the conventional method for PCa risk stratification.

MR imaging characteristics of different pathologic subtypes of esophageal carcinoma.

OBJECTIVES: To describe the specific MRI characteristics of different pathologic subtypes of esophageal carcinoma (EC) METHODS: This prospective study included EC patients who underwent esophageal MRI and esophagectomy between April 2015 and October 2021. Pathomorphological characteristics of EC such as localized type (LT), ulcerative type (UT), protruding type (PT), and infiltrative type (IT) were assessed by two radiologists relying on the imaging characteristics of tumor, especially the specific imaging findings on the continuity of the mucosa overlying the tumor, the opposing mucosa, mucosa linear thickening, and transmural growth pattern. Intraclass correlation coefficients (ICC) were calculated for the consistency between two readers. The associations of imaging characteristics with different pathologic subtypes were assessed using multilogistic regression model (MLR). RESULTS: A total of 201 patients were identified on histopathology with a high inter-reader agreement (ICC = 0.991). LT showed intact mucosa overlying the tumor. IT showed transmural growth pattern extending from the mucosa to the adventitia and a "sandwich" appearance. The remaining normal mucosa on the opposing side was linear and nodular in UT. PT showed correlation with T1 staging and grade 1; IT showed correlation with T3 staging and grades 2-3. Four MLR models showed high predictive performance on the test set with AUCs of 0.94 (LT), 0.87 (PT), 0.96 (IT), and 0.97 (UT), respectively, and the predictors that contributed most to the models matched the four specific characteristics. CONCLUSIONS: Different pathologic subtypes of EC displayed specific MR imaging characteristics, which could help predict T staging and the degree of pathological differentiation. CLINICAL RELEVANCE STATEMENT: Different pathologic subtypes of esophageal carcinoma displayed specific MR imaging characteristics, which correspond to differences in the degree of differentiation, T staging, and sensitivity to radiotherapy, and could also be one of the predictive factors of cause-specific survival and local progression-free rates. KEY POINTS: Different types of EC had different characteristics on MR images. A total of 91/95 (96%) LTEC showed intact mucosa over the tumor, while masses or nodules are specific to PTEC; 21/27 (78%) ITEC showed a "sandwich" sign; and 33/35 (60%) UTEC showed linear and nodular opposing mucosa. In the association of tumor type with degree of differentiation and T staging, PTEC was predominantly associated with T1 and grade 1, and ITEC was associated with T3 and grades 2-3, while LTEC and UECT were likewise primarily linked with T2-3 and grades 2-3.

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.

Application of intravoxel incoherent motion diffusion-weighted imaging for preoperative knowledge of lymphovascular invasion in gastric cancer: a prospective study.

PURPOSE: To investigate the potential of intravoxel incoherent motion diffusion-weighted imaging (IVIM) for preoperative prediction of lymphovascular invasion (LVI) in gastric cancer (GC). METHODS: This study prospectively enrolled 90 patients (62 males, 28 females, 60.79 ± 9.99 years old) who received radical gastrostomy. Abdominal MRI examinations including IVIM were performed within 1 week before surgery. Patients were divided into LVI-positive and -negative group according to pathological diagnosis after surgery. The apparent diffusion coefficient (ADC) and IVIM parameters, including true diffusion coefficient (D), pseudodiffusion coefficient (D*), and pseudodiffusion fraction (f), were compared between the two groups. The relationship between MRI parameters and LVI was studied by Spearman's correlation analysis. Multivariable logistic regression analysis was used to screen independent predictors of LVI. Receiver-operating characteristic curve analyses were applied to evaluate the efficacy. RESULTS: The ADC, D in LVI-positive group were lower, whereas tumor thickness and f parameter in LVI-positive group were higher than those in LVI-negative group, and they were statistically correlated with LVI (p < 0.05). D, f and tumor thickness were independent risk factors of LVI. The area under the curve of ADC, D, f, thickness, and the combined parameter (D + f + thickness) were 0.667, 0.754, 0.695, 0.792, and 0.876, respectively. The combined parameter demonstrated higher efficacy than any other parameters (p < 0.05). CONCLUSION: The ADC, D, and f can effectively distinguish LVI status of GC. The D, f and thickness were independent predictors. The combination of the three predictors further improved the efficacy.

