Resting state functional connectome in breast cancer patients with fear of cancer recurrence.

This study aimed to investigate network-level brain functional changes in breast cancer patients and their relationship with fear of cancer recurrence (FCR). Resting-state functional MRI was collected from 43 patients with breast cancer and 40 healthy controls (HCs). Graph theory analyses, whole-brain voxel-wise functional connectivity strength (FCS) analyses and seed-based functional connectivity (FC) analyses were performed to identify connection alterations in breast cancer patients. Correlations between brain functional connections (i.e. FCS and FC) and FCR level were assessed to further reveal the neural mechanisms of FCR in breast cancer patients. Graph theory analyses indicated a decreased clustering coefficient in breast cancer patients compared to HCs (P = 0.04). Patients with breast cancer exhibited significantly higher FCS in both higher-order function networks (frontoparietal, default mode, and dorsal attention systems) and primary somatomotor networks. Among the hyperconnected regions in breast cancer, the left inferior frontal operculum demonstrated a significant positive correlation with FCR. Our findings suggest that breast cancer patients exhibit less segregation of brain function, and the left inferior frontal operculum is a key region associated with FCR. This study offers insights into the neural mechanisms of FCR in breast cancer patients at the level of brain connectome.

Extranodal Extension at Pretreatment MRI and the Prognostic Value for Patients with Rectal Cancer.

Background Detection of extranodal extension (ENE) at pathology is a poor prognostic indicator for rectal cancer, but whether ENE can be identified at pretreatment MRI is, to the knowledge of the authors, unknown. Purpose To evaluate the performance of pretreatment MRI in detecting ENE using a matched pathologic reference standard and to assess its prognostic value in patients with rectal cancer. Materials and Methods This single-center study included a prospective development data set consisting of participants with rectal adenocarcinoma who underwent pretreatment MRI and radical surgery (December 2021 to January 2023). MRI characteristics were identified by their association with ENE-positive nodes (χ2 test and multivariable logistic regression) and the performance of these MRI features was assessed (area under the receiver operating characteristic curve [AUC]). Interobserver agreement was assessed by Cohen κ coefficient. The prognostic value of ENE detected with MRI for predicting 3-year disease-free survival was assessed by Cox regression analysis in a retrospective independent validation cohort of patients with locally advanced rectal cancer (December 2019 to July 2020). Results The development data set included 147 participants (mean age, 62 years ± 11 [SD]; 87 male participants). The retrospective cohort included 110 patients (mean age, 60 years ± 9; 79 male participants). Presence of vessel interruption and fusion (both P < .001), heterogeneous internal structure, and the broken-ring and tail signs (odds ratio range, 4.10-23.20; P value range, <.001 to .002) were predictors of ENE at MRI, and together achieved an AUC of 0.91 (95% CI: 0.88, 0.93) in detecting ENE. Interobserver agreement was moderate for the presence of vessel interruption and fusion (κ = 0.46 for both) and substantial for others (κ = 0.61-0.67). The presence of ENE at pretreatment MRI was independently associated with worse 3-year disease-free survival (hazard ratio, 3.00; P = .02). Conclusion ENE can be detected at pretreatment MRI, and its presence was associated with worse prognosis for patients with rectal cancer. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Eberhardt in this issue.

Early Contrast-Enhanced MR for Diagnosing Complete Tumor Response of Locally Advanced Esophageal Squamous Cell Carcinoma After Neoadjuvant Therapy: A Retrospective Comparative Study.

BACKGROUND: This study assessed the performance of early contrast-enhanced magnetic resonance (ECE-MR) in the detecting of complete tumor response (ypT0) in patients with esophageal squamous cell carcinoma following neoadjuvant therapy. PATIENTS AND METHODS: Preoperative MR images of consecutive patients who underwent neoadjuvant therapy and surgical resection were reviewed retrospectively. The accuracy of ECE-MR and T2WI+DWI was evaluated by comparing the findings with pathological results. Receiver operating characteristic curve analysis was used to assess the diagnostic performance, and DeLong method was applied to compare the areas under the curves (AUC). Chi-squared analysis was conducted to explore the difference in pathological changes. RESULTS: A total of 198 patients (mean age 62.6 ± 7.8 years, 166 men) with 201 lesions were included. The AUC of ECE-MR was 0.85 (95% CI 0.79-0.90) for diagnosing ypT1-4, which was significantly higher than that of T2WI+DWI (AUC 0.69, 95% CI 0.63-0.76, p < 0.001). The diagnostic performance of both T2WI+DWI and ECE-MR improved with increasing tumor stage. The AUCs of ECE-MRI were higher in ypT1 and ypT2 tumors than T2WI+DWI. Degree 2-3 tumor-infiltrating lymphocytes and neutrophils were commonly seen in ypT0 tumors misdiagnosed by ECE-MR. CONCLUSIONS: Visual evaluation of ECE-MR is a promising diagnostic protocol for the detection of complete tumor response, especially for differentiation with early stage tumors. The accurate diagnosis of complete tumor response after neoadjuvant therapy using imaging modalities is of important significance for clinical decision-making for patients with esophageal squamous cell carcinoma. It is hoped that early contrast-enhanced MR will provide supportive advice for the development of individualized treatment options for patients.

Fully semantic segmentation for rectal cancer based on post-nCRT MRl modality and deep learning framework.

