A systematic literature review for the prediction of anticancer drug response using various machine-learning and deep-learning techniques

Structural brain network metrics as novel predictors of treatment response in restless legs syndrome

OBJECTIVE

This study aimed to investigate morphometric similarity networks in patients with newly diagnosed restless legs syndrome (RLS) compared with healthy controls and to examine their relationship with treatment response.

METHODS

A total of 49 patients with newly diagnosed RLS and 58 healthy controls were prospectively enrolled. Brain magnetic resonance imaging was performed using a 3-T scanner, and morphometric similarity network analysis was conducted on T1-weighted images. The severity of RLS was assessed using the International RLS Scale at baseline and at three months post-treatment initiation. Patients were classified as good or poor responders based on a decrease of ≥5 points in RLS severity scores following treatment with either pramipexole or pregabalin.

RESULTS

Although no significant differences were observed in morphometric similarity networks between patients with RLS and controls, both modularity and small-worldness indices were lower in the RLS group (0.218 vs. 0.258, p = 0.023; 0.841 vs. 0.861, p = 0.042). Among the 40 patients who completed follow-up evaluation, 27 were good responders and 13 were poor responders. Network diameter was significantly higher in good responders than in poor responders (7.061 vs. 6.552, p = 0.002). Similarly, eccentricity was elevated in good responders (5.875 vs. 5.385, p = 0.008). Receiver operating characteristic curve analysis revealed high predictive values for both diameter and eccentricity (AUC = 0.838, p < 0.001; AUC = 0.751, p = 0.002, respectively).

CONCLUSION

Network metrics, particularly diameter and eccentricity, demonstrate potential utility as biomarkers for predicting treatment response in patients with RLS.

CICADA (UCX): A novel approach for automated breast cancer classification through aggressiveness delineation

Breast cancer remains one of the leading causes of mortality worldwide, with current classification and segmentation techniques often falling short in accurately distinguishing between benign and malignant cases. The study both emphasize the novel approach, CICADA (UCX), specifically designed for breast segmentation with a focus on delineating aggressiveness. While the title highlights segmentation, the abstract expands on this by detailing the model's effectiveness in enhancing diagnostic precision in classifying aggressive tumor characteristics. Breast cancer segmentation pertains to the delineation of malignant tissue borders in medical imaging. The objective is to precisely delineate the malignant area from healthy tissues, facilitating reliable evaluation of tumor attributes like location, size, and form. Historically, manual segmentation by radiologists has been the benchmark; however, it is labor-intensive and susceptible to fluctuation among different observers and within the same observer. With the advancement of medical imaging technologies, there is an increasing demand for automated or semi-automatic systems capable of performing segmentation with efficiency and precision. These strategies seek to minimize human error, enhance reproducibility, and expedite diagnosis, so enabling prompt treatment. A significant problem in breast cancer segmentation is the variability in tumor morphology among various patients and imaging techniques. Neoplasms exhibit considerable variability in dimensions, morphology, and density, complicating the formulation of a universal approach. Moreover, elements like breast tissue density, which might hinder tumor appearance in mammograms, further complicate segmentation. A further barrier is the necessity for extensive, meticulously annotated datasets to train and test machine learning models, as medical picture annotation is labor-intensive and demands specialized expertise. Notwithstanding these obstacles, automated breast cancer segmentation has demonstrated significant potential in clinical applications. It assists radiologists in swiftly and precisely identifying questionable areas, resulting in earlier diagnosis and enhanced patient outcomes. Automated devices can aid in treatment planning by delivering accurate measures of tumor size and location, which are essential for establishing suitable surgical or radiation methods. This study addresses these limitations by introducing CICADA (UCX), which aims to enhance diagnostic precision and operational efficiency in clinical applications. The present study focuses on the creation and assessment of a sophisticated medical picture segmentation model, called Cheetah Inspired Convex Adaptive Discriminator Algorithm with Unet Convenet Xt CICADA (UCX), by contrasting it with the most advanced techniques currently in use. With a mean IOU of 96.34 %, a Dice Coefficient/F1-Score of 99.6461 %, and an AUC of 99.88 %, the suggested model performs quite well. The study incorporates various feature selection techniques like Particle Swarm Optimisation, Dragon Fly, Grey Wolf and our proposed novel technique named as CICADA (UCX). Through a thorough comparison analysis using many approaches, the paper highlights the advantages of CICADA (UCX) for medical picture segmentation. The study advances the area by offering fresh perspectives on segmentation accuracy, with a focus on obtaining a high Dice Coefficient/F1-Score. The results highlight how CICADA (UCX) has the ability to greatly improve medical image analysis and enable more precise and effective diagnosis. The CICADA (UCX) model, a revolutionary approach to medical picture segmentation, is presented in this study, which is a significant improvement over other existing technique. The model outperforms state-of-the-art methods in a thorough comparison investigation, showing higher performance across important assessment measures including mean IOU, Dice Coefficient/F1-Score, and AUC. Notably, the model scores a remarkable 99.6461 % Dice Coefficient/F1-Score, demonstrating accurate medical structural delineation. An important aspect of medical imaging applications is segmentation accuracy, which is greatly improved by this study. The results point to possible improvements in operational efficiency and diagnostic accuracy, which would be beneficial to patients as well as medical personnel. This discovery has significance for improving medical picture segmentation techniques and promoting technological developments in medical imaging and computer-aided diagnosis.

