Multi-task reconstruction network for synthetic diffusion kurtosis imaging: Predicting neoadjuvant chemoradiotherapy response in locally advanced rectal cancer.

PURPOSE: To assess the feasibility and clinical value of synthetic diffusion kurtosis imaging (DKI) generated from diffusion weighted imaging (DWI) through multi-task reconstruction network (MTR-Net) for tumor response prediction in patients with locally advanced rectal cancer (LARC). METHODS: In this retrospective study, 120 eligible patients with LARC were enrolled and randomly divided into training and testing datasets with a 7:3 ratio. The MTR-Net was developed for reconstructing Dapp and Kapp images from apparent diffusion coefficient (ADC) images. Tumor regions were manually segmented on both true and synthetic DKI images. The synthetic image quality and manual segmentation agreement were quantitatively assessed. The support vector machine (SVM) classifier was used to construct radiomics models based on the true and synthetic DKI images for pathological complete response (pCR) prediction. The prediction performance for the models was evaluated by the receiver operating characteristic (ROC) curve analysis. RESULTS: The mean squared error (MSE), peak signal-to-noise ratio (PSNR), and structural similarity index measure (SSIM) for tumor regions were 0.212, 24.278, and 0.853, respectively, for the synthetic Dapp images and 0.516, 24.883, and 0.804, respectively, for the synthetic Kapp images. The Dice similarity coefficient (DSC), positive predictive value (PPV), sensitivity (SEN), and Hausdorff distance (HD) for the manually segmented tumor regions were 0.786, 0.844, 0.755, and 0.582, respectively. For predicting pCR, the true and synthetic DKI-based radiomics models achieved area under the curve (AUC) values of 0.825 and 0.807 in the testing datasets, respectively. CONCLUSIONS: Generating synthetic DKI images from DWI images using MTR-Net is feasible, and the efficiency of synthetic DKI images in predicting pCR is comparable to that of true DKI images.

A modified diffusion-weighted magnetic resonance imaging-based model from the radiologist's perspective: improved performance in determining the surgical resectability of advanced high-grade serous ovarian cancer.

BACKGROUND: Complete resection of all visible lesions during primary debulking surgery is associated with the most favorable prognosis in patients with advanced high-grade serous ovarian cancer. An accurate preoperative assessment of resectability is pivotal for tailored management. OBJECTIVE: This study aimed to assess the potential value of a modified model that integrates the original 8 radiologic criteria of the Memorial Sloan Kettering Cancer Center model with imaging features of the subcapsular or diaphragm and mesenteric lesions depicted on diffusion-weighted magnetic resonance imaging and growth patterns of all lesions for predicting the resectability of advanced high-grade serous ovarian cancer. STUDY DESIGN: This study included 184 patients with high-grade serous ovarian cancer who underwent preoperative diffusion-weighted magnetic resonance imaging between December 2018 and May 2023 at 2 medical centers. The patient cohort was divided into 3 subsets, namely a study cohort (n=100), an internal validation cohort (n=46), and an external validation cohort (n=38). Preoperative radiologic evaluations were independently conducted by 2 radiologists using both the Memorial Sloan Kettering Cancer Center model and the modified diffusion-weighted magnetic resonance imaging-based model. The morphologic characteristics of the ovarian tumors depicted on magnetic resonance imaging were assessed as either mass-like or infiltrative, and transcriptomic analysis of the primary tumor samples was performed. Univariate and multivariate statistical analyses were performed. RESULTS: In the study cohort, both the scores derived using the Memorial Sloan Kettering Cancer Center (intraclass correlation coefficients of 0.980 and 0.959, respectively; both P<.001) and modified diffusion-weighted magnetic resonance imaging-based models (intraclass correlation coefficients of 0.962 and 0.940, respectively; both P<.001) demonstrated excellent intra- and interobserver agreement. The Memorial Sloan Kettering Cancer Center model (odds ratio, 1.825; 95% confidence interval, 1.390-2.395; P<.001) and the modified diffusion-weighted magnetic resonance imaging-based model (odds ratio, 1.776; 95% confidence interval, 1.410-2.238; P<.001) independently predicted surgical resectability. The modified diffusion-weighted magnetic resonance imaging-based model demonstrated improved predictive performance with an area under the curve of 0.867 in the study cohort and 0.806 and 0.913 in the internal and external validation cohorts, respectively. Using the modified diffusion-weighted magnetic resonance imaging-based model, patients with scores of 0 to 2, 3 to 4, 5 to 6, 7 to 10, and ≥11 achieved complete tumor debulking rates of 90.3%, 66.7%, 53.3%, 11.8%, and 0%, respectively. Most patients with incomplete tumor debulking had infiltrative tumors, and both the Memorial Sloan Kettering Cancer Center and the modified diffusion-weighted magnetic resonance imaging-based models yielded higher scores. The molecular differences between the 2 morphologic subtypes were identified. CONCLUSION: When compared with the Memorial Sloan Kettering Cancer Center model, the modified diffusion-weighted magnetic resonance imaging-based model demonstrated enhanced accuracy in the preoperative prediction of resectability for advanced high-grade serous ovarian cancer. Patients with scores of 0 to 6 were eligible for primary debulking surgery.

