Tao Yu

This study aimed to improve the accuracy of the diagnosis of lymph node metastasis (LNM) and prediction of patient prognosis in cervical cancer patients using a hybrid model based on MRI and clinical aspects. We retrospectively analyzed routine MR data from 485 patients with pathologically confirmed cervical cancer from January 2014 to June 2021. The data were divided into a training cohort (N = 261), internal cohort (N = 113), and external validation cohort (n = 111). A total of 2194 features were extracted from each ROI from T2WI and CE-T1WI. The clinical model (M1) was built with clinicopathological features including squamous cell carcinoma antigen, MRI-reported LNM, maximal tumor diameter (MTD). The radiomics model (M2) was built with four radiomics features. The hybrid model (M3) was constructed with squamous cell carcinoma antigen, MRI-reported LNM, MTD which consists of M1 and four radiomics features which consist of M2. GBDT algorithms were used to create the scores of M1 (clinical-score, C-score), M2 (radiomic score, R-score), and M3 (hybrid-score, H-score). M3 showed good performance in the training cohort (AUCs, M3 vs. M1 vs. M2, 0.917 vs. 0.830 vs. 0.788), internal validation cohorts (AUCs, M3 vs. M1 vs. M2, 0.872 vs. 0.750 vs. 0.739), and external validation cohort (AUCs, M3 vs. M1 vs. M2, 0.907 vs. 0.811 vs. 0.785). In addition, higher scores were significantly associated with worse disease-free survival (DFS) in the training cohort and the internal validation cohort (C-score, P = 0.001; R-score, P = 0.002; H-score, P = 0.006). Radiomics models can accurately predict LNM status in patients with cervical cancer. The hybrid model, which incorporates clinical and radiomics features, is a novel way to enhance diagnostic performance and predict the prognosis of cervical cancer.

Objectives To develop radiomics signatures from multiparametric magnetic resonance imaging (MRI) scans to detect epidermal growth factor receptor (EGFR) mutations and predict the response to EGFR-tyrosine kinase inhibitors (EGFR-TKIs) in non-small cell lung cancer (NSCLC) patients with brain metastasis (BM). Methods We included 230 NSCLC patients with BM treated at our hospital between January 2017 and December 2021 and 80 patients treated at another hospital between July 2014 and October 2021 to form the primary and external validation cohorts, respectively. All patients underwent contrast-enhanced T1-weighted (T1C) and T2-weighted (T2W) MRI, and radiomics features were extracted from both the tumor active area (TAA) and peritumoral edema area (POA) for each patient. The least absolute shrinkage and selection operator (LASSO) was used to identify the most predictive features. Radiomics signatures (RSs) were constructed using logistic regression analysis. Results For predicting the EGFR mutation status, the created RS-EGFR-TAA and RS-EGFR- POA showed similar performance. By combination of TAA and POA, the multi-region combined RS (RS-EGFR-Com) achieved the highest prediction performance, with AUCs of 0.896, 0.856, and 0.889 in the primary training, internal validation, and external validation cohort, respectively. For predicting response to EGFR-TKI, the multi-region combined RS (RS-TKI-Com) generated the highest AUCs in the primary training (AUC = 0.817), internal validation (AUC = 0.788), and external validation (AUC = 0.808) cohort, respectively. Conclusions Our findings suggested values of multiregional radiomics of BM for predicting EGFR mutations and response to EGFR-TKI. Clinical relevance statement The application of radiomic analysis of multiparametric brain MRI has proven to be a promising tool to stratify which patients can benefit from EGFR-TKI therapy and to facilitate the precise therapeutics of NSCLC patients with brain metastases. Key Points • Multiregional radiomics can improve efficacy in predicting therapeutic response to EGFR-TKI therapy in NSCLC patients with brain metastasis. • The tumor active area (TAA) and peritumoral edema area (POA) may hold complementary information related to the therapeutic response to EGFR-TKI. • The developed multi-region combined radiomics signature achieved the best predictive performance and may be considered as a potential tool for predicting response to EGFR-TKI.

This retrospective study aims to evaluate the use of multi-parametric magnetic resonance imaging (MRI) in predicting lymph-vascular space invasion (LVSI) in early-stage cervical cancer using radiomics methods. A total of 163 patients who underwent contrast-enhanced T1-weighted (CE T1W) and T2-weighted (T2W) MRI scans at 3.0T were enrolled between January 2014 and September 2019. Radiomics features were extracted and selected from the tumoral and peritumoral regions at different dilation distances outside the tumor. Mann–Whitney U test, the least absolute shrinkage and selection operator logistic regression, and logistic regression was applied to select the predictive features and develop the radiomics signature. Univariate analysis was performed on the clinical characteristics. The radiomics nomogram was constructed incorporating the radiomics signature and the selected important clinical predictor. Prediction performance of the radiomics signature, clinical model, and nomogram was evaluated with the area under the curve (AUC), specificity, sensitivity, calibration, and decision curve analysis (DCA). A total of 5 features that were selected from the peritumoral regions with 3- and 7-mm dilation distances outside tumors in CE T1W and T2W MRI, respectively, showed optimal discriminative performance. The radiomics signature comprising the selected features was significantly associated with the LVSI status. The radiomics nomogram integrating the radiomics signature and degree of cellular differentiation exhibited the best predictability with AUCs of 0.771 (specificity (SPE) = 0.831 and sensitivity (SEN) = 0.581) in the training cohort and 0.788 (SPE = 0.727, SEN = 0.773) in the validation cohort. DCA confirmed the clinical usefulness of our model. Our results illustrate that the radiomics nomogram based on MRI features from peritumoral regions and the degree of cellular differentiation can be used as a noninvasive tool for predicting LVSI in cervical cancer.