Epidemiology and reporting characteristics of systematic reviews of clinical prediction models: a scoping review

OBJECTIVES

This study aimed to explore research trends and areas of interest in systematic reviews (SRs) and meta-analysis of clinical prediction models (CPMs), while summarizing their conduct and reporting characteristics.

STUDY DESIGN AND SETTING

A scoping review was conducted, with searches performed in PubMed, Embase, and Cochrane Library from inception to January 7, 2023. Pairs of reviewers independently screened potentially eligible studies. Data on bibliographic and methodological characteristics were collected and analyzed descriptively.

RESULTS

A total of 1004 SRs published between 2001 and 2023 were included. The number of SRs increased significantly after 2020, with the majority originating from Europe (44.1%) and Asia (26.7%). Populations and outcomes were categorized into 19 and 34 classifications, respectively. The general population was the most frequently targeted (38.7%), and mortality was the most common outcome (18.9%). The prediction or diagnosis of neoplasms in the general population was the most prevalent focus (7.2%). Prognostic models were included only in 69.6% of SRs, while diagnostic models were included in 16.8%; 13.6% included both. The number of primary studies included in SRs ranged from 1 to 495, and the models ranged from 1 to 731. Most SRs lacked standardized reporting: 88.3% did not frame their review questions using established frameworks, and 79.8% did not follow standardized checklists for data extraction. Quality and risk of bias assessments were reported in 76.5% of SRs, with the Prediction model Risk of Bias Assessment Tool (27.9%) and the Quality Assessment of Diagnostic Accuracy Studies-2 tool (17.0%) being the most common. Narrative synthesis was the predominant method for evidence summarization (63.5%), while meta-analysis was conducted in 36.5%. Measures of model performance were summarized in 80.5% of SRs, with discrimination being the most frequently reported (67.7%). Only 5.2% assessed the certainty of evidence. Moreover, 42.2% of SRs published a protocol, 76.0% clearly stated support, and 91.1% stated competing interests.

CONCLUSION

The number of SRs of CPMs has grown substantially, with increasing diversity in populations and outcomes. However, significant variability in conduct and reporting was observed. Future SRs should strictly follow well-developed guidelines, and a dedicated study assessing the reporting quality and risk of bias in SRs of CPMs is warranted.

The specification game: rethinking the evaluation of drug response prediction for precision oncology

Precision oncology plays a pivotal role in contemporary healthcare, aiming to optimize treatments for each patient based on their unique characteristics. This objective has spurred the emergence of various cancer cell line drug response datasets, driven by the need to facilitate pre-clinical studies by exploring the impact of multi-omics data on drug response. Despite the proliferation of machine learning models for Drug Response Prediction (DRP), their validation remains critical to reliably assess their usefulness for drug discovery, precision oncology and their actual ability to generalize over the immense space of cancer cells and chemical compounds. Scientific contribution In this paper we show that the commonly used evaluation strategies for DRP methods can be easily fooled by commonly occurring dataset biases, and they are therefore not able to truly measure the ability of DRP methods to generalize over drugs and cell lines ("specification gaming"). This problem hinders the development of reliable DRP methods and their application to experimental pipelines. Here we propose a new validation protocol composed by three Aggregation Strategies (Global, Fixed-Drug, and Fixed-Cell Line) integrating them with three of the most commonly used train-test evaluation settings, to ensure a truly realistic assessment of the prediction performance. We also scrutinize the challenges associated with using IC50 as a prediction label, showing how its close correlation with the drug concentration ranges worsens the risk of misleading performance assessment, and we indicate an additional reason to replace it with the Area Under the Dose-Response Curve instead.