Integrated multiomic profiling of breast cancer in the Chinese population reveals patient stratification and therapeutic vulnerabilities.

Molecular profiling guides precision treatment of breast cancer; however, Asian patients are underrepresented in publicly available large-scale studies. We established a comprehensive multiomics cohort of 773 Chinese patients with breast cancer and systematically analyzed their genomic, transcriptomic, proteomic, metabolomic, radiomic and digital pathology characteristics. Here we show that compared to breast cancers in white individuals, Asian individuals had more targetable AKT1 mutations. Integrated analysis revealed a higher proportion of HER2-enriched subtype and correspondingly more frequent ERBB2 amplification and higher HER2 protein abundance in the Chinese HR+HER2+ cohort, stressing anti-HER2 therapy for these individuals. Furthermore, comprehensive metabolomic and proteomic analyses revealed ferroptosis as a potential therapeutic target for basal-like tumors. The integration of clinical, transcriptomic, metabolomic, radiomic and pathological features allowed for efficient stratification of patients into groups with varying recurrence risks. Our study provides a public resource and new insights into the biology and ancestry specificity of breast cancer in the Asian population, offering potential for further precision treatment approaches.

Enhancing brain metastasis prediction in non-small cell lung cancer: a deep learning-based segmentation and CT radiomics-based ensemble learning model.

BACKGROUND: Brain metastasis (BM) is most common in non-small cell lung cancer (NSCLC) patients. This study aims to enhance BM risk prediction within three years for advanced NSCLC patients by using a deep learning-based segmentation and computed tomography (CT) radiomics-based ensemble learning model. METHODS: This retrospective study included 602 stage IIIA-IVB NSCLC patients, 309 BM patients and 293 non-BM patients, from two centers. Patients were divided into a training cohort (N = 376), an internal validation cohort (N = 161) and an external validation cohort (N = 65). Lung tumors were first segmented by using a three-dimensional (3D) deep residual U-Net network. Then, a total of 1106 radiomics features were computed by using pretreatment lung CT images to decode the imaging phenotypes of primary lung cancer. To reduce the dimensionality of the radiomics features, recursive feature elimination configured with the least absolute shrinkage and selection operator (LASSO) regularization method was applied to select the optimal image features after removing the low-variance features. An ensemble learning algorithm of the extreme gradient boosting (XGBoost) classifier was used to train and build a prediction model by fusing radiomics features and clinical features. Finally, Kaplan‒Meier (KM) survival analysis was used to evaluate the prognostic value of the prediction score generated by the radiomics-clinical model. RESULTS: The fused model achieved area under the receiver operating characteristic curve values of 0.91 ± 0.01, 0.89 ± 0.02 and 0.85 ± 0.05 on the training and two validation cohorts, respectively. Through KM survival analysis, the risk score generated by our model achieved a significant prognostic value for BM-free survival (BMFS) and overall survival (OS) in the two cohorts (P < 0.05). CONCLUSIONS: Our results demonstrated that (1) the fusion of radiomics and clinical features can improve the prediction performance in predicting BM risk, (2) the radiomics model generates higher performance than the clinical model, and (3) the radiomics-clinical fusion model has prognostic value in predicting the BMFS and OS of NSCLC patients.

Extent of surgical resection for radiologically subsolid T1N0 invasive lung adenocarcinoma: When is a wedge resection acceptable?

OBJECTIVE: To evaluate whether wedge resection (WR) was appropriate for the patients with peripheral T1 N0 solitary subsolid invasive lung adenocarcinoma. METHODS: Patients with peripheral T1N0 solitary subsolid invasive lung adenocarcinoma who received sublobar resection were retrospectively reviewed. Clinicopathologic characteristics, 5-year recurrence-free survival, and 5-year lung cancer-specific overall survival were analyzed. Cox regression model was used to elucidate risk factors for recurrence. RESULTS: Two hundred fifty-eight patients receiving WR and 1245 patients receiving segmentectomy were included. The mean follow-up time was 36.87 ± 16.21 months. Five-year recurrence-free survival following WR was 96.89% for patients with ground-glass nodule (GGN) ≤2 cm and 0.25< consolidation-to-tumor ratio (CTR) ≤0.5, not statistically different from 100% for those with GGN≤2 cm and CTR ≤0.25 (P = .231). The 5-year recurrence-free survival was 90.12% for patients with GGN between 2 and 3 cm and CTR ≤0.5, significantly lower than that of patients with GGN ≤2 cm and CTR ≤0.25 (P = .046). For patients with GGN≤2 cm and 0.25

Components of the tumor immune microenvironment based on m-IHC correlate with prognosis and subtype of triple-negative breast cancer.

