Informatics in Medicine Unlocked最新文献

{"title":"Neonatal asphyxia prediction using features extracted from cardiotocography data by explainable artificial intelligence","authors":"Hayato Kinoshita ,&nbsp;Hiroaki Fukunishi ,&nbsp;Chihiro Shibata ,&nbsp;Toyofumi Hirakawa ,&nbsp;Kohei Miyata ,&nbsp;Fusanori Yotsumoto","doi":"10.1016/j.imu.2025.101636","DOIUrl":"10.1016/j.imu.2025.101636","url":null,"abstract":"<div><h3>Background and objective</h3><div>Developing Artificial Intelligence (AI)-assisted technology for cardiotocography (CTG) monitoring system is highly anticipated in the field of obstetrics. This study developed a neonatal asphyxia prediction model to assist obstetricians and practitioners in making early treatment decisions in clinical practice.</div></div><div><h3>Methods</h3><div>Using 32,711 CTG records, features based on fetal heart rate (FHR) were extracted following Japanese Society of Obstetrics and Gynecology (JSOG) guidelines. The machine learning algorithm LightGBM was adopted to construct a binary prediction model of normal and abnormal states for newborns after delivery. To address the data imbalance between normal and abnormal samples, multiple prediction models were constructed using the underbagging technique. Furthermore, features impacting neonatal asphyxia were analyzed using the SHapley Additive exPlanations (SHAP), an explainable artificial intelligence (XAI) technique.</div></div><div><h3>Results</h3><div>The best prediction model used the Apgar score as the outcome variable and 13 FHR-based features + maternal age as the feature set, with an area under the curve of 0.759. This performance is reliable because this study used 32,711 CTG records, whereas most prior studies used datasets with only a few hundred records. When risk factors were analyzed via SHAP, the top three features were mean FHR, frequency of acceleration, and frequency of marked variability. The relationship between many of the features and abnormal risk corresponded to the CTG interpretation of the JSOG guidelines.</div></div><div><h3>Conclusions</h3><div>This study demonstrated reliable prediction performance using a large dataset along with the rationale behind its prediction. These results will facilitate the use of AI-assisted technology in clinical practice. In the future, it is expected that XAI technology will be integrated into real-time CTG monitoring systems, and that the display of associated risk factors will occur simultaneously with risk alerts.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"54 ","pages":"Article 101636"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143611190","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Investigating the accuracy of neural networks for blood pressure prediction in the ICU","authors":"Charles J. Gillan,&nbsp;Bartosz Gorecki","doi":"10.1016/j.imu.2025.101635","DOIUrl":"10.1016/j.imu.2025.101635","url":null,"abstract":"<div><div>This paper reports on research which investigates the viability of artificial neural networks, used in an ICU environment, for predicting both systolic and diastolic blood pressure up to 1 h ahead. In this environment, patients often receive pharmacological intervention to increase or decrease blood pressure. The physiological state of an ICU patient is therefore quite different to a hyper or hypotensive patient outside hospital, suggesting that predicting blood pressure in this environment is more challenging The work investigates whether building neural network architectures with multivariate input data is capable of predicting blood pressures in this environment. Our work uses skin temperature and heart rate readings in addition to systolic and diastolic blood pressure. Two types of neural network are explored are explored in this paper: an encoder-decoder long short-term memory architecture and, separately, a convolutional neural network architecture. The top-performing configuration, when using a 70 %–30 % train-test split of data, is a convolutional neural network model. This predicted systolic and diastolic blood pressures for a patient with an error of approximately _{3<em>.</em>4 %}. These results are at the same level of accuracy as work on blood pressure prediction outside the ICU environment. Our work shows that neural networks are a viable tool for short term prediction of arterial blood pressures in an ICU context.