A two-center study of a combined nomogram based on mammography and MRI to predict ALN metastasis in breast cancer.

OBJECTIVES: To develop and validate a predictive method for axillary lymph node (ALN) metastasis of breast cancer by using radiomics based on mammography and MRI. MATERIALS AND METHODS: A retrospective analysis of 492 women from center 1 (The affiliated Hospital of Qingdao University) and center 2 (Yantai Yuhuangding Hospital) with primary breast cancer from August 2013 to May 2021 was carried out. The radscore was calculated using the features screened based on preoperative mammography and MRI from the training cohort of Center 1 (n = 231), then tested in the validation cohort (n = 99), an internal test cohort (n = 90) from Center 1, and an external test cohort (n = 72) from Center 2. Univariate and multivariate analyses were used to screen for the clinical and radiological characteristics most associated with ALN metastasis. A combined nomogram was established in combination with radscore that predicted the clinicopathological and radiological characteristics. Calibration curves were used to test the effectiveness of the combined nomogram. The receiver operating characteristic (ROC) curve was used to evaluate the performance of the combined nomogram and then compare with the clinical and radiomic models. The decision curve analysis (DCA) value was used to evaluate the combined nomogram for clinical applications. RESULTS: The constructed combined nomogram incorporating the radscore and MRI-reported ALN metastasis status exhibited good calibration and outperformed the radiomics signatures in predicting ALN metastasis (area under the curve [AUC]: 0.886 vs. 0.846 in the training cohort; 0.826 vs. 0.762 in the validation cohort; 0.925 vs. 0.899 in the internal test cohort; and 0.902 vs. 0.793 in the external test cohort). The combination nomogram achieved a higher AUC in the training cohort (0.886 vs. 0.786) and the internal test cohort (0.925 vs. 0.780) and similar AUCs in the validation (0.826 vs. 0.811) and external test (0.902 vs. 0.837) cohorts than the clinical model. CONCLUSION: A combined nomogram based on mammography and MRI can be used for preoperative prediction of ALN metastasis in primary breast cancer.

Improved deep learning for automatic localisation and segmentation of rectal cancer on T2-weighted MRI.

INTRODUCTION: The automatic segmentation approaches of rectal cancer from magnetic resonance imaging (MRI) are very valuable to relieve physicians from heavy workloads and enhance working efficiency. This study aimed to compare the segmentation accuracy of a proposed model with the other three models and the inter-observer consistency. METHODS: A total of 65 patients with rectal cancer who underwent MRI examination were enrolled in our cohort and were randomly divided into a training cohort (n = 45) and a validation cohort (n = 20). Two experienced radiologists independently segmented rectal cancer lesions. A novel segmentation model (AttSEResUNet) was trained on T2WI based on ResUNet and attention mechanisms. The segmentation performance of the AttSEResUNet, U-Net, ResUNet and U-Net with Attention Gate (AttUNet) was compared, using Dice similarity coefficient (DSC), Hausdorff distance (HD), mean distance to agreement (MDA) and Jaccard index. The segmentation variability of automatic segmentation models and inter-observer was also evaluated. RESULTS: The AttSEResUNet with post-processing showed perfect lesion recognition rate (100%) and false recognition rate (0), and its evaluation metrics outperformed other three models for two independent readers (observer 1: DSC = 0.839 ± 0.112, HD = 9.55 ± 6.68, MDA = 0.556 ± 0.722, Jaccard index = 0.736 ± 0.150; observer 2: DSC = 0.856 ± 0.099, HD = 11.0 ± 10.1, MDA = 0.789 ± 1.07, Jaccard index = 0.673 ± 0.130). The segmentation performance of AttSEResUNet was comparable and similar to manual variability (DSC = 0.857 ± 0.115, HD = 10.0 ± 10.0, MDA = 0.704 ± 1.17, Jaccard index = 0.666 ± 0.139). CONCLUSION: Comparing with other three models, the proposed AttSEResUNet model was demonstrated as a more accurate model for contouring the rectal tumours in axial T2WI images, whose variability was similar to that of inter-observer.

Charge-Localized Retention and Long-Term Memory Enabled by Cooperating Sterically Confined Molecular Crystallization with Spiro[fluorene-9,9'-xanthene]-Based Csp3-Hindrance.

Charge localization of memory materials plays a crucial role in the endurance and retention ability of organic nonvolatile memory, which is completely opposite from the charge delocalization of high-mobility materials. However, charge transfer of both though-space and through-bond based on molecular design principles still faces challenges. Herein, a nonplanar wide-bandgap semiconductor with Csp3-hindrance (DOCH3-DDPA-SFX) has been designed and synthesized. An effective crystallization effect of self-assembled two-dimensional nanosheets on charge trapping dynamics and kinetics is visualized by Kelvin probe force microscopy (KPFM). The trapped charges are localized completely on a single nanosheet, which has better charge trapping and retention properties than an amorphous film. Meanwhile, crystallization also greatly improves structure stability. Combining DFT theoretical calculations, the mechanisms of localization and long-term retention are discussed. The steric crystallization effects on the charge localization will guide the effective design of single-component semiconducting charge-memory materials by molecular assembly and aggregate control for high-performance organic memory.