Drug discovery and mechanism prediction with explainable graph neural networks

Apprehension of drug action mechanism is paramount for drug response prediction and precision medicine. The unprecedented development of machine learning and deep learning algorithms has expedited the drug response prediction research. However, existing methods mainly focus on forward encoding of drugs, which is to obtain an accurate prediction of the response levels, but omitted to decipher the reaction mechanism between drug molecules and genes. We propose the eXplainable Graph-based Drug response Prediction (XGDP) approach that achieves a precise drug response prediction and reveals the comprehensive mechanism of action between drugs and their targets. XGDP represents drugs with molecular graphs, which naturally preserve the structural information of molecules and a Graph Neural Network module is applied to learn the latent features of molecules. Gene expression data from cancer cell lines are incorporated and processed by a Convolutional Neural Network module. A couple of deep learning attribution algorithms are leveraged to interpret interactions between drug molecular features and genes. We demonstrate that XGDP not only enhances the prediction accuracy compared to pioneering works but is also capable of capturing the salient functional groups of drugs and interactions with significant genes of cancer cells.

Development and validation of prognostic machine learning models for short- and long-term mortality among acutely admitted patients based on blood tests

Several scores predicting mortality at the emergency department have been developed. However, all with shortcomings either simple and applicable in a clinical setting, with poor performance, or advanced, with high performance, but clinically difficult to implement. This study aimed to explore if machine learning algorithms could predict all-cause short- and long-term mortality based on the routine blood test collected at admission.

METHODS

We analyzed data from a retrospective cohort study, including patients > 18 years admitted to the Emergency Department (ED) of Copenhagen University Hospital Hvidovre, Denmark between November 2013 and March 2017. The primary outcomes were 3-, 10-, 30-, and 365-day mortality after admission. PyCaret, an automated machine learning library, was used to evaluate the predictive performance of fifteen machine learning algorithms using the area under the receiver operating characteristic curve (AUC).

RESULTS

Data from 48,841 admissions were analyzed, of these 34,190 (70%) were randomly divided into training data, and 14,651 (30%) were in test data. Eight machine learning algorithms achieved very good to excellent results of AUC on test data in a of range 0.85-0.93. In prediction of short-term mortality, lactate dehydrogenase (LDH), leukocyte counts and differentials, Blood urea nitrogen (BUN) and mean corpuscular hemoglobin concentration (MCHC) were the best predictors, whereas prediction of long-term mortality was favored by age, LDH, soluble urokinase plasminogen activator receptor (suPAR), albumin, and blood urea nitrogen (BUN).

CONCLUSION

The findings suggest that measures of biomarkers taken from one blood sample during admission to the ED can identify patients at high risk of short-and long-term mortality following emergency admissions.

Integration of Computational Docking into Anti-Cancer Drug Response Prediction Models

Cancer is a heterogeneous disease in that tumors of the same histology type can respond differently to a treatment. Anti-cancer drug response prediction is of paramount importance for both drug development and patient treatment design. Although various computational methods and data have been used to develop drug response prediction models, it remains a challenging problem due to the complexities of cancer mechanisms and cancer-drug interactions. To better characterize the interaction between cancer and drugs, we investigate the feasibility of integrating computationally derived features of molecular mechanisms of action into prediction models. Specifically, we add docking scores of drug molecules and target proteins in combination with cancer gene expressions and molecular drug descriptors for building response models. The results demonstrate a marginal improvement in drug response prediction performance when adding docking scores as additional features, through tests on large drug screening data. We discuss the limitations of the current approach and provide the research community with a baseline dataset of the large-scale computational docking for anti-cancer drugs.