PURPOSE: Rectal tumor segmentation on post neoadjuvant chemoradiotherapy (nCRT) magnetic resonance imaging (MRI) has great significance for tumor measurement, radiomics analysis, treatment planning, and operative strategy. In this study, we developed and evaluated segmentation potential exclusively on post-chemoradiation T2-weighted MRI using convolutional neural networks, with the aim of reducing the detection workload for radiologists and clinicians. METHODS: A total of 372 consecutive patients with LARC were retrospectively enrolled from October 2015 to December 2017. The standard-of-care neoadjuvant process included 22-fraction intensity-modulated radiation therapy and oral capecitabine. Further, 243 patients (3061 slices) were grouped into training and validation datasets with a random 80:20 split, and 41 patients (408 slices) were used as the test dataset. A symmetric eight-layer deep network was developed using the nnU-Net Framework, which outputs the segmentation result with the same size. The trained deep learning (DL) network was examined using fivefold cross-validation and tumor lesions with different TRGs. RESULTS: At the stage of testing, the Dice similarity coefficient (DSC), 95% Hausdorff distance (HD95), and mean surface distance (MSD) were applied to quantitatively evaluate the performance of generalization. Considering the test dataset (41 patients, 408 slices), the average DSC, HD95, and MSD were 0.700 (95% CI: 0.680-0.720), 17.73 mm (95% CI: 16.08-19.39), and 3.11 mm (95% CI: 2.67-3.56), respectively. Eighty-two percent of the MSD values were less than 5 mm, and fifty-five percent were less than 2 mm (median 1.62 mm, minimum 0.07 mm). CONCLUSIONS: The experimental results indicated that the constructed pipeline could achieve relatively high accuracy. Future work will focus on assessing the performances with multicentre external validation.

Radiomics analysis from magnetic resonance imaging in predicting the grade of nonfunctioning pancreatic neuroendocrine tumors: a multicenter study.

OBJECTIVES: To explore the potential of radiomics features to predict the histologic grade of nonfunctioning pancreatic neuroendocrine tumor (NF-PNET) patients using non-contrast sequence based on MRI. METHODS: Two hundred twenty-eight patients with NF-PNETs undergoing MRI at 5 centers were retrospectively analyzed. Data from center 1 (n = 115) constituted the training cohort, and data from centers 2-5 (n = 113) constituted the testing cohort. Radiomics features were extracted from T2-weighted images and the apparent diffusion coefficient. The least absolute shrinkage and selection operator was applied to select the most important features and to develop radiomics signatures. The area under receiver operating characteristic curve (AUC) was performed to assess models. RESULTS: Tumor boundary, enhancement homogeneity, and vascular invasion were used to construct the radiological model to stratify NF-PNET patients into grade 1 and 2/3 groups, which yielded AUC of 0.884 and 0.684 in the training and testing groups. A radiomics model including 4 features was constructed, with an AUC of 0.941 and 0.871 in the training and testing cohorts. The fusion model combining the radiomics signature and radiological characteristics showed good performance in the training set (AUC = 0.956) and in the testing set (AUC = 0.864), respectively. CONCLUSION: The developed model that integrates radiomics features with radiological characteristics could be used as a non-invasive, dependable, and accurate tool for the preoperative prediction of grade in NF-PNETs. CLINICAL RELEVANCE STATEMENT: Our study revealed that the fusion model based on a non-contrast MR sequence can be used to predict the histologic grade before operation. The radiomics model may be a new and effective biological marker in NF-PNETs. KEY POINTS: The diagnostic performance of the radiomics model and fusion model was better than that of the model based on clinical information and radiological features in predicting grade 1 and 2/3 of nonfunctioning pancreatic neuroendocrine tumors (NF-PNETs). Good performance of the model in the four external testing cohorts indicated that the radiomics model and fusion model for predicting the grades of NF-PNETs were robust and reliable, indicating the two models could be used in the clinical setting and facilitate the surgeons' decision on risk stratification. The radiomics features were selected from non-contrast T2-weighted images (T2WI) and diffusion-weighted imaging (DWI) sequence, which means that the administration of contrast agent was not needed in grading the NF-PNETs.

Small arteriole sign: an imaging feature for staging T4a colon cancer.

OBJECTIVES: By analyzing the distribution of existing and newly proposed staging imaging features in pT1-3 and pT4a tumors, we searched for a salient feature and validated its diagnostic performance. METHODS: Preoperative multiphase contrast-enhanced CT images of the training cohort were retrospectively collected at three centers from January 2016 to December 2017. We used the chi-square test to analyze the distribution of several stage-related imaging features in pT1-3 and pT4a tumors, including small arteriole sign (SAS), outer edge of the intestine, tumor invasion range, and peritumoral adipose tissue. Preoperative multiphase contrast-enhanced CT images of the validation cohort were retrospectively collected at Beijing Cancer Hospital from January 2018 to December 2018. The diagnostic performance of the selected imaging feature, including accuracy, sensitivity, and specificity, was validated and compared with the conventional clinical tumor stage (cT) by the McNemar test. RESULTS: In the training cohort, a total of 268 patients were enrolled, and only SAS was significantly different between pT1-3 and pT4a tumors. The accuracy, sensitivity, and specificity of the SAS and conventional cT in differentiating T1-3 and T4a tumors were 94.4%, 81.6%, and 97.3% and 53.7%, 32.7%, and 58.4%, respectively (all p < 0.001). In the validation cohort, a total of 135 patients were collected. The accuracy, sensitivity, and specificity of the SAS and the conventional cT were 93.3%, 76.2%, and 96.5% and 62.2%, 38.1%, and 66.7%, respectively (p < 0.001, p = 0.021, p < 0.001). CONCLUSION: Small arteriole sign positivity, an indirect imaging feature of serosa invasion, may improve the accuracy of identifying T4a colon cancer. CLINICAL RELEVANCE STATEMENT: Small arteriole sign helps to distinguish T1-3 and T4a colon cancer and further improves the accuracy of preoperative CT staging of colon cancer. KEY POINTS: • The accuracy of preoperative CT staging of colon cancer is not ideal, especially for T4a tumors. • Small arteriole sign (SAS) is a newly defined imaging feature that shows the appearance of tumor-supplying arterioles at the site where they penetrate the intestine wall. • SAS is an indirect imaging marker of tumor invasion into the serosa with a great value in distinguishing between T1-3 and T4a colon cancer.

