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oncology Oncology

Machine learning to predict occult metastatic lymph nodes along the recurrent laryngeal nerves in thoracic esophageal squamous cell carcinoma.

In BMC cancer

PURPOSE : Esophageal squamous cell carcinoma (ESCC) metastasizes in an unpredictable fashion to adjacent lymph nodes, including those along the recurrent laryngeal nerves (RLNs). This study is to apply machine learning (ML) for prediction of RLN node metastasis in ESCC.

METHODS : The dataset contained 3352 surgically treated ESCC patients whose RLN lymph nodes were removed and pathologically evaluated. Using their baseline and pathological features, ML models were established to predict RLN node metastasis on each side with or without the node status of the contralateral side. Models were trained to achieve at least 90% negative predictive value (NPV) in fivefold cross-validation. The importance of each feature was measured by the permutation score.

RESULTS : Tumor metastases were found in 17.0% RLN lymph nodes on the right and 10.8% on the left. In both tasks, the performance of each model was comparable, with a mean area under the curve ranging from 0.731 to 0.739 (without contralateral RLN node status) and from 0.744 to 0.748 (with contralateral status). All models showed approximately 90% NPV scores, suggesting proper generalizability. The pathology status of chest paraesophgeal nodes and tumor depth had the highest impacts on the risk of RLN node metastasis in both models.

CONCLUSION : This study demonstrated the feasibility of ML in predicting RLN node metastasis in ESCC. These models may potentially be used intraoperatively to spare RLN node dissection in low-risk patients, thereby minimizing adverse events associated with RLN injuries.

Zhang Yiliang, Zhang Longfu, Li Bin, Ye Ting, Zhang Yang, Yu Yongfu, Ma Yuan, Sun Yihua, Xiang Jiaqing, Li Yike, Chen Haiquan

2023-Mar-02

Esophageal squamous cell carcinoma, Lymph node metastasis, Machine learning, Recurrent laryngeal nerve

General General

Prediction and early warning model of mixed exposure to air pollution and meteorological factors on death of respiratory diseases based on machine learning.

In Environmental science and pollution research international

In recent years, with the repeated occurrence of extreme weather and the continuous increase of air pollution, the incidence of weather-related diseases has increased yearly. Air pollution and extreme temperature threaten sensitive groups' lives, among which air pollution is most closely related to respiratory diseases. Owing to the skewed attention, timely intervention is necessary to better predict and warn the occurrence of death from respiratory diseases. In this paper, according to the existing research, based on a number of environmental monitoring data, the regression model is established by integrating the machine learning methods XGBoost, support vector machine (SVM), and generalized additive model (GAM) model. The distributed lag nonlinear model (DLNM) is used to set the warning threshold to transform the data and establish the warning model. According to the DLNM model, the cumulative lag effect of meteorological factors is explored. There is a cumulative lag effect between air temperature and PM2.5, which reaches the maximum when the lag is 3 days and 5 days, respectively. If the low temperature and high environmental pollutants (PM2.5) continue to influence for a long time, the death risk of respiratory diseases will continue to rise, and the early warning model based on DLNM has better performance.

Sun HongYing, Chen SiYi, Li XinYi, Cheng LiPing, Luo YiPei, Xie LingLi

2023-Mar-03

DLNM, Forecast and early warning model, Machine learning methods, Respiratory diseases, SVM, XGBoost

General General

Integrated machine learning-based model and WQI for groundwater quality assessment: ML, geospatial, and hydro-index approaches.

In Environmental science and pollution research international

The demands upon the arid area for water supply pose threats to both the quantity and quality of social and economic activities. Thus, a widely used machine learning model, namely the support vector machines (SVM) integrated with water quality indices (WQI), was used to assess the groundwater quality. The predictive ability of the SVM model was assessed using a field dataset for groundwater from Abu-Sweir and Abu-Hammad, Ismalia, Egypt. Multiple water quality parameters were chosen as independent variables to build the model. The results revealed that the permissible and unsuitable class values range from 36 to 27%, 45 to 36%, and 68 to 15% for the WQI approach, SVM method and SVM-WQI model respectively. Besides, the SVM-WQI model shows a low percentage of the area for excellent class compared to the SVM model and WQI. The SVM model trained with all predictors with a mean square error (MSE) of 0.002 and 0.41; the models that had higher accuracy reached 0.88. Moreover, the study highlighted that SVM-WQI can be successfully implemented for the assessment of groundwater quality (0.90 accuracy). The resulting groundwater model in the study sites indicates that the groundwater is influenced by rock-water interaction and the effect of leaching and dissolution. Overall, the integrated ML model and WQI give an understanding of water quality assessment, which may be helpful in the future development of such areas.

Abu El-Magd Sherif Ahmed, Ismael Ismael S, El-Sabri Mohamed A Sh, Abdo Mohamed Sayed, Farhat Hassan I

2023-Mar-03

Egypt, Machine learning model (ML), SVM, SVM-WQI, WQI

Public Health Public Health

Developing and validating a machine learning ensemble model to predict postoperative delirium in a cohort of high-risk surgical patients: A secondary cohort analysis.