Purpose This study aimed to investigate the efficacy of digital mammography (DM), digital breast tomosynthesis (DBT), diffusion-weighted (DW) and dynamic contrast-enhanced (DCE) MRI separately and combined in the prediction of molecular subtypes of breast cancer. Methods A total of 241 patients were enrolled and underwent breast MD, DBT, DW and DCE scans. Radiomics features were calculated from intra- and peritumoral regions, and selected with least absolute shrinkage and selection operator (LASSO) regression to develop radiomics signatures (RSs). Prediction performance of intra- and peritumoral regions in the four modalities were evaluated and compared with area under the receiver-operating characteristic (ROC) curve (AUC), specificity and sensitivity as comparison metrics. Results The RSs derived from combined intra- and peritumoral regions improved prediction AUCs compared with those from intra- or peritumoral regions alone. DM plus DBT generated better AUCs than the DW plus DCE on predicting Luminal A and Luminal B in the training (Luminal A: 0.859 and 0.805; Luminal B: 0.773 and 0.747) and validation (Luminal A: 0.906 and 0.853; Luminal B: 0.807 and 0.784) cohort. For the prediction of HER2-enriched and TN, the DW plus DCE yielded better AUCs than the DM plus DBT in the training (HER2-enriched: 0.954 and 0.857; TN: 0.877 and 0.802) and validation (HER2-enriched: 0.974 and 0.907; TN: 0.938 and 0.874) cohort. Conclusions Peritumoral regions can provide complementary information to intratumoral regions for the prediction of molecular subtypes. Compared with MRI, the mammography showed higher AUCs for the prediction of Luminal A and B, but lower AUCs for the prediction of HER2-enriched and TN.

Purpose: Accumulated literature has reported the comparative efficacy of the sinus tarsi approach (STA) and the extensile lateral approach (ELA) for the treatment of calcaneal fractures (CFs). However, the best alternative treatment for CF is still inconsistent. Herein, the present systematic review of overlapping meta-analyses aims to achieve an evident conclusion by performing a comprehensive reanalysis of previous meta-analyses regarding the comparison of the STA and the ELA. Methods: We searched several databases, including Pubmed, Medline, Embase, the Cochrane Library, SpringerLink, Clinical Trials.gov , OVID, and CNKI for the meta-analyses comparing the STA and the ELA for the treatment of CF. All related meta-analyses of randomized controlled trials and cohort studies were included. Two researchers independently assessed the quality of the articles and extracted the data. The Jadad decision algorithm was used to evaluate the evidence of the articles. Results: Ultimately, five meta-analyses were included in the present study. The Assessment of Multiple Systematic Reviews scores of these articles ranged from 5 to 9 with a median of 7. The analysis of best quality, Bai 2018, was selected based on the Jadad algorithm. In this article, the significant differences were found in wound complications and operating time, recovery of Böhler’s angle, the American Orthopaedic Foot and Ankle Society scores, and the visual analog scale. Conclusion: The clinical relevance of the present study is that both the STA and the ELA are effective in alleviating pain and improving functionality in the treatment of CF. However, due to a shorter operation duration and lower complication rates, the STA was indicated to be a superior alternative for CF treatment.

This study aims to build deep learning-based radiomic methods in differentiating vessel invasion from non-vessel invasion in cervical cancer with multi-parametric MRI data. A set of 1,070 dynamic T1 contrast-enhanced (DCE-T1) and 986 T2 weighted imaging (T2WI) MRI images from 167 early-stage cervical cancer patients (January 2014 - August 2018) were used to train and validate deep learning models. Predictive performances were evaluated using receiver operating characteristic (ROC) curve and confusion matrix analysis, with the DCE-T1 showing more discriminative results than T2WI MRI. By adopting an attention ensemble learning strategy that integrates both MRI sequences, the highest average area was obtained under the ROC curve (AUC) of 0.911 (Sensitivity = 0.881 and Specificity = 0.752). The superior performances in this study, when compared to existing radiomic methods, indicate that a wealth of deep learning-based radiomics could be developed to aid radiologists in preoperatively predicting vessel invasion in cervical cancer patients.

Purpose: Metastasis is one of the most important causes of treatment failure in nasopharyngeal carcinoma (NPC). In T4 or N2-3 patients at high-risk of metastasis, concurrent chemoradiotherapy (CCRT) is inadequate and additional induction chemotherapy (IC) is controversial. There is a critical need to develop a better patient stratification to efficiently identify patients at high-risk of metastasis for additional IC. Recently, Sequestosome 1 (SQSTM1)/p62, an autophagy adaptor protein, was identified as one of the metastasis-related proteins in NPC. However, the mechanism by which SQSTM1 is involved in NPC metastasis was not investigated. Experimental Design: The effect of SQSTM1 on cell migration and invasion was examined in vitro and in vivo. SQSTM1 expression was analyzed in clinical NPC samples using IHC. Luciferase reporter analyses were conducted to identify the effects of SQSTM1 on NF-κB transcriptional activity. A prediction bio-model was constructed by Cox analysis. Retrospective and prospective randomized clinical data were adopted to build and test the model, respectively. Results: SQSTM1 mediated epithelial to mesenchymal transition (EMT) through the NF-κB pathway to promote NPC metastasis. Inhibiting SQSTM1 enhanced sensitivity to cisplatin in NPC cells. In NPC patients, high SQSTM1 expression was associated with increased risk of distant metastasis. Furthermore, we propose a prognostic bio-model based on SQSTM1 and N-stage to predict NPC metastasis. Most importantly, our prospective randomized study suggested that IC is beneficial for NPC patients with high metastasis risk. Conclusions: The prognostic bio-model identifies NPC patients at high-risk of metastasis for additional IC. Clin Cancer Res; 24(3); 648–58. ©2017 AACR.