BACKGROUND AND AIM: The spatial distribution and interactions of cells in the tumor immune microenvironment (TIME) might be related to the different responses of triple-negative breast cancer (TNBC) to immunomodulators. The potential of multiplex IHC (m-IHC) in evaluating the TIME has been reported, but the efficacy is insufficient. We aimed to research whether m-IHC results could be used to reflect the TIME, and thus to predict prognosis and complement the TNBC subtyping system. METHODS: The clinical, imaging, and prognosis data for 86 TNBC patients were retrospectively reviewed. CD3, CD4, CD8, Foxp3, PD-L1, and Pan-CK markers were stained by m-IHC. Particular cell spatial distributions and interactions in the TIME were evaluated with the HALO multispectral analysis platform. Then, we calculated the prognostic value of components of the TIME and their correlations with TNBC transcriptomic subtypes and MRI radiomic features reflecting TNBC subtypes. RESULTS: The components of the TIME score were established by m-IHC and demonstrated positive prognostic value for TNBC (p = 0.0047, 0.039, <0.0001 for DMFS, RFS, and OS). The score was calculated from several indicators, including Treg% in the tumor core (TC) or stromal area (SA), PD-L1+ cell% in the SA, CD3 + cell% in the TC, and PD-L1+ /CD8+ cells in the invasive margin and SA. According to the TNBC subtyping system, a few TIME indicators were significantly different in different subtypes and significantly correlated with MRI radiomic features reflecting TNBC subtypes. CONCLUSION: We demonstrated that the m-IHC-based quantitative score and indicators related to the spatial distribution and interactions of cells in the TIME can aid in the accurate diagnosis of TNBC in terms of prognosis and classification.

Development and validation of CT-based radiomics deep learning signatures to predict lymph node metastasis in non-functional pancreatic neuroendocrine tumors: a multicohort study.

Background: Lymph node status is an important factor for the patients with non-functional pancreatic neuroendocrine tumors (NF-PanNETs) with respect to the surgical methods, prognosis, recurrence. Our aim is to develop and validate a combination model based on contrast-enhanced CT images to predict the lymph node metastasis (LNM) in NF-PanNETs. Methods: Retrospective data were gathered for 320 patients with NF-PanNETs who underwent curative pancreatic resection and CT imaging at two institutions (Center 1, n = 236 and Center 2, n = 84) between January 2010 and March 2022. RDPs (Radiomics deep learning signature) were developed based on ten machine-learning techniques. These signatures were integrated with the clinicopathological factors into a nomogram for clinical applications. The evaluation of the model's performance was conducted through the metrics of the area under the curve (AUC). Findings: The RDPs showed excellent performance in both centers with a high AUC for predicting LNM and disease-free survival (DFS) in Center 1 (AUC, 0.88; 95% CI: 0.84-0.92; DFS, p < 0.05) and Center 2 (AUC, 0.91; 95% CI: 0.85-0.97; DFS, p < 0.05). The clinical factors of vascular invasion, perineural invasion, and tumor grade were associated with LNM (p < 0.05). The combination nomogram showed better prediction capability for LNM (AUC, 0.93; 95% CI: 0.89-0.96). Notably, our model maintained a satisfactory predictive ability for tumors at the 2-cm threshold, demonstrating its effectiveness across different tumor sizes in Center 1 (≤2 cm: AUC, 0.90 and >2 cm: AUC, 0.86) and Center 2 (≤2 cm: AUC, 0.93 and >2 cm: AUC, 0.91). Interpretation: Our RDPs may have the potential to preoperatively predict LNM in NF-PanNETs, address the insufficiency of clinical guidelines concerning the 2-cm threshold for tumor lymph node dissection, and provide precise therapeutic strategies. Funding: This work was supported by JSPS KAKENHI Grant Number JP22K20814; the Rare Tumor Research Special Project of the National Natural Science Foundation of China (82141104) and Clinical Research Special Project of Shanghai Municipal Health Commission (202340123).

Contrast-Enhanced Mammography Radiomics Analysis for Preoperative Prediction of Breast Cancer Molecular Subtypes.