In European journal of anaesthesiology

BACKGROUND : Postoperative delirium (POD) has a negative impact on prognosis, length of stay and the burden of care. Although its prediction and identification may improve postoperative care, this need is largely unmet in the Brazilian public health system.

OBJECTIVE : To develop and validate a machine-learning prediction model and estimate the incidence of delirium. We hypothesised that an ensemble machine-learning prediction model that incorporates predisposing and precipitating features could accurately predict POD.

DESIGN : A secondary analysis nested in a cohort of high-risk surgical patients.

SETTING : An 800-bed, quaternary university-affiliated teaching hospital in Southern Brazil. We included patients operated on from September 2015 to February 2020.

PATIENTS : We recruited 1453 inpatients with an all-cause postoperative 30-day mortality risk greater than 5% assessed preoperatively by the ExCare Model.

MAIN OUTCOME MEASURE : The incidence of POD classified by the Confusion Assessment Method, up to 7 days postoperatively. Predictive model performance with different feature scenarios were compared with the area under the receiver operating characteristic curve.

RESULTS : The cumulative incidence of delirium was 117, giving an absolute risk of 8.05/100 patients. We developed multiple machine-learning nested cross-validated ensemble models. We selected features through partial dependence plot analysis and theoretical framework. We treated the class imbalance with undersampling. Different feature scenarios included: 52 preoperative, 60 postoperative and only three features (age, preoperative length of stay and the number of postoperative complications). The mean areas (95% confidence interval) under the curve ranged from 0.61 (0.59 to 0.63) to 0.74 (0.73 to 0.75).

CONCLUSION : A predictive model composed of three indicative readily available features performed better than those with numerous perioperative features, pointing to its feasibility as a prognostic tool for POD. Further research is required to test the generalisability of this model.

TRIAL REGISTRATION : Institutional Review Board Registration number 04448018.8.0000.5327 (Brazilian CEP/CONEP System, available in https://plataformabrasil.saude.gov.br/).

Neto Paulo C S, Rodrigues Attila L, Stahlschmidt Adriene, Helal Lucas, Stefani Luciana C

2023-Mar-02

Surgery Surgery

Evaluation of Endoscopic Response Using Deep Neural Network in Esophageal Cancer Patients Who Received Neoadjuvant Chemotherapy.

In Annals of surgical oncology ; h5-index 71.0

BACKGROUND : We previously reported that endoscopic response evaluation can preoperatively predict the prognosis and distribution of residual tumors after neoadjuvant chemotherapy (NAC). In this study, we developed artificial intelligence (AI)-guided endoscopic response evaluation using a deep neural network to discriminate endoscopic responders (ERs) in patients with esophageal squamous cell carcinoma (ESCC) after NAC.

METHOD : Surgically resectable ESCC patients who underwent esophagectomy following NAC were retrospectively analyzed in this study. Endoscopic images of the tumors were analyzed using a deep neural network. The model was validated with a test data set using 10 newly collected ERs and 10 newly collected non-ER images. The sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the endoscopic response evaluation by AI and endoscopists were calculated and compared.

RESULTS : Of 193 patients, 40 (21%) were diagnosed as ERs. The median sensitivity, specificity, PPV, and NPV values for ER detection in 10 models were 60%, 100%, 100%, and 71%, respectively. Similarly, the median values by the endoscopist were 80%, 80%, 81%, and 81%, respectively.

CONCLUSION : This proof-of-concept study using a deep learning algorithm demonstrated that the constructed AI-guided endoscopic response evaluation after NAC could identify ER with high specificity and PPV. It would appropriately guide an individualized treatment strategy that includes an organ preservation approach in ESCC patients.

Matsuda Satoru, Irino Tomoyuki, Kawakubo Hirofumi, Takeuchi Masashi, Nishimura Erika, Hisaoka Kazuhiko, Sano Junichi, Kobayashi Ryota, Fukuda Kazumasa, Nakamura Rieko, Takeuchi Hiroya, Kitagawa Yuko

2023-Mar-02

General General

Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends.

In Analytical and bioanalytical chemistry

Surface-enhanced Raman spectroscopy (SERS) has gained increasing attention because it provides rich chemical information and high sensitivity, being applicable in many scientific fields including medical diagnosis, forensic analysis, food control, and microbiology. Although SERS is often limited by the lack of selectivity in the analysis of samples with complex matrices, the use of multivariate statistics and mathematical tools has been demonstrated to be an efficient strategy to circumvent this issue. Importantly, since the rapid development of artificial intelligence has been promoting the implementation of a wide variety of advanced multivariate methods in SERS, a discussion about the extent of their synergy and possible standardization becomes necessary. This critical review comprises the principles, advantages, and limitations of coupling SERS with chemometrics and machine learning for both qualitative and quantitative analytical applications. Recent advances and trends in combining SERS with uncommonly used but powerful data analysis tools are also discussed. Finally, a section on benchmarking and tips for selecting the suitable chemometric/machine learning method is included. We believe this will help to move SERS from an alternative detection strategy to a general analytical technique for real-life applications.

Dos Santos Diego P, Sena Marcelo M, Almeida Mariana R, Mazali Italo O, Olivieri Alejandro C, Villa Javier E L

2023-Mar-03

Data analysis, Nanomaterials, PCA, Plasmonics, Supervised methods, Vibrational spectroscopy