Exploring the potential of machine learning to design antidiabetic molecules: a comprehensive study with experimental validation

Recent advances in hardware and software algorithms have led to the rise of data-driven approaches for designing therapeutic modalities. One of the major causes of human mortality is diabetes. Thus, there is a tremendous opportunity for research into effective antidiabetic designs. Therefore, in this study, we used machine learning-based small molecule design. We used various chemoinformatic and binary fingerprint techniques on small molecules to construct multiple models for alpha-amylase inhibitors. Among these models, the top models were used for ensemble-based machine learning predictions on libraries of organic molecules supplemented with synthetic scaffolds that could be used as antidiabetic agents. Further, involved identifying 10 promising molecules from computational studies and determining their inhibitory effects on alpha-amylase. These molecules were synthesised and thoroughly analysed to assess their biological inhibitory properties. Then, thermodynamic simulations were conducted to determine the stability and affinity of experimentally active molecules. The research results showcased the top 10 ML models recorded impressive statistics with an average model score of 0.8216, Pearson-r value of 0.827 and external validation yielding a Q2 value of 0.835, proving their reliability and accuracy. Ten derivatives of benzothiophene dioxolane was prime research focus due to computational predictions. The biological inhibitory assay of synthesised molecules showed that small molecules with ID ALC5 and ALC6 exhibited inhibitory efficiencies (IC50) of 2.1 ± 0.14 µM and 5.71 ± 0.02 µM against alpha-amylase enzyme, whereas other molecules showed moderate inhibition. In conclusion, the positive results of the experiment indicate that researchers should explore machine learning-driven design.Communicated by Ramaswamy H. Sarma.

Revolutionizing healthcare: the role of artificial intelligence in clinical practice

INTRODUCTION

Healthcare systems are complex and challenging for all stakeholders, but artificial intelligence (AI) has transformed various fields, including healthcare, with the potential to improve patient care and quality of life. Rapid AI advancements can revolutionize healthcare by integrating it into clinical practice. Reporting AI's role in clinical practice is crucial for successful implementation by equipping healthcare providers with essential knowledge and tools.

RESEARCH SIGNIFICANCE

This review article provides a comprehensive and up-to-date overview of the current state of AI in clinical practice, including its potential applications in disease diagnosis, treatment recommendations, and patient engagement. It also discusses the associated challenges, covering ethical and legal considerations and the need for human expertise. By doing so, it enhances understanding of AI's significance in healthcare and supports healthcare organizations in effectively adopting AI technologies.

MATERIALS AND METHODS

The current investigation analyzed the use of AI in the healthcare system with a comprehensive review of relevant indexed literature, such as PubMed/Medline, Scopus, and EMBASE, with no time constraints but limited to articles published in English. The focused question explores the impact of applying AI in healthcare settings and the potential outcomes of this application.

RESULTS

Integrating AI into healthcare holds excellent potential for improving disease diagnosis, treatment selection, and clinical laboratory testing. AI tools can leverage large datasets and identify patterns to surpass human performance in several healthcare aspects. AI offers increased accuracy, reduced costs, and time savings while minimizing human errors. It can revolutionize personalized medicine, optimize medication dosages, enhance population health management, establish guidelines, provide virtual health assistants, support mental health care, improve patient education, and influence patient-physician trust.

CONCLUSION

AI can be used to diagnose diseases, develop personalized treatment plans, and assist clinicians with decision-making. Rather than simply automating tasks, AI is about developing technologies that can enhance patient care across healthcare settings. However, challenges related to data privacy, bias, and the need for human expertise must be addressed for the responsible and effective implementation of AI in healthcare.

NeuPD-A Neural Network-Based Approach to Predict Antineoplastic Drug Response

With the beginning of the high-throughput screening, in silico-based drug response analysis has opened lots of research avenues in the field of personalized medicine. For a decade, many different predicting techniques have been recommended for the antineoplastic (anti-cancer) drug response, but still, there is a need for improvements in drug sensitivity prediction. The intent of this research study is to propose a framework, namely NeuPD, to validate the potential anti-cancer drugs against a panel of cancer cell lines in publicly available datasets. The datasets used in this work are Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE). As not all drugs are effective on cancer cell lines, we have worked on 10 essential drugs from the GDSC dataset that have achieved the best modeling results in previous studies. We also extracted 1610 essential oncogene expressions from 983 cell lines from the same dataset. Whereas, from the CCLE dataset, 16,383 gene expressions from 1037 cell lines and 24 drugs have been used in our experiments. For dimensionality reduction, Pearson correlation is applied to best fit the model. We integrate the genomic features of cell lines and drugs' fingerprints to fit the neural network model. For evaluation of the proposed NeuPD framework, we have used repeated K-fold cross-validation with 5 times repeats where K = 10 to demonstrate the performance in terms of root mean square error (RMSE) and coefficient determination (R²). The results obtained on the GDSC dataset that were measured using these cost functions show that our proposed NeuPD framework has outperformed existing approaches with an RMSE of 0.490 and R² of 0.929.