BACKGROUND: Predicting breast cancer molecular subtypes can help guide individualised clinical treatment of patients who need the rational preoperative treatment. This study aimed to investigate the efficacy of preoperative prediction of breast cancer molecular subtypes by contrast-enhanced mammography (CEM) radiomic features. METHODS: This retrospective two-centre study included women with breast cancer who underwent CEM preoperatively between August 2016 and May 2022. We included 356 patients with 386 lesions, which were grouped into training (n = 162), internal test (n = 160) and external test sets (n = 64). Radiomics features were extracted from low-energy (LE) images and recombined (RC) images and selected. Three dichotomous tasks were established according to postoperative immunohistochemical results: Luminal vs. non-Luminal, human epidermal growth factor receptor (HER2)-enriched vs. non-HER2-enriched, and triple-negative breast cancer (TNBC) vs. non-TNBC. For each dichotomous task, the LE, RC, and LE+RC radiomics models were built by the support vector machine classifier. The prediction performance of the models was assessed by the area under the receiver operating characteristic curve (AUC). Then, the accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were calculated for the models. DeLong's test was utilised to compare the AUCs. RESULTS: Radiomics models based on CEM are valuable for predicting breast cancer molecular subtypes. The LE+RC model achieved the best performance in the test set. The LE+RC model predicted Luminal, HER2-enriched, and TNBC subtypes with AUCs of 0.93, 0.89, and 0.87 in the internal test set and 0.82, 0.83, and 0.69 in the external test set, respectively. In addition, the LE model performed more satisfactorily than the RC model. CONCLUSION: CEM radiomics features can effectively predict breast cancer molecular subtypes preoperatively, and the LE+RC model has the best predictive performance.

Hybrid deep multi-task learning radiomics approach for predicting EGFR mutation status of non-small cell lung cancer in CT images.

Objective.Epidermal growth factor receptor (EGFR) mutation genotyping plays a pivotal role in targeted therapy for non-small cell lung cancer (NSCLC). We aimed to develop a computed tomography (CT) image-based hybrid deep radiomics model to predict EGFR mutation status in NSCLC and investigate the correlations between deep image and quantitative radiomics features.Approach.First, we retrospectively enrolled 818 patients from our centre and 131 patients from The Cancer Imaging Archive database to establish a training cohort (N= 654), an independent internal validation cohort (N= 164) and an external validation cohort (N= 131). Second, to predict EGFR mutation status, we developed three CT image-based models, namely, a multi-task deep neural network (DNN), a radiomics model and a feature fusion model. Third, we proposed a hybrid loss function to train the DNN model. Finally, to evaluate the model performance, we computed the areas under the receiver operating characteristic curves (AUCs) and decision curve analysis curves of the models.Main results.For the two validation cohorts, the feature fusion model achieved AUC values of 0.86 ± 0.03 and 0.80 ± 0.05, which were significantly higher than those of the single-task DNN and radiomics models (allP< 0.05). There was no significant difference between the feature fusion and the multi-task DNN models (P> 0.8). The binary prediction scores showed excellent prognostic value in predicting disease-free survival (P= 0.02) and overall survival (P< 0.005) for validation cohort 2.Significance.The results demonstrate that (1) the feature fusion and multi-task DNN models achieve significantly higher performance than that of the conventional radiomics and single-task DNN models, (2) the feature fusion model can decode the imaging phenotypes representing NSCLC heterogeneity related to both EGFR mutation and patient NSCLC prognosis, and (3) high correlations exist between some deep image and radiomics features.

Artificial intelligence in breast imaging: Current situation and clinical challenges.

Breast cancer ranks among the most prevalent malignant tumours and is the primary contributor to cancer-related deaths in women. Breast imaging is essential for screening, diagnosis, and therapeutic surveillance. With the increasing demand for precision medicine, the heterogeneous nature of breast cancer makes it necessary to deeply mine and rationally utilize the tremendous amount of breast imaging information. With the rapid advancement of computer science, artificial intelligence (AI) has been noted to have great advantages in processing and mining of image information. Therefore, a growing number of scholars have started to focus on and research the utility of AI in breast imaging. Here, an overview of breast imaging databases and recent advances in AI research are provided, the challenges and problems in this field are discussed, and then constructive advice is further provided for ongoing scientific developments from the perspective of the National Natural Science Foundation of China.

Radiologic Identification of Pathologic Tumor Invasion in Patients With Lung Adenocarcinoma.