Peri-lesion regions in differentiating suspicious breast calcification-only lesions specifically on contrast enhanced mammography.

PURPOSE: The explore the added value of peri-calcification regions on contrast-enhanced mammography (CEM) in the differential diagnosis of breast lesions presenting as only calcification on routine mammogram. METHODS: Patients who underwent CEM because of suspicious calcification-only lesions were included. The test set included patients between March 2017 and March 2019, while the validation set was collected between April 2019 and October 2019. The calcifications were automatically detected and grouped by a machine learning-based computer-aided system. In addition to extracting radiomic features on both low-energy (LE) and recombined (RC) images from the calcification areas, the peri-calcification regions, which is generated by extending the annotation margin radially with gradients from 1 mm to 9 mm, were attempted. Machine learning (ML) models were built to classify calcifications into malignant and benign groups. The diagnostic matrices were also evaluated by combing ML models with subjective reading. RESULTS: Models for LE (significant features: wavelet-LLL_glcm_Imc2_MLO; wavelet-HLL_firstorder_Entropy_MLO; wavelet-LHH_glcm_DifferenceVariance_CC; wavelet-HLL_glcm_SumEntropy_MLO;wavelet-HLH_glrlm_ShortRunLowGray LevelEmphasis_MLO; original_firstorder_Entropy_MLO; original_shape_Elongation_MLO) and RC (significant features: wavelet-HLH_glszm_GrayLevelNonUniformityNormalized_MLO; wavelet-LLH_firstorder_10Percentile_CC; original_firstorder_Maximum_MLO; wavelet-HHH_glcm_Autocorrelation_MLO; original_shape_Elongation_MLO; wavelet-LHL_glszm_GrayLevelNonUniformityNormalized_MLO; wavelet-LLH_firstorder_RootMeanSquared_MLO) images were set up with 7 features. Areas under the curve (AUCs) of RC models are significantly better than those of LE models with compact and expanded boundary (RC v.s. LE, compact: 0.81 v.s. 0.73, p < 0.05; expanded: 0.89 v.s. 0.81, p < 0.05) and RC models with 3 mm boundary extension yielded the best performance compared to those with other sizes (AUC = 0.89). Combining with radiologists' reading, the 3mm-boundary RC model achieved a sensitivity of 0.871 and negative predictive value of 0.937 with similar accuracy of 0.843 in predicting malignancy. CONCLUSIONS: The machine learning model integrating intra- and peri-calcification regions on CEM has the potential to aid radiologists' performance in predicting malignancy of suspicious breast calcifications.

Intentional Watch and Wait or Organ Preservation Surgery Following Neoadjuvant Chemoradiotherapy Plus Consolidation CAPEOX for MRI-defined Low-risk Rectal Cancer: Findings From a Prospective Phase 2 Trial (PKUCH-R01 Trial, NCT02860234).

OBJECTIVE: To assess the efficacy and safety of intentional watch and wait (W&W) and organ preservation surgery following neoadjuvant chemoradiotherapy plus consolidation CAPEOX in magnetic resonance imaging (MRI)-defined low-risk rectal cancer. BACKGROUND: Clinical T2/early T3 rectal cancers can achieve high yield pathological complete response (ypCR) rates after chemoradiotherapy; thus, an intentional W&W or organ preservation strategy for good clinical responders in these subgroups can be further tested. METHODS: This prospective, single-arm, phase 2 trial enrolled patients with low-risk MRI prestaged rectal cancers, who concurrently received chemoradiation, followed by four 3-weekly cycles of CAPEOX regimen. Following reassessment, clinical complete response (cCR) or near-cCR patients underwent W&W/organ preservation surgery; the primary endpoint was a 3-year organ preservation rate. RESULTS: Of the 64 participants, 58 completed treatment, with 6.4% and 33.9% grade 3 to 4 toxicities in the radiotherapy and consolidation CAPEOX phases, respectively, during a median 39.5-month follow-up. Initial cCR, and non-cCR occurred in 33, 13, and 18 patients, respectively. Of the 31 cCR and 7 near-cCR cases managed by W&W, local regrowth occurred in 7; of these, 6 received salvage surgery. The estimated 2-year local regrowth rates were 12.9% [95% confidence interval (CI): 1.1%-24.7%] in cCR and 42.9% (95% CI: 6.2%-79.6%) in near-cCR cases, respectively. Eight patients received local excision, including 2 with regrowth salvage. Lung metastases occurred in 3 patients and multiple metastasis occurred in 1 patient; no local recurrence occurred. The estimated 3-year organ preservation rate was 67.2% (95% CI: 55.6%-78.8%). The estimated 3-year cancer-specific survival, non-regrowth disease-free survival, and stoma-free survival were 96.6% (95% CI: 92.1%-100%), 92.2% (95% CI: 85.5%-98.9%), and 82.7% (95% CI: 73.5%-91.9%), respectively. CONCLUSIONS: Chemoradiotherapy plus consolidation CAPEOX for MRI-defined low-risk rectal cancer can lead to high rates of organ preservation through intentional W&W or local excision. The oncologic safety of this strategy should be further tested.