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"55 ","pages":"Article 101635"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143815369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Early detection of gynecological malignancies using ensemble deep learning models: ResNet50 and inception V3","authors":"Chetna Vaid Kwatra ,&nbsp;Harpreet Kaur ,&nbsp;Monika Mangla ,&nbsp;Arun Singh ,&nbsp;Swapnali N. Tambe ,&nbsp;Saiprasad Potharaju","doi":"10.1016/j.imu.2025.101620","DOIUrl":"10.1016/j.imu.2025.101620","url":null,"abstract":"<div><h3>Background and objective</h3><div>Improving patient outcomes and lowering death rates depend on the early identification of gynecological cancers. This work intends to improve the accuracy and dependability of early gynecological tumor diagnosis by means of a hybrid deep learning model combining ResNet50 and Inception v3 architectures.</div></div><div><h3>Methods</h3><div>The proposed ensemble model combines multi-scale feature extraction of Inception v3 with the deep residual learning capability of ResNet50. A significant number of gynecological images were employed for training, testing, and assessment of the proposed model. By entailing accuracy, sensitivity, specificity, and F1 score, among other parameters the performance of the model was assessed.</div></div><div><h3>Results</h3><div>The first experiment depicted displays that the ensemble model performed better than single models with a training accuracy of 99.80 %, a validation accuracy of 99.80 %, and a test accuracy of 99.80 %. Comparing the two studies done in the current research, the model has shown to have a high sensitivity of 99 %, specificity of 99 %, and F1 score of 0.99, making it better in the identification of gynecological cancers and significantly reducing low true negatives and low true positives.</div></div><div><h3>Conclusions</h3><div>Ensembling of ResNet50 with Inception v3 for early diagnosis of gynecological cancers is promising and reproducible. Thus, according to the presented results, this method can contribute to the diagnoses of diseases by doctors quickly and accurately and, therefore, improve the treatment outcomes and the patient's health</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101620"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103460","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of breast cancer classification and segmentation techniques: A comprehensive review","authors":"Malaya Kumar Nath,&nbsp;Kohilavani Sundararajan,&nbsp;Shanmathi Mathivanan,&nbsp;Bhagyashree Thandapani","doi":"10.1016/j.imu.2025.101642","DOIUrl":"10.1016/j.imu.2025.101642","url":null,"abstract":"<div><div>Breast cancer (BC) is caused by the mutation of breast cells and their uncontrolled proliferation, making diagnosis critical at the chronic stage. Early cancer detection can help plan treatment and reduce its severity and mortality rate. It can be confirmed by the biopsy test. Due to technological advancements, it can be effectively detected by various modalities, such as X-rays, ultrasound, MRI scans, histopathology images, etc. Development in machine learning (ML), data mining, sensors, and signal processing techniques gained popularity in early breast cancer detection and grading. However, these techniques must be improved for better prediction, localization, and grading of cancer tissues. This manuscript discusses the tissue variation due to the propagation of cancer and its havoc in life, along with various AI-based techniques for early identification with their limitations. Publicly available breast cancer databases and performance evaluation metrics used by the researchers have been summarized. Based on the limitations and potential strengths of various techniques, a deep learning (DL) model for multi-class classification of breast cancer for the whole slide image (WSI) is proposed. This study identifies ongoing issues essential for driving future advancements in BC detection and segmentation to improve clinical outcomes.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"56 ","pages":"Article 101642"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143937872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated lumbar intervertebral disc identification and herniation detection in MR images using cascade CNN architecture","authors":"Md Abu Sayed ,&nbsp;Ashiqur Rahman ,&nbsp;Sadman Mohammad Nasif ,&nbsp;Sudipto Halder ,&nbsp;Akram Hossain ,&nbsp;Hasan Ahmed ,&nbsp;Muhammad Abdul Kadir","doi":"10.1016/j.imu.2025.101648","DOIUrl":"10.1016/j.imu.2025.101648","url":null,"abstract":"<div><h3>Objective</h3><div>Identifying herniated discs in MRI scans is inherently challenging due to the small size, irregular shape, and complex appearance of the affected regions. Conventional approaches typically rely on semi-automated region-of-interest (ROI) selection and single-model classification using either axial or sagittal views, limiting diagnostic performance. This study aims to develop an automated, accurate, and efficient system for the detection and classification of lumbar intervertebral disc herniation using deep learning models applied to axial and sagittal MR images.