MRI-Based Kinetic Heterogeneity Evaluation in the Accurate Access of Axillary Lymph Node Status in Breast Cancer Using a Hybrid CNN-RNN Model.

BACKGROUND: Accurate evaluation of the axillary lymph node (ALN) status is needed for determining the treatment protocol for breast cancer (BC). The value of magnetic resonance imaging (MRI)-based tumor heterogeneity in assessing ALN metastasis in BC is unclear. PURPOSE: To assess the value of deep learning (DL)-derived kinetic heterogeneity parameters based on BC dynamic contrast-enhanced (DCE)-MRI to infer the ALN status. STUDY TYPE: Retrospective. SUBJECTS: 1256/539/153/115 patients in the training cohort, internal validation cohort, and external validation cohorts I and II, respectively. FIELD STRENGTH/SEQUENCE: 1.5 T/3.0 T, non-contrast T1-weighted spin-echo sequence imaging (T1WI), DCE-T1WI, and diffusion-weighted imaging. ASSESSMENT: Clinical pathological and MRI semantic features were obtained by reviewing histopathology and MRI reports. The segmentation of the tumor lesion on the first phase of T1WI DCE-MRI images was applied to other phases after registration. A DL architecture termed convolutional recurrent neural network (ConvRNN) was developed to generate the KHimage (kinetic heterogeneity of DCE-MRI image) score that indicated the ALN status in patients with BC. The model was trained and optimized on training and internal validation cohorts, tested on two external validation cohorts. We compared ConvRNN model with other 10 models and the subgroup analyses of tumor size, magnetic field strength, and molecular subtype were also evaluated. STATISTICAL TESTS: Chi-squared, Fisher's exact, Student's t, Mann-Whitney U tests, and receiver operating characteristics (ROC) analysis were performed. P < 0.05 was considered significant. RESULTS: The ConvRNN model achieved area under the curve (AUC) of 0.802 in the internal validation cohort and 0.785-0.806 in the external validation cohorts. The ConvRNN model could well evaluate the ALN status of the four molecular subtypes (AUC = 0.685-0.868). The patients with larger tumor sizes (>5 cm) were more susceptible to ALN metastasis with KHimage scores of 0.527-0.827. DATA CONCLUSION: A ConvRNN model outperformed traditional models for determining the ALN status in patients with BC. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY: Stage 2.

Prediction of Disease-Free Survival in Breast Cancer using Deep Learning with Ultrasound and Mammography: A Multicenter Study.

BACKGROUND: Breast cancer is a leading cause of cancer morbility and mortality in women. The possibility of overtreatment or inappropriate treatment exists, and methods for evaluating prognosis need to be improved. MATERIALS AND METHODS: Patients (from January 2013 to December 2018) were recruited and divided into a training group and a testing group. All patients were followed for more than 3 years. Patients were divided into a disease-free group and a recurrence group based on follow up results at 3 years. Ultrasound (US) and mammography (MG) images were collected to establish deep learning models (DLMs) using ResNet50. Clinical data, MG, and US characteristics were collected to select independent prognostic factors using a cox proportional hazards model to establish a clinical model. DLM and independent prognostic factors were combined to establish a combined model. RESULTS: In total, 1242 patients were included. Independent prognostic factors included age, neoadjuvant chemotherapy, HER2, orientation, blood flow, dubious calcification, and size. We established 5 models: the US DLM, MG DLM, US + MG DLM, clinical and combined model. The combined model using US images, MG images, and pathological, clinical, and radiographic characteristics had the highest predictive performance (AUC = 0.882 in the training group, AUC = 0.739 in the testing group). CONCLUSION: DLMs based on the combination of US, MG, and clinical data have potential as predictive tools for breast cancer prognosis.

Fe-doping and oxygen vacancy achieved by electrochemical activation and precipitation/dissolution equilibrium in NiOOH for oxygen evolution reaction.

The poor conductivities and instabilities of accessible nickel oxyhydroxides hinder their use as oxygen evolution reaction (OER) electrocatalysts. Herein, we constructed Fe-NiOOH-OV-600, an Fe-doped nickel oxide hydroxide with abundant oxygen vacancies supported on nickel foam (NF), using a hydrothermal method and an electrochemical activation strategy involving 600 cycles of cyclic voltammetry, assisted by the precipitation/dissolution equilibrium of ferrous sulfide (FeS) in the electrolyte. This two-step method endows the catalyst with abundant Fe-containing active sites while maintaining the ordered structure of nickel oxide hydroxide (NiOOH). Characterization and density functional theory (DFT) calculations revealed that synergy between trace amounts of the Fe dopant and the oxygen vacancies not only promotes the generation of reconstructed active layers but also optimizes the electronic structure and adsorption capacity of the active sites. Consequently, the as-prepared Fe-NiOOH-OV-600 delivered large current densities of 100 and 1000 mA cm-2 for the OER at overpotentials of only 253 and 333 mV in 1 mol/L KOH. Moreover, the catalyst is stable for at least 100 h at 500 mA cm-2. This work provides insight into the design of efficient transition-metal-based electrocatalysts for the OER.

