Objective:To explore the application of machine learning (ML) models based on radiomics and dosiomics to the assessment of hematologic toxicity (HT) in patients with locally advanced cervical cancer, and to preliminarily explore the comprehensive application of multi-omics features.Methods:A retrospective study was conducted on the clinical data, planning computed tomography (CT) images, and dose files of 205 patients with locally advanced cervical cancer who received concurrent chemoradiotherapy at the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, from January 2022 to June 2023. Patients were categorized according to the severity of HT. Radiomics and dosiomics features were extracted from the same regions of interest (ROIs), followed by feature selection utilizing a random forest algorithm. Then, radiomics, dosiomics, and hybrid models were established based on extreme gradient boosting (XGBoost). The classification performance of these models was assessed by calculating their sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).Results:The radiomics model yielded sensitivity, specificity, and AUC of 0.42, 0.86, and 0.78, respectively. The dosiomics model exhibited sensitivity, specificity, and AUC of 0.50, 0.90, and 0.74, respectively. In contrast, the hybrid model achieved sensitivity, specificity, and AUC of 0.50, 0.83, and 0.83, respectively. These findings suggest that the hybrid model possessed an enhanced classification capability compared to the individual radiomics and dosiomics models.Conclusions:It is feasible to use ML models based on radiomics and dosiomics to conduct the classification and prediction of HT in patients with locally advanced cervical cancer treated with concurrent chemoradiotherapy. Furthermore, integrating both radiomics features and dosiomics features can improve the classification performance of relevant prediction models, thus holding application potentials to optimize treatment strategies for patients with locally advanced cervical cancer.

Objective:To explore the application of machine learning (ML) models based on radiomics and dosiomics to the assessment of hematologic toxicity (HT) in patients with locally advanced cervical cancer, and to preliminarily explore the comprehensive application of multi-omics features.Methods:A retrospective study was conducted on the clinical data, planning computed tomography (CT) images, and dose files of 205 patients with locally advanced cervical cancer who received concurrent chemoradiotherapy at the Affiliated Cancer Hospital of Xiangya School of Medicine, Central South University, from January 2022 to June 2023. Patients were categorized according to the severity of HT. Radiomics and dosiomics features were extracted from the same regions of interest (ROIs), followed by feature selection utilizing a random forest algorithm. Then, radiomics, dosiomics, and hybrid models were established based on extreme gradient boosting (XGBoost). The classification performance of these models was assessed by calculating their sensitivity, specificity, and area under the receiver operating characteristic curve (AUC).Results:The radiomics model yielded sensitivity, specificity, and AUC of 0.42, 0.86, and 0.78, respectively. The dosiomics model exhibited sensitivity, specificity, and AUC of 0.50, 0.90, and 0.74, respectively. In contrast, the hybrid model achieved sensitivity, specificity, and AUC of 0.50, 0.83, and 0.83, respectively. These findings suggest that the hybrid model possessed an enhanced classification capability compared to the individual radiomics and dosiomics models.Conclusions:It is feasible to use ML models based on radiomics and dosiomics to conduct the classification and prediction of HT in patients with locally advanced cervical cancer treated with concurrent chemoradiotherapy. Furthermore, integrating both radiomics features and dosiomics features can improve the classification performance of relevant prediction models, thus holding application potentials to optimize treatment strategies for patients with locally advanced cervical cancer.

Clinical trials are an important method for evaluating the safety and efficacy of in vitro diagnostic reagents, and are a key basis for product registration review and approval. In order to strengthen the management of clinical trials of in vitro diagnostic reagents, the National Medical Products Administration and relevant departments have formulated a series of regulations at the regulatory level, and require applicants and clinical trial institutions to establish a quality management system for clinical trials of in vitro diagnostic reagents. Medical laboratory is the main department and implementer of in vitro diagnostic reagent clinical trials in medical institutions. In recent years, with the rapid development of the in vitro diagnostic industry, the clinical trial projects of in vitro diagnostic reagents conducted by medical laboratory have been increasing day by day. However, there are currently few discussions on the clinical trial of in vitro diagnostic reagents from the perspective of researchers. Therefore, this article summarizes the characteristics of clinical trials of in vitro diagnostic reagents, analyzes the problems and difficulties in conducting clinical trials of in vitro diagnostic reagents in current medical laboratories, and introduces the laboratory′s experience in management; to provide reference for medical testing laboratories that have not yet conducted or have already conducted clinical trials of in vitro diagnostic reagents, in order to improve the quality and efficiency of clinical trials.

Clinical trials are an important method for evaluating the safety and efficacy of in vitro diagnostic reagents, and are a key basis for product registration review and approval. In order to strengthen the management of clinical trials of in vitro diagnostic reagents, the National Medical Products Administration and relevant departments have formulated a series of regulations at the regulatory level, and require applicants and clinical trial institutions to establish a quality management system for clinical trials of in vitro diagnostic reagents. Medical laboratory is the main department and implementer of in vitro diagnostic reagent clinical trials in medical institutions. In recent years, with the rapid development of the in vitro diagnostic industry, the clinical trial projects of in vitro diagnostic reagents conducted by medical laboratory have been increasing day by day. However, there are currently few discussions on the clinical trial of in vitro diagnostic reagents from the perspective of researchers. Therefore, this article summarizes the characteristics of clinical trials of in vitro diagnostic reagents, analyzes the problems and difficulties in conducting clinical trials of in vitro diagnostic reagents in current medical laboratories, and introduces the laboratory′s experience in management; to provide reference for medical testing laboratories that have not yet conducted or have already conducted clinical trials of in vitro diagnostic reagents, in order to improve the quality and efficiency of clinical trials.

AIM:To optimize the formulation of breviscapine oral disintegrating tablets and preparation process. METHODS: To adopt the multifactor and multilevel uniform design to optimize the preparation prescription,disintegrating time as the assessment index. RESULTS: The tablets prepared were finer in appearance,disintegrated within 35 s,with desirable taste.The dissolution rate was faster than breviscapine ordinary tablets. CONCLUSION: Breviscapine oral disintegrating tablets achieve the goal of design and it is easy to operate.

AIM:To establish quality control method for Breviscapine(Orally) Disintegrating Tablets. METHODS: Its main ingredient scutellarin was identified by TLC, the scutellarin in the tablets was determined by HPLC.And the disintegration, dissolution rate and tablet weights were determined. RESULTS: Breviscapine(Orally) Disintegrating Tablets could be disintegrated in 30 seconds,and the tablet weight was fit for the criterion.And the dissolution rate was more quick than the original Breviscapine tablets in the market.A good linearity was obtained within the range of 0.081 6 ?g-0.408 0 ?g with the correlation coefficient(0.999 9.) The recovery was(99.67%) and RSD was 0.47%. CONCLUSION: The method for identifying and determing Breviscapine Orally Disintegrating Tablets is simple,reproducible and practical.