CT Radiomics for Predicting Pathological Complete Response of Axillary Lymph Nodes in Breast Cancer After Neoadjuvant Chemotherapy: A Prospective Study.

BACKGROUND: The diagnostic effectiveness of traditional imaging techniques is insufficient to assess the response of lymph nodes (LNs) to neoadjuvant chemotherapy (NAC), especially for pathological complete response (pCR). A radiomics model based on computed tomography (CT) could be helpful. PATIENTS AND METHODS: Prospective consecutive breast cancer patients with positive axillary LNs initially were enrolled, who received NAC prior to surgery. Chest contrast-enhanced thin-slice CT scan was performed both before and after the NAC (recorded as the first and the second CT respectively), and on both of them, the target metastatic axillary LN was identified and demarcated layer by layer. Using pyradiomics-based software that was independently created, radiomics features were retrieved. A pairwise machine learning workflow based on Sklearn (https://scikit-learn.org/) and FeAture Explorer was created to increase diagnostic effectiveness. An effective pairwise auto encoder model was developed by the improvement of data normalization, dimensionality reduction, and features screening scheme as well as the comparison of the prediction effectiveness of the various classifiers. RESULTS: A total of 138 patients were enrolled, and 77 (58.7%) in the overall group achieved pCR of LN after NAC. Nine radiomics features were finally chosen for modeling. The AUCs of the training group, validation group, and test group were 0.944 (0.919-0.965), 0.962 (0.937-0.985), and 1.000 (1.000-1.000), respectively, and the corresponding accuracies were 0.891, 0.912, and 1.000. CONCLUSION: The pCR of axillary LNs in breast cancer following NAC can be precisely predicted using thin-sliced enhanced chest CT-based radiomics.

CT radiomics features of meso-esophageal fat in predicting overall survival of patients with locally advanced esophageal squamous cell carcinoma treated by definitive chemoradiotherapy.

OBJECTIVE: To investigate the value of CT radiomics features of meso-esophageal fat in the overall survival (OS) prediction of patients with locally advanced esophageal squamous cell carcinoma (ESCC). METHODS: A total of 166 patients with locally advanced ESCC in two medical centers were retrospectively analyzed. The volume of interest (VOI) of meso-esophageal fat and tumor were manually delineated on enhanced chest CT using ITK-SNAP. Radiomics features were extracted from the VOIs by Pyradiomics and then selected using the t-test, the Cox regression analysis, and the least absolute shrinkage and selection operator. The radiomics scores of meso-esophageal fat and tumors for OS were constructed by a linear combination of the selected radiomic features. The performance of both models was evaluated and compared by the C-index. Time-dependent receiver operating characteristic (ROC) analysis was employed to analyze the prognostic value of the meso-esophageal fat-based model. A combined model for risk evaluation was constructed based on multivariate analysis. RESULTS: The CT radiomic model of meso-esophageal fat showed valuable performance for survival analysis, with C-indexes of 0.688, 0.708, and 0.660 in the training, internal, and external validation cohorts, respectively. The 1-year, 2-year, and 3-year ROC curves showed AUCs of 0.640-0.793 in the cohorts. The model performed equivalently compared to the tumor-based radiomic model and performed better compared to the CT features-based model. Multivariate analysis showed that meso-rad-score was the only factor associated with OS. CONCLUSIONS: A baseline CT radiomic model based on the meso-esophagus provide valuable prognostic information for ESCC patients treated with dCRT.

Quantitative CT evaluation after two cycles of induction chemotherapy to predict prognosis of patients with locally advanced oesophageal squamous cell carcinoma before undergoing definitive chemoradiotherapy/radiotherapy.

OBJECTIVE: To investigate the performance of quantitative CT analysis in predicting the prognosis of patients with locally advanced oesophageal squamous cell carcinoma (ESCC) after two cycles of induction chemotherapy before definitive chemoradiotherapy/radiotherapy. METHODS: A total of 110 patients with locally advanced ESCC were retrospectively analysed. Baseline chest CT and CT after two cycles of induction chemotherapy were analysed. A multivariate Cox proportional-hazard regression model was used to identify independent prognostic markers for survival analysis. Then, a CT scoring system was established. Time-dependent receiver operating characteristic (ROC) curve analysis and the Kaplan-Meier method were employed for analysing the prognostic value of the CT scoring system. RESULTS: Body mass index, treatment strategy, change ratios of thickness (ΔTHmax), CT value of the primary tumour (ΔCTVaxial) and the short diameter (ΔSD-LN), and the presence of an enlarged small lymph node (ESLN) after two cycles of chemotherapy were noted as independent factors for predicting overall survival (OS). The specificity of the presence of ESLN for death after 12 months was up to 100%. Areas under the curve value of the CT scoring system for predicting OS and progression-free survival (PFS) were higher than that of the RECIST (p < 0.05). Responders had significantly longer OS and PFS than non-responders. CONCLUSION: Quantitative CT analysis after two cycles of induction chemotherapy could predict the outcome of locally advanced ESCC patients treated with definitive chemoradiotherapy/radiotherapy. The CT scoring system could contribute to the development of an appropriate strategy for patients with locally advanced ESCC. KEY POINTS: • Quantitative CT evaluation after two cycles of induction chemotherapy can predict the long-term outcome of locally advanced oesophageal cancer treated with definitive chemoradiotherapy/radiotherapy. • A CT scoring system provides valuable imaging support for indicating the prognosis at the early stage of therapy. • Quantitative CT evaluation can assist clinicians in personalising treatment plans.