Evaluation of Multiparametric MRI Radiomics-Based Nomogram in Prediction of Response to Neoadjuvant Chemotherapy in Breast Cancer: A Two-Center study.

INTRODUCTION: This study evaluated the performance of primary foci of breast cancer on multiparametric magnetic resonance imaging (MRI) contributing to establish and validate radiomics-based nomograms for predicting the different pathological outcome of breast cancer patients after neoadjuvant chemotherapy (NAC). MATERIALS AND METHODS: Retrospectively collected 387 patients with locally advanced breast cancer, all treated with NAC and received breast dynamic contrast-enhanced MRI (DCE-MRI) before NAC. Radiomics signatures were extracted from region of interest (ROI) on multiparametric MRI to build rad score. Clinical-pathologic data and radiological features established the clinical model. The comprehensive model featured rad-score, predictive clinical-pathologic data and radiological features, which was ultimately displayed as a nomogram. Patients were grouped in 2 different ways in accordance with the Miller-Payne (MP) grading of surgical specimens. The first grouping method: 181 patients with pathological reaction grades Ⅳ∼Ⅴ were included in the significant remission group, while 206 patients with pathological reaction grades Ⅰ∼Ⅲ were included in the nonsignificant remission group. The second grouping method: 117 patients with pathological complete response (pCR) were assigned to the pCR group, and 270 patients who failed to meet pCR were assigned to in the non-pCR group. Two combined nomograms are created from 2 grouped data for predicting different pathological responses to NAC. The area under the curves (AUC) of the receiver operating characteristic curves (ROC) were used to evaluate the performance of each model. While decision curve analysis (DCA) and calibration curves were used for estimating the clinical application value of the nomogram. RESULTS: Two combined nomograms embodying rad score and clinical-pathologic data outperformed, showing good calibrations for predicting response to NAC. The combined nomogram predicting pCR showed the best performance with the AUC values of 0.97, 0.90 and 0.86 in the training, testing, and external validation cohorts respectively. The AUC values of another combined nomogram predicting significant remission: 0.98, 0.88 0.80 in the training, testing and external validation cohorts. DCA showed the comprehensive model nomogram obtained the most clinical benefit. CONCLUSIONS: The combined nomogram could preoperatively predict significant remission or even pCR to NAC in breast cancer based on multiparametric MRI and clinical-pathologic data.

Preoperative Evaluation of Gd-EOB-DTPA-Enhanced MRI Radiomics-Based Nomogram in Small Solitary Hepatocellular Carcinoma (≤3 cm) With Microvascular Invasion: A Two-Center Study.

BACKGROUND: Preoperative evaluation of microvascular invasion (MVI) in small solitary hepatocellular carcinoma (HCC; maximum lesion diameter ≤ 3 cm) is important for treatment decisions. PURPOSE: To apply gadolinium ethoxybenzyl diethylenetriamine pentaacetic acid (Gd-EOB-DTPA)-enhanced MRI to develop and validate a nomogram for preoperative evaluation of MVI in small solitary HCC and to compare the effectiveness of radiomics evaluation models based on different volumes of interest (VOIs). STUDY TYPE: Retrospective. POPULATION: A total of 196 patients include 62 MVI-positive and 134 MVI-negative patients were enrolled (training cohort, n = 105; testing cohort, n = 45; external validation cohort, n = 46). FIELD STRENGTH/SEQUENCE: 3.0 T, fat suppressed fast-spin-echo T2-weighted and Gd-EOB-DTPA-enhanced T1-weighted magnetization-prepared rapid gradient-echo sequences. ASSESSMENT: Radiomics features were extracted on T2-weighted, arterial phase (AP), and hepatobiliary phase (HBP) images from different VOIs (VOIintratumor and VOIintratumor+peritumor ) and filtered by the least absolute shrinkage selection operator (LASSO) regression. From VOIintratumor and VOIintratumor+peritumor , eight radiomics models were constructed based on three MRI sequences (T2-weighted, AP, and HBP) and fused sequences (combined of three sequences). Nomograms were constructed of a clinical-radiological (CR) model and a clinical-radiological-radiomics (CRR) model. STATISTICAL TESTS: One-way analysis of variance, independent t-test, Chi-square test or Fisher's exact test, Wilcoxon rank-sum test, LASSO, logistic regression analysis, area under the curve (AUC), nomograms, decision curve, net reclassification improvement (NRI), integrated discrimination improvement (IDI) analyses, and DeLong test. RESULTS: Among eight radiomics models, the fused sequences-based VOIintratumor+peritumor radiomics model showed the best performance. The CRR model containing the best performance radiomics model and CR model with the AUC values were 0.934, 0.889, and 0.875, respectively. NRI and IDI analyses showed that the CRR model improved evaluation efficacy over the CR model for all three cohorts (all P-value <0.05). DATA CONCLUSION: The CRR model nomogram could preoperatively evaluate MVI in small solitary HCC. The radiomics model based on VOIintratumor+peritumor might achieve better evaluation results. EVIDENCE LEVEL: 4 TECHNICAL EFFICACY STAGE: 2.