</div></div><div><h3>Methods</h3><div>A YOLO-based framework was developed to automatically identify lumbar intervertebral discs (IVD_1-5) and extract ROIs from MR images. Attention-enhanced and fine-tuned VGG19 and ResNet50 models were employed to analyze axial and sagittal images for herniation detection. A decision fusion strategy was used to combine the classification probabilities from both models to further enhance accuracy. The dataset underwent extensive preprocessing and augmentation to improve model robustness and generalization.</div></div><div><h3>Results</h3><div>The proposed approach demonstrated exceptional performance in detection and classification tasks. For detection, the model achieved mAP50 scores of 95.18 % (axial IVD_1-5), 99.50 % (lumbar regions), and 94.87 % (sagittal IVD_1-5). Classification accuracy reached 97.05 % for axial images and 97.45 % for sagittal images, increasing to 98.09 % with decision fusion.</div></div><div><h3>Conclusion</h3><div>Designed to assist physicians, especially during high-demand periods such as pandemics, this approach has the potential to improve diagnostic efficiency and reduce clinical workload.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"55 ","pages":"Article 101648"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143901918","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning approach to study drug effects and identification of signals from symptomatic and asymptomatic mutation carries using iPSC-derived cardiomyocytes","authors":"Martti Juhola ,&nbsp;Henry Joutsijoki ,&nbsp;Kirsi Penttinen ,&nbsp;Katriina Aalto-Setälä","doi":"10.1016/j.imu.2025.101631","DOIUrl":"10.1016/j.imu.2025.101631","url":null,"abstract":"<div><div>Earlier it has been found that peak data of calcium transient signals originating from human induced pluripotent stem cell-derived cardiomyocytes are possible to be used to study how machine learning methods can be applied to separate which cells respond to a drug. Beating behavior of induced pluripotent stem cell derived cardiomyocytes (iPSC-CMs) from a symptomatic individual and an asymptomatic individual carrying a mutation for Brugada syndrome was analyzed with Ca²⁺ imaging method. Using machine learning methods, it is studied whether it is possible to classify the current peak data successfully and whether differences in the two mutant cell lines could be observed. We applied more machine learning methods than before. Baseline signals were first recorded and they were then exposed to adrenaline and these to an antiarrhythmic drug flecainide which should provoke the disease phenotype. Calcium transient signals derived from induced pluripotent stem cell-derived cardiomyocytes were used for all computational analyses executed. Good classification results were generated with effective machine learning methods. Various test situations were applied to study how different parts of data can be separated to ensure their differences. Good results were gained that support the target so that it is possible to analyze whether the drug impacted on iPSC-CMs. It is also possible to separate which cells were affected by the drug and which were not affected. An important finding was that there were significant differences between calcium transient signals data originated from control subjects and patients and also between responses of the cells from symptomatic and asymptomatic individuals.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"54 ","pages":"Article 101631"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143579488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Machine learning-based classification of medication adherence among patients with noncommunicable diseases","authors":"Wellington Kanyongo ,&nbsp;Absalom E. Ezugwu ,&nbsp;Tsitsi Moyo ,&nbsp;Jean Vincent Fonou Dombeu","doi":"10.1016/j.imu.2024.101611","DOIUrl":"10.1016/j.imu.2024.101611","url":null,"abstract":"<div><div>Non-adherence to medication among individuals with non-communicable diseases (NCDs) leads to increased morbidity, mortality, and healthcare costs. The integration of electronic drug prescription and dispensation