Importance: It is currently unclear whether high-resolution computed tomography can preoperatively identify pathologic tumor invasion for ground-glass opacity lung adenocarcinoma. Objectives: To evaluate the diagnostic value of high-resolution computed tomography for identifying pathologic tumor invasion for ground-glass opacity featured lung tumors. Design, Setting, and Participants: This prospective, multicenter diagnostic study enrolled patients with suspicious malignant ground-glass opacity nodules less than or equal to 30 mm from November 2019 to July 2021. Thoracic high-resolution computed tomography was performed, and pathologic tumor invasion (invasive adenocarcinoma vs adenocarcinoma in situ or minimally invasive adenocarcinoma) was estimated before surgery. Pathologic nonadenocarcinoma, benign diseases, or those without surgery were excluded from analyses; 673 patients were recruited, and 620 patients were included in the analysis. Statistical analysis was performed from October 2021 to January 2022. Exposure: Patients were grouped according to pathologic tumor invasion. Main Outcomes and Measures: Primary end point was diagnostic yield for pathologic tumor invasion. Secondary end point was diagnostic value of radiologic parameters. Results: Among 620 patients (442 [71.3%] female; mean [SD] age, 53.5 [12.0] years) with 622 nodules, 287 (46.1%) pure ground-glass opacity nodules and 335 (53.9%) part-solid nodules were analyzed. The median (range) size of nodules was 12.1 (3.8-30.0) mm; 47 adenocarcinomas in situ, 342 minimally invasive adenocarcinomas, and 233 invasive adenocarcinomas were confirmed. Overall, diagnostic accuracy was 83.0% (516 of 622; 95% CI, 79.8%-85.8%), diagnostic sensitivity was 82.4% (192 of 233; 95% CI, 76.9%-87.1%), and diagnostic specificity was 83.3% (324 of 389; 95% CI, 79.2%-86.9%). For tumors less than or equal to 10 mm, 3.6% (8 of 224) were diagnosed as invasive adenocarcinomas. The diagnostic accuracy was 96.0% (215 of 224; 95% CI, 92.5%-98.1%), diagnostic specificity was 97.2% (210 of 216; 95% CI, 94.1%-99.0%); for tumors greater than 20 mm, 6.9% (6 of 87) were diagnosed as adenocarcinomas in situ or minimally invasive adenocarcinomas. The diagnostic accuracy was 93.1% (81 of 87; 95% CI, 85.6%-97.4%) and diagnostic sensitivity was 97.5% (79 of 81; 95% CI, 91.4%-99.7%). For tumors between 10 to 20 mm, the diagnostic accuracy was 70.7% (220 of 311; 95% CI, 65.3%-75.7%), diagnostic sensitivity was 75.0% (108 of 144; 95% CI, 67.1%-81.8%), and diagnostic specificity was 67.1% (112 of 167; 95% CI, 59.4%-74.1%). Tumor size (odds ratio, 1.28; 95% CI, 1.18-1.39) and solid component size (odds ratio, 1.31; 95% CI, 1.22-1.42) could each independently serve as identifiers of pathologic invasive adenocarcinoma. When the cutoff value of solid component size was 6 mm, the diagnostic sensitivity was 84.6% (95% CI, 78.8%-89.4%) and specificity was 82.9% (95% CI, 75.6%-88.7%). Conclusions and relevance: In this diagnostic study, radiologic analysis showed good performance in identifying pathologic tumor invasion for ground-glass opacity-featured lung adenocarcinoma, especially for tumors less than or equal to 10 mm and greater than 20 mm; these results suggest that a solid component size of 6 mm could be clinically applied to distinguish pathologic tumor invasion.

Radiogenomic-based multiomic analysis reveals imaging intratumor heterogeneity phenotypes and therapeutic targets.

Intratumor heterogeneity (ITH) profoundly affects therapeutic responses and clinical outcomes. However, the widespread methods for assessing ITH based on genomic sequencing or pathological slides, which rely on limited tissue samples, may lead to inaccuracies due to potential sampling biases. Using a newly established multicenter breast cancer radio-multiomic dataset (n = 1474) encompassing radiomic features extracted from dynamic contrast-enhanced magnetic resonance images, we formulated a noninvasive radiomics methodology to effectively investigate ITH. Imaging ITH (IITH) was associated with genomic and pathological ITH, predicting poor prognosis independently in breast cancer. Through multiomic analysis, we identified activated oncogenic pathways and metabolic dysregulation in high-IITH tumors. Integrated metabolomic and transcriptomic analyses highlighted ferroptosis as a vulnerability and potential therapeutic target of high-IITH tumors. Collectively, this work emphasizes the superiority of radiomics in capturing ITH. Furthermore, we provide insights into the biological basis of IITH and propose therapeutic targets for breast cancers with elevated IITH.

Using a CT-based scale to evaluate disease extension and the resectability of locally advanced thyroid cancer.