Effect of concomitant atrial septal defect on left ventricular function in adult patients with unrepaired Ebstein's anomaly: a cardiovascular magnetic resonance imaging study.

BACKGROUND: Due to the heterogeneity of anatomic anomalies in Ebstein's anomaly (EA), particularly in the subset of patients with atrial septal defect (ASD), hemodynamic changes, which ultimately cause left ventricular (LV) deterioration remain unclear. The current study aimed to investigate the effect of concomitant ASD on LV function using cardiovascular magnetic resonance (CMR) imaging in patients with EA. METHODS: This study included 31 EA patients with ASD, 76 EA patients without ASD, 35 patients with simple ASD and 40 healthy controls. Left/right ventricular (RV, the RV was defined as a summation of the functional RV and atrialized RV in EA patients) volumes and functional parameters, LV strain parameters, and echocardiogram indices were compared among the four groups. Associations between variables were evaluated via Spearman or Pearson correlation analyses. The association between risk factors and LV ejection fraction (EF) was determined via multivariate linear regression analysis. RESULTS: Both EA patients and ASD patients had a higher RV/LV end-diastolic volume (RVEDV/LVEDV) as well as lower LV and RV ejection fractions (LVEF/RVEF) compared to healthy controls (all p < 0.05). Moreover, the EA patients with ASD had a significantly higher RVEDV/LVEDV and a lower LVEF and RVEF than those without ASD (all p < 0.05). Multivariate linear regression analysis revealed that the presence of ASD was independently associated with LVEF (ß = - 0.337, p < 0.001). The RVEDV/LVEDV index was associated with LVEF (r = - 0.361, p < 0.001). Furthermore, the LV longitudinal peak diastolic strain rate (PDSR) was lower in EA patients with ASD than those without ASD, patients with simple ASD, and healthy controls (p < 0.05). CONCLUSION: Concomitant ASD is an important risk factor of LV dysfunction in patients with EA, and diastolic dysfunction is likely the predominate mechanism related to LV dysfunction.

Discrimination of Liver Metastases of Digestive System Neuroendocrine Tumors From Neuroendocrine Carcinoma by Computed Tomography-Based Radiomics Analysis.

OBJECTIVE: The aim of the study is to investigate the value of computed tomography (CT) radiomics features to discriminate the liver metastases (LMs) of digestive system neuroendocrine tumors (NETs) from neuroendocrine carcinoma (NECs). METHODS: Ninety-nine patients with LMs of digestive system neuroendocrine neoplasms from 2 institutions were included. Radiomics features were extracted from the portal venous phase CT images by the Pyradiomics and then selected by using the t test, Pearson correlation analysis, and least absolute shrinkage and selection operator method. The radiomics score (Rad score) for each patient was constructed by linear combination of the selected radiomics features. The radiological model was constructed by radiological features using the multivariable logistic regression. Then, the combined model was constructed by combining Rad score and the radiological model into logistic regression. The performance of all models was evaluated by the receiver operating characteristic curves with the area under curve (AUC). RESULTS: In the radiological model, only the enhancement degree (odds ratio, 8.299; 95% confidence interval, 2.070-32.703; P = 0.003) was an independent predictor for discriminating the LMs of digestive system NETs from those of NECs. The combined model constructed by the Rad score in combination with the enhancement degree showed good discrimination performance, with AUCs of 0.893, 0.841, and 0.740 in the training, testing, and external validation groups, respectively. In addition, it performed better than radiological model in the training and testing groups (AUC, 0.893 vs 0.726; AUC, 0.841 vs 0.621). CONCLUSIONS: The CT radiomics might be useful for discrimination LMs of digestive system NECs from NETs.

Analysis of enhanced CT imaging signs and clinicopathological prognostic factors in hepatoid adenocarcinoma of stomach patients with radical surgery: a retrospective study.

BACKGROUND: To investigate the association between CT signs and clinicopathological features and disease recurrence in patients with hepatoid adenocarcinoma of stomach (HAS). METHODS: Forty nine HAS patients undergoing radical surgery were retrospectively collected. Association between CT and clinicopathological features and disease recurrence was analyzed. Multivariate logistic model was constructed and evaluated for predicting recurrence by using receiver operating characteristic (ROC) curve. Survival curves between model-defined risk groups was compared using Kaplan-Meier method. RESULTS: 24(49.0%) patients developed disease recurrence. Multivariate logistic analysis results showed elevated serum CEA level, peritumoral fatty space invasion and positive pathological vascular tumor thrombus were independent factors for disease recurrence. Odds ratios were 10.87 (95%CI, 1.14-103.66), 6.83 (95%CI, 1.08-43.08) and 42.67 (95%CI, 3.66-496.85), respectively. The constructed model showed an area under ROC of 0.912 (95%CI,0.825-0.999). The model-defined high-risk group showed poorer overall survival and recurrence-free survival than the low-risk group (both P < 0.001). CONCLUSIONS: Preoperative CT appearance of peritumoral fatty space invasion, elevated serum CEA level, and pathological vascular tumor thrombus indicated poor prognosis of HAS patients.