systems enables comprehensive analysis of medication adherence (MA). Patient-level and medical claims data for 8141 diabetic and hypertensive patients in Harare, Zimbabwe, were analysed. Non-adherence was defined as medication refills falling below 75 % of the intended 12 monthly claims, while adherence required at least 75 % of the refills. Classification employed multiple machine learning algorithms, including SVM, KNN, DT, Naïve Bayes, DNN, LR, and RF in Python 3.11.3. Significant variables for MA were identified through the Random Forest (RF) feature importance mechanism and the information gain technique. These included the annual quantity of medical supplies, annual claim amount, patient age, wellness program subscription, medical aid cover, contribution towards medical aid cover, comorbidity, diagnosis, hospital cover type, complications development, gender, and medical aid scheme. The total units of medical supplies dispensed annually emerged as the most significant predictor of MA. Considering the 8-feature subset, which consistently produced the most robust machine learning models, the classification accuracy of the ML classifiers ranged from 84.9 % to 88.2 %, while the AUC values varied between 0.857 and 0.934. RF, an ensemble learning technique, was the most robust classifier, achieving 88.2 % accuracy, an AUC of 0.935, and superior precision, recall, and F1-score. This model shows promise as a prognostic tool for enhancing MA, aiding in identifying adherence levels among patients. These findings contribute to addressing disparities in medication refilling and adherence rates among patients with NCDs. The ML model holds potential for the development of intelligent MA and intervention applications to improve patient adherence to medication in the chronic disease domain.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101611"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Spatiotemporal chest wall movement analysis using depth sensor imaging for detecting respiratory asynchrony","authors":"Masaru Mitsuya ,&nbsp;Hiroki Nishine ,&nbsp;Hiroshi Handa ,&nbsp;Masamichi Mineshita ,&nbsp;Masaki Kurosawa ,&nbsp;Tetsuo Kirimoto ,&nbsp;Shohei Sato ,&nbsp;Takemi Matsui ,&nbsp;Guanghao Sun","doi":"10.1016/j.imu.2025.101619","DOIUrl":"10.1016/j.imu.2025.101619","url":null,"abstract":"<div><h3>Background and objective</h3><div>This study aimed to enhance point-of-care pulmonary function tests by developing a novel method for the spatiotemporal analysis of chest wall movements using a sequence of depth sensor images.</div></div><div><h3>Methods</h3><div>The proposed method employs singular value decomposition (SVD) to extract features from respiratory waveforms, which are then used to cluster pixels while preserving high resolution. The initial validation using simulated thoracic movement data confirmed the validity of the method. Further validation with clinical data capturing the chest wall movements of a patient undergoing interventional bronchology for a right bronchial tumor demonstrated the ability of this method to detect respiratory asynchrony.</div></div><div><h3>Results</h3><div>A phase lag of 867 ms was observed between the left and right sides of the rib cage preoperatively along with notable amplitude differences. These asynchronies resolved postoperatively. These results were consistent with the pulmonary pathophysiology, underscoring the clinical relevance of this method. The proposed system, integrated into an iOS app for an iPhone, is user-friendly and noninvasive and has the potential to become a valuable tool for the real-time assessment of interventional outcomes.</div></div><div><h3>Conclusions</h3><div>The novel method can be applied to various pulmonary diseases to detect the regional ventilation distribution. The method establishes a new generic framework for clinical studies of chest wall motion and pathophysiology.