Artificial intelligence-based computer-aided diagnosis system supports diagnosis of lymph node metastasis in esophageal squamous cell carcinoma: A multicenter study.

Background: This study aimed to develop an artificial intelligence-based computer-aided diagnosis system (AI-CAD) emulating the diagnostic logic of radiologists for lymph node metastasis (LNM) in esophageal squamous cell carcinoma (ESCC) patients, which contributed to clinical treatment decision-making. Methods: A total of 689 ESCC patients with PET/CT images were enrolled from three hospitals and divided into a training cohort and two external validation cohorts. 452 CT images from three publicly available datasets were also included for pretraining the model. Anatomic information from CT images was first obtained automatically using a U-Net-based multi-organ segmentation model, and metabolic information from PET images was subsequently extracted using a gradient-based approach. AI-CAD was developed in the training cohort and externally validated in two validation cohorts. Results: The AI-CAD achieved an accuracy of 0.744 for predicting pathological LNM in the external cohort and a good agreement with a human expert in two external validation cohorts (kappa = 0.674 and 0.587, p < 0.001). With the aid of AI-CAD, the human expert's diagnostic performance for LNM was significantly improved (accuracy [95% confidence interval]: 0.712 [0.669-0.758] vs. 0.833 [0.797-0.865], specificity [95% confidence interval]: 0.697 [0.636-0.753] vs. 0.891 [0.851-0.928]; p < 0.001) among patients underwent lymphadenectomy in the external validation cohorts. Conclusions: The AI-CAD could aid in preoperative diagnosis of LNM in ESCC patients and thereby support clinical treatment decision-making.

Aorta and tracheobronchial invasion in esophageal cancer: comparing diagnostic performance of 3.0-T MRI and CT.

OBJECTIVES: To compare between the diagnostic performance of 3.0-T MRI and CT for aorta and tracheobronchial invasion in patients with esophageal cancer (EC). METHODS: We prospectively included patients with pathologically confirmed EC from November 2018 to June 2021, who had baseline stage of T3-4N0-2M0 and restaging after neoadjuvant chemotherapy. All patients underwent contrast-enhanced CT and MRI of the thorax. Two independent blinded radiologists scored image quality and the presence of invasion. Agreements between the two readers were calculated using kappa test. The sensitivity, specificity, accuracy, positive predict value (PPV), and negative predict value (NPV) of MRI and CT in evaluating invasion were calculated. The net reclassification index (NRI) was used to evaluate the change in the number of patients correctly classified by MRI and CT. RESULTS: A total of 70 patients (64.8 ± 9.0 years; 53 men) were enrolled. Inter-reader agreements of image quality scores and presence of invasion by MRI and CT between the two readers were almost perfect (kappa > 0.80). The accuracy of MRI in evaluating thoracic aorta invasion was significantly higher than that of CT (reader 1: 90.0% vs. 71.4%; reader 2: 92.9% vs. 70.0%, respectively), and the accuracy of MRI in evaluating tracheobronchial invasion also was significantly higher than that of CT (reader 1: 92.9% vs. 72.9%; reader 2: 95.7% vs. 70.0%, respectively). NRI values were positive in both the evaluation of aorta and tracheobronchial invasion. CONCLUSIONS: The accuracy of 3-T MRI in determining thoracic aorta and tracheobronchial invasion is significantly higher than that of CT. KEY POINTS: • 3.0-T MRI was significantly more accurate than CT in assessing invasion of the thoracic aorta in patients with esophageal cancer. • 3.0-T MRI was also significantly more accurate than CT in assessing tracheobronchial invasion in patients with esophageal cancer. • 3.0-T MRI has a higher diagnostic performance than CT in evaluating patients with suspected aortic or tracheobronchial invasion in esophageal cancer.

Comparison of MRI and CT-Based Radiomics and Their Combination for Early Identification of Pathological Response to Neoadjuvant Chemotherapy in Locally Advanced Gastric Cancer.

