Machine learning-based radiomics for predicting outcomes in cervical cancer patients undergoing concurrent chemoradiotherapy.

PURPOSES: To investigate the value of machine learning-based radiomics for predicting disease-free survival (DFS) and overall survival (OS) undergoing concurrent chemoradiotherapy (CCRT) for patients with locally advanced cervical cancer (LACC). MATERIALS AND METHODS: In this multicentre study, 700 patients with IB2-IVA cervical cancer who underwent CCRT with ongoing follow-up were retrospectively analyzed. Three-dimensional radiomics features of primary lesions and its surrounding 5 mm region in T2WI sequences were collected. Six machine learning methods were used to construct the optimal radiomics model for accurate prediction of DFS and OS after CCRT in LACC patients. Eventually, TCGA and GEO databases were used to explore the mechanisms of radiomics in predicting the progression and survival of cervical cancer. This study adhered CLEAR for reporting and its quality was assessed using RQS and METRICS. RESULTS: In the prediction of DFS, the RSF model combined tumor and peritumor radiomics demonstrated the best predictive efficacy, with the AUC for predicting 1-year, 3-year, and 5-year DFS in the training, validation, and test sets of 0.986, 0.989, 0.990, and 0.884, 0.838, 0.823, and 0.829, 0.809, 0.841, respectively. In the prediction of OS, the GBM model best performer, with AUC of 0.999, 0.995, 0.978, and 0.981, 0.975, 0.837, and 0.904, 0.860, 0.905. Differential genes in TCGA and GEO suggest that the prediction of radiomics model may be associated with KDELR2 and HK2. CONCLUSION: Machine learning-based radiomics models help to predict DFS and OS after CCRT in LACC patients, and the combination of tumor and peritumor information has higher predictive efficacy, which can provide a reliable basis for therapeutic decision-making in cervical cancer patients.

Machine Learning-Based Models for Assessing Postoperative Risk Factors in Patients with Cervical Cancer.

RATIONALE AND OBJECTIVES: To investigate the value of machine learning-based radiomics, intravoxel incoherent motion (IVIM) diffusion-weighted imaging and its combined model in predicting the postoperative risk factors of parametrial infiltration (PI), lymph node metastasis (LNM), deep muscle invasion (DMI), lymph-vascular space invasion (LVSI), pathological type (PT), differentiation degree (DD), and Ki-67 expression level in patients with cervical cancer. MATERIALS AND METHODS: The data of 180 patients with cervical cancer were retrospectively analyzed and randomized 2:1 into a training and validation group. The IVIM-DWI and radiomics parameters of primary lesions were measured in all patients. Seven machine learning methods were used to calculate the optimal radiomics score (Rad-score), which was combined with IVIM-DWI and clinical parameters to construct nomograms for predicting the risk factors of cervical cancer, with internal and external validation. RESULTS: The diagnostic efficacy of the nomograms based on clinical and imaging parameters was significantly better than MRI assessment alone. The area under the curve (AUC) of nomograms and MRI for the assessment of PI, LNM, and DMI were 0.981 vs 0.868, 0.848 vs 0.639, and 0.896 vs 0.780, respectively. Nomograms also performed well in the assessment of LVSI, PT, DD, and Ki-67 expression levels, with AUC of 0.796, 0.854, 0.806, 0.839 and 0.840, 0.856, 0.810, 0.832 in the training and validation groups. CONCLUSION: Machine learning-based nomograms can serve as a useful tool for assessing postoperative risk factors in patients with cervical cancer.

The effects of Shexiang Baoxin pills combined with intracoronary injection of nicorandil on myocardial blood perfusion and short-term prognosis for STEMI patients after PPCI / 药学实践杂志

Objective To observe the effects of Shexiang Baoxin pill combined with intracoronary injection of nicorandil on myocardial perfusion and short-term prognosis after primary percutaneous coronary intervention in patients with ST-segment elevation myocardial infarction. Methods 151 patients with acute myocardial infarction after PPCI were enrolled in this study. Those patients were admitted to our hospital during January 2017 to January 2018. According to the numerical randomization method, 51 patients were selected as routine treatment group (group A), 50 patients with intracoronary injection of nicorandil (group B) and 50 patients received intracoronary injection of nicorandil plus oral Shexiang Baoxin pills (group C). Intra-operative corrected TIMI frame count (cTFC), postoperative TIMI grade 3 blood flow ratio, 2-hour ECG ST segment fallback >50% index, the incidence of major adverse cardiovascular events (MACE) during hospitalization and the incidence of angina and MACE within 3 months after surgery were evaluated. Results cTFC, 2 hours postoperative ECG ST segment fall >50% index in group B and C were better than group A (P<0.05). The results from group C were better than group B. Group C exhibited better results than group B and C in post-operative angina pectoris 3 months after surgery (P<0.05). Conclusion Shexiang Baoxin pills combined with intra-coronary injection of nicorandil can improve myocardial perfusion and short-term prognosis after primary percutaneous coronary intervention in patients with ST-segment elevation myocardial infarction

Analysis of the pedigree of a child with interstitial lung disease caused by a novel mutation of p. K114N in SFTPC gene / 中国小儿急救医学

Objective:To analyze the clinical and chest CT features in a family with interstitial lung disease(ILD), and assess the probable causative gene mutations for the family.Methods:In order to identify the etiology of the proband′s ILD, the pedigree was investigated.The clinical data of five proband′s pedigree members were collected, and the chest HRCT examination was performed on four proband′s pedigree members with respiratory symptoms.The human whole exon sequencing was performed on the proband′s blood samples, then its deleterious effects were assessed.Subsequently, the strong pathogenic mutation was validated by Sanger sequencing.Results:According to the family survey, there were five patients with ILD in the family, including three males and two females.One of them died.The surfactant protein C(SFTPC)gene(exon4, c.342G>T, p.K114N)was found in all four surviving patients.The mutation was considered to be originated from the father of the proband, and the pathogenic mutation was considered, which was not included in the databases and was a noval mutation.In addition, the clinical manifestations of different patients in the family were significantly different.Conclusion:The novel mutation of p. k114n in SFTPC gene can lead to ILD in children, and the mutation has incomplete exons in family members.Chest CT and whole exon sequencing play an important role in the diagnosis of ILD in children.