OBJECTIVES: To establish a computed tomography (CT)-based scale to evaluate the resectability of locally advanced thyroid cancer. METHODS: This twin-centre retrospective study included 95 locally advanced thyroid cancer patients from the 1st centre as the training cohort and 31 patients from the 2nd centre as the testing cohort, who were categorised into the resectable and unresectable groups. Three radiologists scored the CT scans of each patient by evaluating the extension to the recurrent laryngeal nerve (RLN), trachea, oesophagus, artery, vein, soft tissue, and larynx. A 14-score scale (including all comprised structures) and a 12-score scale (excluding larynx) were developed. Receiver-operating characteristic (ROC) analysis was used to evaluate the performance of the scales. Stratified fivefold cross-validation and external verification were used to validate the scale. RESULTS: In the training cohort, compromised RLN (p < 0.001), trachea (p = 0.001), oesophagus (p = 0.002), artery (p < 0.001), vein (p = 0.005), and soft tissue (p < 0.001) were predictors for unresectability, while compromised larynx (p = 0.283) was not. The 12-score scale (AUC = 0.882, 95%CI: 0.812-0.952) was not inferior to the 14-score scale (AUC = 0.891, 95%CI: 0.823-0.960). In subgroup analysis, the AUCs of the 12-score scale were 0.826 for treatment-naïve patients and 0.976 for patients with prior surgery. The 12-score scale was further validated with a fivefold cross-validation analysis, with an overall accuracy of 78.9-89.4%. Finally, external validation using the testing cohort showed an AUC of 0.875. CONCLUSIONS: The researchers built a CT-based 12-score scale to evaluate the resectability of locally advanced thyroid cancer. Validation with a larger sample size is required to confirm the efficacy of the scale. CLINICAL RELEVANCE STATEMENT: This 12-score CT scale would help clinicians evaluate the resectability of locally advanced thyroid cancer. KEY POINTS: • The researchers built a 12-score CT scale (including recurrent laryngeal nerve, trachea, oesophagus, artery, vein, and soft tissue) to evaluate the resectability of locally advanced thyroid cancer. • This scale has the potential to help clinicians make treatment plans for locally advanced thyroid cancer.

Development and validation of MRI-based radiomics model to predict recurrence risk in patients with endometrial cancer: a multicenter study.

OBJECTIVES: To develop a fusion model based on clinicopathological factors and MRI radiomics features for the prediction of recurrence risk in patients with endometrial cancer (EC). METHODS: A total of 421 patients with histopathologically proved EC (101 recurrence vs. 320 non-recurrence EC) from four medical centers were included in this retrospective study, and were divided into the training (n = 235), internal validation (n = 102), and external validation (n = 84) cohorts. In total, 1702 radiomics features were respectively extracted from areas with different extensions for each patient. The extreme gradient boosting (XGBoost) classifier was applied to establish the clinicopathological model (CM), radiomics model (RM), and fusion model (FM). The performance of the established models was assessed by the discrimination, calibration, and clinical utility. Kaplan-Meier analysis was conducted to further determine the prognostic value of the models by evaluating the differences in recurrence-free survival (RFS) between the high- and low-risk patients of recurrence. RESULTS: The FMs showed better performance compared with the models based on clinicopathological or radiomics features alone but with a reduced tendency when the peritumoral area (PA) was extended. The FM based on intratumoral area (IA) [FM (IA)] had the optimal performance in predicting the recurrence risk in terms of the ROC, calibration curve, and decision curve analysis. Kaplan-Meier survival curves showed that high-risk patients of recurrence defined by FM (IA) had a worse RFS than low-risk ones of recurrence. CONCLUSIONS: The FM integrating intratumoral radiomics features and clinicopathological factors could be a valuable predictor for the recurrence risk of EC patients. CLINICAL RELEVANCE STATEMENT: An accurate prediction based on our developed FM (IA) for the recurrence risk of EC could facilitate making an individualized therapeutic decision and help avoid under- or over-treatment, therefore improving the prognosis of patients. KEY POINTS: • The fusion model combined clinicopathological factors and radiomics features exhibits the highest performance compared with the clinicopathological model and radiomics model. • Although higher values of area under the curve were observed for all fusion models, the performance tended to decrease with the extension of the peritumoral region. • Identifying patients with different risks of recurrence, the developed models can be used to facilitate individualized management.

Intra- and peritumoral radiomics for predicting malignant BiRADS category 4 breast lesions on contrast-enhanced spectral mammography: a multicenter study.