BACKGROUND: Current radiomics for treatment response assessment in gastric cancer (GC) have focused solely on Computed tomography (CT). The importance of multi-parametric magnetic resonance imaging (mp-MRI) radiomics in GC is less clear. PURPOSE: To compare and combine CT and mp-MRI radiomics for pretreatment identification of pathological response to neoadjuvant chemotherapy in GC. STUDY TYPE: Retrospective. POPULATION: Two hundred twenty-five GC patients were recruited and split into training (157) and validation dataset (68) in the ratio of 7:3 randomly. FIELD/SEQUENCE: T2-weighted fast spin echo (fat suppressed T2-weighted imaging [fs-T2WI]), diffusion weighted echo planar imaging (DWI), and fast gradient echo (dynamic contrast enhanced [DCE]) sequences at 3.0T. ASSESSMENT: Apparent diffusion coefficient (ADC) maps were generated from DWI. CT, fs-T2WI, ADC, DCE, and mp-MRI Radiomics score (Radscores) were compared between responders and non-responders. A multimodal nomogram combining CT and mp-MRI Radscores was developed. Patients were followed up for 3-65 months (median 19) after surgery, the overall survival (OS) and progression free survival (PFS) were calculated. STATISTICAL TESTS: A logistic regression classifier was applied to construct the five models. Each model's performance was evaluated using a receiver operating characteristic curve. The association of the nomogram with OS/PFS was evaluated by Kaplan-Meier survival analysis and C-index. A P value <0.05 was considered statistically significant. RESULTS: CT Radscore, mp-MRI Radscore and nomogram were significantly associated with tumor regression grading. The nomogram achieved the highest area under the curves (AUCs) of 0.893 (0.834-0.937) and 0.871 (0.767-0.940) in training and validation datasets, respectively. The C-index was 0.589 for OS and 0.601 for PFS. The AUCs of the mp-MRI model were not significantly different to that of the CT model in training (0.831 vs. 0.770, P = 0.267) and validation dataset (0.797 vs. 0.746, P = 0.137). DATA CONCLUSIONS: mp-MRI radiomics provides similar results to CT radiomics for early identification of pathologic response to neoadjuvant chemotherapy. The multimodal radiomics nomogram further improved the capability. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: 2.

Noninvasive Imaging of Tumor PD-L1 Expression Using [99mTc]Tc-Labeled KN035 with SPECT/CT.

Programmed cell death protein-1/ligand-1 (PD-1/PD-L1) checkpoint blockade is a major breakthrough in cancer therapy, but identifying patients likely to benefit from this therapy remains challenging. Immunohistochemistry is not informative about PD-L1 expression heterogeneity because of the limitations of invasive tissue collection. Noninvasive SPECT imaging is an approach to patient selection and therapeutic monitoring by assessing the PD-L1 status throughout the whole body. Here, we radiolabeled a single-domain PD-L1 antibody with technetium-99m (99mTc) for immune-SPECT imaging to evaluate its feasibility of detecting PD-L1 expression. The radiochemical purity of [99mTc]Tc-HYNIC-KN035 was 99.40 ± 0.11% with a specific activity of 2.68 MBq/µg. [99mTc]Tc-HYNIC-KN035 displayed a high PD-L1 specificity both in vitro and in vivo and showed a high specific affinity for PD-L1 with an equilibrium dissociation constant (KD) of 31.04 nM. The binding of [99mTc]Tc-HYNIC-KN035 to H1975 cells (high expression of PD-L1) was much higher than to A549 cells (low expression of PD-L1). SPECT/CT imaging showed that H1975 tumors were visualized at 4 h post-injection and became clearer with time. However, mild tumor uptake was observed in A549 tumors and H1975 tumors of the blocking group at all time points. The uptake value of [99mTc]Tc-HYNIC-KN035 in H1975 tumors was increased continuously from 9.68 ± 0.91% ID/g at 4 h to 13.31 ± 2.23% ID/g at 24 h post-injection, which was higher than in A549 tumors with %ID/g of 4.59 ± 0.76 and 5.54 ± 0.28 at 4 and 24 h post-injection, respectively. These specific bindings were confirmed by blocking studies. [99mTc]Tc-HYNIC-KN035 can be synthesized easily and specifically targeted to PD-L1 in the tumor environment, allowing PD-L1 expression assessment noninvasively and dynamically with SPECT/CT imaging.