Tumor attachment to Major intrahepatic vascular for Colorectal liver metastases.

BACKGROUND: Colorectal liver metastases attached major intrahepatic vessels has been considered to be a risk factor for survival outcome after liver resection. The present study aimed to clarify the outcomes of R1 surgery (margin < 1 mm) in CRLM patients, distinguishing parenchymal margin R1 and attached to major intrahepatic vessels R1. METHODS: In present study, 283 CRLM patients who were evaluated to be attached to major intrahepatic vessels initially and underwent liver resection following preoperative chemotherapy. They were assigned to two following groups: R0 (n = 167), R1 parenchymal (n = 58) and R1 vascular (n = 58). The survival outcomes and local recurrence rates were analyzed in each group. RESULTS: Overall, 3- and 5-year overall survival rates after liver resection were 53.0% and 38.2% (median overall survival 37 months). Five-year overall survival was higher in patients with R0 than parenchymal R1 (44.9%% vs. 26.3%, p = 0.009), whereas there was no significant difference from patients with vascular R1 (34.3%, p = 0.752). In the multivariable analysis, preoperative chemotherapy > 4 cycles, clinical risk score 3-5, RAS mutation, parenchymal R1 and CA199 > 100 IU/ml were identified as independent predictive factors of overall survival (p < 0.05). There was no significant difference for local recurrence among three groups. CONCLUSION: Parenchymal R1 resection was independent risk factor for CRLM. Vascular R1 surgery achieved survival outcomes equivalent to R0 resection. Non-anatomic liver resection for CRLM attached to intrahepatic vessels might be pursued to increase patient resectability by preoperative chemotherapy.

Contrast-enhanced MRI for T Restaging of Locally Advanced Rectal Cancer Following Neoadjuvant Chemotherapy and Radiation Therapy.

Background Accurate restaging of rectal cancer is crucial in the selection of candidates for local excision after neoadjuvant chemotherapy and radiation therapy (NCRT). The conventional approach of combined T2-weighted imaging and diffusion-weighted imaging (DWI) at MRI has been found to have limitations in restaging. Purpose To determine the diagnostic performance of contrast-enhanced MRI in distinguishing between pathologic stage ypT0-1 and ypT2-4 rectal cancer after NCRT compared with T2-weighted imaging and DWI by using surgical pathologic specimens as the reference standard. Materials and Methods This retrospective study evaluated MRI scans in all consecutive patients with locally advanced rectal cancer who underwent total mesorectal excision after NCRT in Peking University Cancer Hospital (Beijing, China) from January 2014 to October 2018. All MRI features obtained before and after NCRT were evaluated by two experienced radiologists, independently and blinded to personal, clinical, and histopathologic information. The post-NCRT yT stage was assigned based on high b value (b = 1000 sec/mm2) DWI with T2-weighted imaging (protocol 1) in the first round and on contrast-enhanced MRI scans (protocol 2) in a second round. The diagnostic accuracies for the differentiation of pathologic stage ypT0-1 from ypT2-4 tumors with the two protocols were compared. Multivariable regression analysis was used to explore the independent predictors of pathologic stage ypT0-1 tumors. Results A total of 328 patients (mean age, 57 years ± 10 [SD]; 227 men; 69%) were enrolled. The area under the receiver operating characteristic curve of the contrast-enhanced MRI protocol in predicting pathologic stage ypT0-1 tumors was 0.81 (95% CI: 0.77, 0.85), which was better than that of the T2-weighted DWI protocol (0.66; 95% CI: 0.60, 0.71; P < .001). Multivariable logistic regression analysis showed that yT stage after NCRT on contrast-enhanced MRI scans was the only independent predictor of pathologic stage ypT0-1 tumors (P < .001). Conclusion Contrast-enhanced MRI provides accurate differentiation of ypT0-1 from ypT2-4 tumors after neoadjuvant chemotherapy and radiation therapy. © RSNA, 2022 Online supplemental material is available for this article. See also the editorial by Zins and Santiago in this issue.

Prediction of Therapeutic Effect to Treatment in Patients with Colorectal Liver Metastases Using Functional Magnetic Resonance Imaging and RECIST Criteria: A Pilot Study in Comparison between Bevacizumab-Containing Chemotherapy and Standard Chemotherapy.