OBJECTIVE: To construct and test a nomogram based on intra- and peritumoral radiomics and clinical factors for predicting malignant BiRADS 4 lesions on contrast-enhanced spectral mammography. METHODS: A total of 884 patients with BiRADS 4 lesions were enrolled from two centers. For each lesion, five ROIs were defined using the intratumoral region (ITR), peritumoral regions (PTRs) of 5 and 10 mm around the tumor, and ITR plus PTRs of 5 mm and 10 mm. Five radiomics signatures were established by LASSO after selecting features. A nomogram was built using selected signatures and clinical factors by multivariable logistic regression analysis. The performance of the nomogram was assessed with the AUC, decision curve analysis, and calibration curves, and also compared with the radiomics model, clinical model, and radiologists. RESULTS: The nomogram built by three radiomics signatures (constructed from ITR, 5 mm PTR, and ITR + 10 mm PTR) and two clinical factors (age and BiRADS category) showed powerful predictive ability in internal and external test sets with AUCs of 0.907 and 0.904, respectively. The calibration curves, decision curve analysis, showed favorable predictive performance of the nomogram. In addition, radiologists improved the diagnostic performance with the help of nomogram. CONCLUSION: The nomogram established via intratumoral and peritumoral radiomics features and clinical risk factors had the best performance in distinguishing benign and malignant BiRADS 4 lesions, which could help radiologists improve diagnostic capabilities. KEY POINTS: • Radiomics features from peritumoral regions in contrast-enhanced spectral mammography images may provide valuable information for the diagnosis of benign and malignant breast imaging reporting and data system category 4 breast lesions. • The nomogram incorporated intra- and peritumoral radiomics features and clinical variables have good application prospects in assisting clinical decision-makers.

Deep learning-enabled fully automated pipeline system for segmentation and classification of single-mass breast lesions using contrast-enhanced mammography: a prospective, multicentre study.

Background: Breast cancer is the leading cause of cancer-related deaths in women. However, accurate diagnosis of breast cancer using medical images heavily relies on the experience of radiologists. This study aimed to develop an artificial intelligence model that diagnosed single-mass breast lesions on contrast-enhanced mammography (CEM) for assisting the diagnostic workflow. Methods: A total of 1912 women with single-mass breast lesions on CEM images before biopsy or surgery were included from June 2017 to October 2022 at three centres in China. Samples were divided into training and validation sets, internal testing set, pooled external testing set, and prospective testing set. A fully automated pipeline system (FAPS) using RefineNet and the Xception + Pyramid pooling module (PPM) was developed to perform the segmentation and classification of breast lesions. The performances of six radiologists and adjustments in Breast Imaging Reporting and Data System (BI-RADS) category 4 under the FAPS-assisted strategy were explored in pooled external and prospective testing sets. The segmentation performance was assessed using the Dice similarity coefficient (DSC), and the classification was assessed using heatmaps, area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. The radiologists' reading time was recorded for comparison with the FAPS. This trial is registered with China Clinical Trial Registration Centre (ChiCTR2200063444). Findings: The FAPS-based segmentation task achieved DSCs of 0.888 ± 0.101, 0.820 ± 0.148 and 0.837 ± 0.132 in the internal, pooled external and prospective testing sets, respectively. For the classification task, the FAPS achieved AUCs of 0.947 (95% confidence interval [CI]: 0.916-0.978), 0.940 (95% [CI]: 0.894-0.987) and 0.891 (95% [CI]: 0.816-0.945). It outperformed radiologists in terms of classification efficiency based on single lesions (6 s vs 3 min). Moreover, the FAPS-assisted strategy improved the performance of radiologists. BI-RADS category 4 in 12.4% and 13.3% of patients was adjusted in two testing sets with the assistance of FAPS, which may play an important guiding role in the selection of clinical management strategies. Interpretation: The FAPS based on CEM demonstrated the potential for the segmentation and classification of breast lesions, and had good generalisation ability and clinical applicability. Funding: This study was supported by the Taishan Scholar Foundation of Shandong Province of China (tsqn202211378), National Natural Science Foundation of China (82001775), Natural Science Foundation of Shandong Province of China (ZR2021MH120), and Special Fund for Breast Disease Research of Shandong Medical Association (YXH2021ZX055).

Variations of image interpretations of radiologists from different populations in mammography and tomosynthesis with different levels of breast density.

Purpose: This study aims to investigate the diagnostic performances of Australian and Shanghai-based Chinese radiologists in reading full-field digital mammogram (FFDM) and digital breast tomosynthesis (DBT) with different levels of breast density. Approach: Eighty-two Australian radiologists interpreted a 60-case FFDM set, and 29 radiologists also reported a 35-case DBT set. Sixty Shanghai radiologists read the same FFDM set, and 32 radiologists read the DBT set. The diagnostic performances of Australian and Shanghai radiologists were assessed using truth data (cancer cases were biopsy proven) and compared overall in specificity, case sensitivity, lesion sensitivity, receiver operating characteristics (ROC) area under the curve, and jack-knife free-response receiver operating characteristics (JAFROC) figure of merit, and they were stratified by case characteristics using the Mann-Whitney U test. The Spearman rank test was used to explore the association between radiologists' performances and their work experience in mammogram interpretation. Results: There were significantly higher performances of Australian radiologists compared with Shanghai radiologists in low breast density for case sensitivity, lesion sensitivity, ROC, and JAFROC in the FFDM set ( P < 0.0001 ); in high breast density, Shanghai radiologists' performances in lesion sensitivity and JAFROC were also lower than Australian radiologists ( P < 0.0001 ). In the DBT test set, Australian radiologists performed better than Shanghai radiologists in cancer detection in both low and high breast density. The work experience of Australian radiologists was positively linked to their diagnostic performances, whereas this association was not statistically significant in Shanghai radiologists. Conclusion: There were significant variations in reading performances between Australian and Shanghai radiologists in FFDM and DBT across different levels of breast density, lesion types, and lesion sizes. An effective training initiative tailored to suit local readers is essential to enhancing the diagnostic accuracy of Shanghai radiologists.