Multiparametric MRI-based radiomics nomogram for early prediction of pathological response to neoadjuvant chemotherapy in locally advanced gastric cancer.

OBJECTIVES: To build and validate a multi-parametric MRI (mpMRI)-based radiomics nomogram for early prediction of treatment response to neoadjuvant chemotherapy (NAC) in locally advanced gastric cancer. METHODS: Baseline MRI were retrospectively enrolled from 141 patients with gastric adenocarcinoma who received NAC followed by radical gastrectomy. According to pathologic response of tumor regression grading (TRG), patients were labeled as responders (TRG = 0 + 1) and non-responders (TRG = 2 + 3) and further divided into a training (n = 85) and validation dataset (n = 56). Radiomics score (Radscore) were built from T2WI, ADC, and venous phase of dynamic-contrasted-enhanced MR imaging. Clinical information, laboratory indicators, MRI parameters, and follow-up data were also recorded. According to multivariable regression analysis, an mpMRI radiomics nomogram was built and its predictive ability was evaluated by ROC analysis. Decision curve analysis was applied to evaluate the clinical usefulness. Kaplan-Meier survival curves based on the nomogram were used to estimate the progression-free survival (PFS) and overall survival (OS) in the validation dataset. RESULTS: Both single sequence-based Radscores and mpMRI radiomics nomogram were associated with pathologic response (p < 0.001). The nomogram achieved the highest diagnostic ability with area under ROC curve of 0.844 (95% CI, 0.749-0.914) and 0.820 (95% CI, 0.695-0.910) in the training and validation datasets. The hazard ratio of the nomogram for PFS and OS prediction was 2.597 (95% CI: 1.046-6.451, log-rank p = 0.023) and 2.570 (95% CI: 1.166-5.666, log-rank p = 0.011). CONCLUSIONS: The mpMRI-based radiomics nomogram showed preferable performance in predicting pathologic response to NAC in LAGC. KEY POINTS: • This study investigated the value of multi-parametric MRI-based radiomics in predicting pathologic response to neoadjuvant chemotherapy in locally advanced gastric cancer. • The nomogram incorporating T2WI Radscore, ADC Radscore, and DCE Radscore as well as Borrmann classification outperformed the single sequence-based Radscore. • The nomogram also exhibited a promising prognostic ability for patient survival and enriched radiomics studies in gastric cancer.

Spectral CT for preoperative prediction of lymphovascular invasion in resectable gastric cancer: With external prospective validation.

Objectives: To develop and externally validate a spectral CT based nomogram for the preoperative prediction of LVI in patients with resectable GC. Methods: The two centered study contained a retrospective primary dataset of 224 pathologically confirmed gastric adenocarcinomas (161 males, 63 females; mean age: 60.57 ± 10.81 years, range: 20-86 years) and an external prospective validation dataset from the second hospital (77 males and 35 females; mean age, 61.05 ± 10.51 years, range, 31 to 86 years). Triple-phase enhanced CT scans with gemstone spectral imaging mode were performed within one week before surgery. The clinicopathological characteristics were collected, the iodine concentration (IC) of the primary tumours at arterial phase (AP), venous phase (VP), and delayed phase (DP) were measured and then normalized to aorta (nICs). Univariable analysis was used to compare the differences of clinicopathological and IC values between LVI positive and negative groups. Independent predictors for LVI were screened by multivariable logistic regression analysis in primary dataset and used to develop a nomogram, and its performance was evaluated by using ROC analysis and tested in validation dataset. Its clinical use was evaluated by decision curve analysis (DCA). Results: Tumor thickness, Borrmann classification, CT reported lymph node (LN) status and nICDP were independent predictors for LVI, and the nomogram based on these indicators was significantly associated with LVI (P<0.001). It yielded an AUC of 0.825 (95% confidence interval [95% CI], 0.769-0.872) and 0.802 (95% CI, 0.716-0.871) in primary and validation datasets (all P<0.05), with promising clinical utility by DCA. Conclusion: This study presented a dual energy CT quantification based nomogram, which enables preferable preoperative individualized prediction of LVI in patients with GC.