BACKGROUND: Response Evaluation Criteria in Solid Tumors (RECIST) criteria are widely used for evaluating the therapeutic effect of colorectal liver metastases (CRLM) patients, but showed undesirable accuracy. OBJECTIVE: This study aimed to evaluate the value of functional MRI compared with RECIST criteria in predicting the therapeutic effect in CRLM patients receiving neoadjuvant chemotherapy with and without bevacizumab. METHODS: Overall, 137 patients with CRLM who received neoadjuvant chemotherapy followed by hepatic resection between January 2013 and November 2018 were included and were divided into the bevacizumab and non-bevacizumab groups. Apparent diffusion coefficient (ADC) values of pre- and post-treatment diffusion-weighted imaging (DWI) were generated on the whole-volume (ADCmean), periphery (ADCperi), and isocenter (ADCcentral) of the tumor at the maximum slice. Overall survival (OS) and relapse-free survival (RFS) were used as prognostic indicators. RESULTS: Post-treatment ADCmean was significantly associated with OS (p = 0.001) and RFS (p = 0.008) in the bevacizumab group, while RECIST-defined response was found to be only significantly associated with RFS in the non-bevacizumab group (p = 0.042). When categorizing the bevacizumab group by the post-treatment ADCmean cut-off value of 1.15 ×10-3 mm2/s, patients in the ADC response group showed significantly better OS than the non-response group (3-year OS: 91.5% vs. 64.5%, p = 0.001). However, no significant difference was found between RECIST-defined response and non-response in either OS (3-year OS: 60.2% vs. 44.0%, p = 0.104) or RFS (3-year RFS: 26.2% vs. 17.4%, p = 0.129) in the bevacizumab group. CONCLUSIONS: DWI-related parameters such as post-treatment ADCmean could accurately reflect the therapeutic effectiveness and predicting survival in patients treated with bevacizumab, which is superior to the RECIST criteria.

The Conversion of MRI Data With Multiple b-Values into Signature-Like Pictures to Predict Treatment Response for Rectal Cancer.

BACKGROUND: Diffusion weighted imaging (DWI) at multiple b-values has been used to predict the pathological complete response (pCR) to neoadjuvant chemoradiotherapy for locally advanced rectal cancer. Non-Gaussian models fit the signal decay of diffusion by several physical values from different approaches of approximation. PURPOSE: To develop a deep learning method to analyze DWI data scanned at multiple b-values independent on Gaussian or non-Gaussian models and to apply to a rectal cancer neoadjuvant chemoradiotherapy model. STUDY TYPE: Retrospective. POPULATION: A total of 472 participants (age: 56.6 ± 10.5 years; 298 males and 174 females) with locally advanced adenocarcinoma were enrolled and chronologically divided into a training group (n = 200; 42 pCR/158 non-pCR), a validation group (n = 72; 11 pCR/61 non-pCR) and a test group (n = 200; 44 pCR/156 non-pCR). FIELD STRENGTH/SEQUENCE: A 3.0 T MRI scanner. DWI with a single-shot spin echo-planar imaging pulse sequence at 12 b-values (0, 20, 50, 100, 200, 400, 600, 800, 1000, 1200, 1400, and 1600 sec/mm2 ). ASSESSMENT: DWI signals from manually delineated tumor region were converted into a signature-like picture by concatenating all histograms from different b-values. Pathological results (pCR/non-pCR) were used as the ground truth for deep learning. Gaussian and non-Gaussian methods were used for comparison. STATISTICAL TESTS: Analysis of variance for age; Chi-square for gender and pCR/non-pCR; area under the receiver operating characteristic (ROC) curve (AUC); DeLong test for AUC. P < 0.05 for significant difference. RESULTS: The AUC in the test group is 0.924 (95% CI: 0.866-0.983) for the signature-like pictures converted from 35 bins, and it is 0.931 (95% CI: 0.884-0.979) for the signature-like pictures converted from 70 bins, which is significantly (Z = 3.258, P < 0.05) larger than Dapp , the best predictor in non-Gaussian methods with AUC = 0.773 (95% CI: 0.682-0.865). DATA CONCLUSION: The proposed signature-like pictures provide more accurate pretreatment prediction of the response to neoadjuvant chemoradiotherapy than the fitted methods for locally advanced rectal cancer. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 2.

Development and validation of a computed tomography-based immune ecosystem diversity index as an imaging biomarker in non-small cell lung cancer.

OBJECTIVES: To date, there are no data on the noninvasive surrogate of intratumoural immune status that could be prognostic of survival outcomes in non-small cell lung cancer (NSCLC). We aimed to develop and validate the immune ecosystem diversity index (iEDI), an imaging biomarker, to indicate the intratumoural immune status in NSCLC. We further investigated the clinical relevance of the biomarker for survival prediction. METHODS: In this retrospective study, two independent NSCLC cohorts (Resec1, n = 149; Resec2, n = 97) were included to develop and validate the iEDI to classify the intratumoural immune status. Paraffin-embedded resected specimens in Resec1 and Resec2 were stained by immunohistochemistry, and the density percentiles of CD3+, CD4+, and CD8+ T cells to all cells were quantified to estimate intratumoural immune status. Then, EDI features were extracted using preoperative computed tomography to develop an imaging biomarker, called iEDI, to determine the immune status. The prognostic value of iEDI was investigated on NSCLC patients receiving surgical resection (Resec1; Resec2; internal cohort Resec3, n = 419; external cohort Resec4, n = 96; and TCIA cohort Resec5, n = 55). RESULTS: iEDI successfully classified immune status in Resec1 (AUC 0.771, 95% confidence interval [CI] 0.759-0.783; and 0.770 through internal validation) and Resec2 (0.669, 0.647-0.691). Patients with higher iEDI-score had longer overall survival (OS) in Resec3 (unadjusted hazard ratio 0.335, 95%CI 0.206-0.546, p < 0.001), Resec4 (0.199, 0.040-1.000, p < 0.001), and TCIA (0.303, 0.098-0.944, p = 0.001). CONCLUSIONS: iEDI is a non-invasive surrogate of intratumoural immune status and prognostic of OS for NSCLC patients receiving surgical resection. KEY POINTS: • Decoding tumour immune microenvironment enables advanced biomarkers identification. • Immune ecosystem diversity index characterises intratumoural immune status noninvasively. • Immune ecosystem diversity index is prognostic for NSCLC patients.