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"53 ","pages":"Article 101619"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143103203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Explainable AI for early malaria detection using stacked-LSTM and attention mechanisms","authors":"Adil Gaouar ,&nbsp;Souaad Hamza Cherif ,&nbsp;Abdellatif Rahmoun ,&nbsp;Mostafa El Habib Daho","doi":"10.1016/j.imu.2025.101667","DOIUrl":"10.1016/j.imu.2025.101667","url":null,"abstract":"<div><div>Malaria remains a global public health challenge, affecting more than 247 million people and causing 619,000 deaths worldwide in 2024 (according to WHO). Rapid diagnosis is essential for effective treatment and to improve patients’ chances of survival. In this study, we propose an interpretable deep learning framework for accurate malaria diagnosis using blood smear images. Also, We evaluate and compare several baseline deep learning (DL) models (fundamentals), customized VGG-16 and VGG-19, as well as newer DL models such as Vision Transformer (ViT) and MobileNet, and, for the first time, a stacked long-short-term memory network (stacked-LSTM) with an attention mechanism for automatic detection of malaria from blood smear images. These models were trained and validated on a publicly available dataset of over 27.000 labeled blood smear images. The comparative and statistical study conducted in this research showed us that the proposed Stacked-LSTM model with attention mechanism outperformed all other approaches, achieving a classification accuracy (0.9912), sensitivity, specificity, precision, F1 score (0.9911), and area under the curve (AUC) superior to all other models. Despite their solid performance, these models are often considered ”black boxes” due to their lack of transparency in the decision-making process, which poses significant challenges in medical applications and fields where human life is at stake. To address this, we have integrated explainable AI (XAI) techniques, namely Grad-CAM and LIME, to improve the model’s interpretability. Our results demonstrate the complementary value of combining high-performance deep learning models with XAI methods to enhance trust and certainty in AI-assisted medical diagnosis, suggesting that our model can support early and interpretable malaria detection in clinical environments.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"57 ","pages":"Article 101667"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144632161","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Targeting KRAS G12C and G12S mutations in lung cancer: In silico drug repurposing and antiproliferative assessment on A549 cells","authors":"Mansour S. Alturki ,&nbsp;Nada Tawfeeq ,&nbsp;Amal Alissa ,&nbsp;Zahra Ahbail ,&nbsp;Mohamed S. Gomaa ,&nbsp;Abdulaziz H. Al Khzem ,&nbsp;Thankhoe A. Rants'o ,&nbsp;Mohammad J. Akbar ,&nbsp;Waleed S. Alharbi ,&nbsp;Bayan Y. Alshehri ,&nbsp;Amjad N. Alotaibi ,&nbsp;Fahad A. Almughem ,&nbsp;Abdullah A. Alshehri","doi":"10.1016/j.imu.2024.101612","DOIUrl":"10.1016/j.imu.2024.101612","url":null,"abstract":"<div><div>The RAS protein is a notable target in cancer research, being the most often mutated oncogene in human malignancies. The RAS G12X mutation is predominantly found in non-small cell lung cancer, including G12C and G12S variants, which are associated with a poor prognosis. Despite the approval of two inhibitors for the KRAS G12C mutation (sotorasib and adagrasib), the necessity persists due to the emergence of resistance to these inhibitors, which has become a substantial concern. This work involved the repurposing of FDA-approved drugs through <em>in silico</em> methods to identify compounds capable of covalently binding to KRAS G12C (PDB entry: 6OIM) and G12S (PDB entry: 7TLG). The computational studies involved virtual screening, induced fit, and covalent docking, and molecular dynamics simulations, and identified five promising candidates, the antibiotics; capreomycin, cefadroxil, and Cefdinir, the antifungal; natamycin, and the anti-inflammatory, cortisone. The hits exhibited binding affinities between −9.98 and −11.35 kcal/mol compared to −9.81 for sotorasib and were found to be covalent binders targeting KRAS G12C and G12S. The computational results were supported with <em>in vitro</em> evaluation on A549 malignant cells and HFF-1 non-cancerous cells. The antiproliferative efficacy of these drugs was evaluated by MTS tests, and their IC₅₀ values were determined in which natamycin, although non-selective, and cortisone showed the highest activity with IC₅₀ of 53.42 and 53.51 μg/mL, respectively, followed by cefadroxil (84.63 μg/mL). This study promisingly repurposed five drugs for KRAS mutant lung cancer, of which cefadroxil, and cortisone are particularly warranting further assessment either as a standalone or combination therapy while capreomycin is still an effective inhibitor for KRAS G12C mutant as evident from <em>in silico</em> and <em>in vitro</em> studies.</div></div>","PeriodicalId":13953,"journal":{"name":"Informatics in Medicine Unlocked","volume":"52 ","pages":"Article 101612"},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143178788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}