Prediction of platinum resistance for advanced high-grade serous ovarian carcinoma using MRI-based radiomics nomogram.

OBJECTIVE: This study aimed to explore the value of a radiomics nomogram to identify platinum resistance and predict the progression-free survival (PFS) of patients with advanced high-grade serous ovarian carcinoma (HGSOC). MATERIALS AND METHODS: In this multicenter retrospective study, 301 patients with advanced HGSOC underwent radiomics features extraction from the whole primary tumor on contrast-enhanced T1WI and T2WI. The radiomics features were selected by the support vector machine-based recursive feature elimination method, and then the radiomics signature was generated. Furthermore, a radiomics nomogram was developed using the radiomics signature and clinical characteristics by multivariable logistic regression. The predictive performance was evaluated using receiver operating characteristic analysis. The net reclassification index (NRI), integrated discrimination improvement (IDI), and decision curve analysis (DCA) were used to compare the clinical utility and benefits of different models. RESULTS: Five features significantly correlated with platinum resistance were selected to construct the radiomics model. The radiomics nomogram, combining radiomics signatures with three clinical characteristics (FIGO stage, CA-125, and residual tumor), had a higher area under the curve (AUC) compared with the clinical model alone (AUC: 0.799 vs 0.747), with positive NRI and IDI. The net benefit of the radiomics nomogram is typically higher than clinical-only and radiomics-only models. Kaplan-Meier survival analysis showed that the radiomics nomogram-defined high-risk groups had shorter PFS compared with the low-risk groups in patients with advanced HGSOC. CONCLUSIONS: The radiomics nomogram can identify platinum resistance and predict PFS. It helps make the personalized management of advanced HGSOC. KEY POINTS: • The radiomics-based approach has the potential to identify platinum resistance and can help make the personalized management of advanced HGSOC. • The radiomics-clinical nomogram showed improved performance compared with either of them alone for predicting platinum-resistant HGSOC. • The proposed nomogram performed well in predicting the PFS time of patients with low-risk and high-risk HGSOC in both training and testing cohorts.

Magnetic resonance-based radiomics nomogram for predicting microsatellite instability status in endometrial cancer.

Background: Microsatellite instability (MSI) status is an important indicator for screening patients with endometrial cancer (EC) who have potential Lynch syndrome (LS) and may benefit from immunotherapy. This study aimed to develop a magnetic resonance imaging (MRI)-based radiomics nomogram for the prediction of MSI status in EC. Methods: A total of 296 patients with histopathologically diagnosed EC were enrolled, and their MSI status was determined using immunohistochemical (IHC) analysis. Patients were randomly divided into the training cohort (n=236) and the validation cohort (n=60) at a ratio of 8:2. To predict the MSI status in EC, the tumor radiomics features were extracted from T2-weighted images and contrast-enhanced T1-weighted images, which in turn were selected using one-way analysis of variance (ANOVA) and the least absolute shrinkage and selection operator (LASSO) algorithm to build the radiomics signature (radiomics score; radscore) model. Five clinicopathologic characteristics were used to construct a clinicopathologic model. Finally, the nomogram model combining radscore and clinicopathologic characteristics was constructed. The performance of the three models was evaluated using receiver operating characteristic (ROC), calibration, and decision curve analyses (DCA). Results: Totals of 21 radiomics features and five clinicopathologic characteristics were selected to develop the radscore and clinicopathological models. The radscore and clinicopathologic models achieved an area under the curve (AUC) of 0.752 and 0.600, respectively, in the training cohort; and of 0.723 and 0.615, respectively, in the validation cohort. The radiomics nomogram model showed improved discrimination efficiency compared with the radscore and clinicopathologic models, with an AUC of 0.773 and 0.740 in the training and validation cohorts, respectively. The calibration curve analysis and DCA showed favorable calibration and clinical utility of the nomogram model. Conclusions: The nomogram incorporating MRI-based radiomics features and clinicopathologic characteristics could be a potential tool for the prediction of MSI status in EC.