Correlation Between the Distance to Mesorectal Fascia and Prognosis of cT3 Rectal Cancer: Results of a Multicenter Study From China.

BACKGROUND: The cT3 substage criteria based on extramural depth of tumor invasion in rectal cancer have several limitations. OBJECTIVE: This study proposed that the distance between the deepest tumor invasion and mesorectal fascia on pretherapy MRI can distinguish the prognosis of patients with cT3 rectal cancer. DESIGN: This is a cohort study. SETTING: This study included a prospective, single-center, observational cohort and a retrospective, multicenter, independent validation cohort. PATIENT: Patients who had cT3 rectal cancer with negative mesorectal fascia undergoing neoadjuvant chemoradiotherapy followed by radical surgery were included in 4 centers in China from January 2013 to September 2014. INTERVENTION: Baseline MRI with the distance between the deepest tumor invasion and mesorectal fascia, extramural depth of tumor invasion, and mesorectum thickness were measured. MAIN OUTCOME MEASURES: The cutoff of the distance between the deepest tumor invasion and mesorectal fascia was determined by time-dependent receiver operating characteristic curves, supported by a 5-year progression rate from the prospective cohort, and was then validated in a retrospective cohort. RESULTS: There were 124 and 274 patients included in the prospective and independent validation cohorts. The distance between the deepest tumor invasion and mesorectal fascia was the only predictor for cancer-specific death (HR, 0.1; 95% CI, 0.0-0.7) and was also a significant predictor for distant recurrence (HR, 0.4; 95% CI, 0.2-0.9). No statistically significant difference was observed in prognosis between patients classified as T3a/b and T3c/d. LIMITATIONS: The sample size is relatively small, and the study focused on cT3 rectal cancers with a negative mesorectal fascia. CONCLUSIONS: A cutoff of 7 mm of the distance between the deepest tumor invasion and mesorectal fascia on baseline MRI can distinguish cT3 rectal cancer from a different prognosis. We recommend using the distance between the deepest tumor invasion and mesorectal fascia on baseline MRI for local and systemic risk assessment and providing a tailored schedule of neoadjuvant treatment. See Video Abstract at http://links.lww.com/DCR/B682.CORRELACIÓN ENTRE LA DISTANCIA DE LA FASCIA MESORRECTAL Y EL PRONÓSTICO DEL CÁNCER DE RECTO cT3: RESULTADOS DE UN ESTUDIO MULTICÉNTRICO DE CHINAANTECEDENTES:Los criterios de subestadificación cT3 basados en la profundidad extramural de invasión tumoral en el cáncer de recto tienen varias limitaciones.OBJETIVO:Este estudio propuso que la distancia entre la invasión tumoral más profunda y la fascia mesorrectal en la resonancia magnética preterapia puede distinguir el pronóstico de los pacientes con cT3.DISEÑO:Estudio de cohorte.ENTORNO CLINICO:El estudio incluyó una cohorte observacional, prospectiva, unicéntrica, y una cohorte de validación retrospectiva, multicéntrica e independiente.PACIENTE:Se incluyeron pacientes con cáncer de recto cT3 con fascia mesorrectal negativa sometidos a quimio-radioterapia neoadyuvante seguida de cirugía radical en cuatro centros de China desde enero de 2013 hasta septiembre de 2014.INTERVENCIÓN:Imágenes de resonancia magnética de referencia fueron medidas con la distancia entre la invasión tumoral más profunda y la fascia mesorrectal; la profundidad extramural de la invasión tumoral y el grosor del mesorrecto.PRINCIPALES MEDIDAS DE VALORACION:El límite de la distancia entre la invasión tumoral más profunda y la fascia mesorrectal se determinó mediante curvas características operativas del receptor dependientes del tiempo y se apoyó en la tasa de progresión a 5 años de la cohorte prospectiva, y luego se validó en una cohorte retrospectiva.RESULTADOS:Se incluyeron 124 y 274 pacientes en la cohorte de validación prospectiva e independiente, respectivamente. La distancia entre la invasión tumoral más profunda de la fascia mesorrectal fue el único predictor de muerte específica por cáncer (Hazard ratio: 0.1, 95% CI, 0,0-0,7); y también fue un predictor significativo de recurrencia distante Hazard ratio: 0,4, 95% CI, 0,2-0,9). No se observaron diferencias estadísticamente significativas en el pronóstico entre los pacientes clasificados como T3a/b y T3c/d.LIMITACIONES:El tamaño de la muestra es relativamente pequeño y el estudio se centró en los cánceres de recto cT3 con fascia mesorrectal negativa.CONCLUSIONES:Un límite de 7 mm de distancia entre la invasión tumoral más profunda y la fascia mesorrectal en la resonancia magnética de referencia puede distinguir el cáncer de recto cT3 de diferentes pronósticos. Recomendamos la distancia entre la invasión tumoral más profunda y la fascia mesorrectal en la resonancia magnética de referencia para la evaluación del riesgo local y sistémico, proporcionando un programa personalizado de tratamiento neoadyuvante. Consulte Video Resumen en http://links.lww.com/DCR/B682. (Traducción- Dr. Francisco M. Abarca-Rendon).