1
|
Chakrabarty N, Mahajan A. Imaging Analytics using Artificial Intelligence in Oncology: A Comprehensive Review. Clin Oncol (R Coll Radiol) 2024; 36:498-513. [PMID: 37806795 DOI: 10.1016/j.clon.2023.09.013] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2023] [Revised: 08/09/2023] [Accepted: 09/21/2023] [Indexed: 10/10/2023]
Abstract
The present era has seen a surge in artificial intelligence-related research in oncology, mainly using deep learning, because of powerful computer hardware, improved algorithms and the availability of large amounts of data from open-source domains and the use of transfer learning. Here we discuss the multifaceted role of deep learning in cancer care, ranging from risk stratification, the screening and diagnosis of cancer, to the prediction of genomic mutations, treatment response and survival outcome prediction, through the use of convolutional neural networks. Another role of artificial intelligence is in the generation of automated radiology reports, which is a boon in high-volume centres to minimise report turnaround time. Although a validated and deployable deep-learning model for clinical use is still in its infancy, there is ongoing research to overcome the barriers for its universal implementation and we also delve into this aspect. We also briefly describe the role of radiomics in oncoimaging. Artificial intelligence can provide answers pertaining to cancer management at baseline imaging, saving cost and time. Imaging biobanks, which are repositories of anonymised images, are also briefly described. We also discuss the commercialisation and ethical issues pertaining to artificial intelligence. The latest generation generalist artificial intelligence model is also briefly described at the end of the article. We believe this article will not only enrich knowledge, but also promote research acumen in the minds of readers to take oncoimaging to another level using artificial intelligence and also work towards clinical translation of such research.
Collapse
Affiliation(s)
- N Chakrabarty
- Department of Radiodiagnosis, Advanced Centre for Treatment, Research and Education in Cancer, Tata Memorial Centre, Homi Bhabha National Institute (HBNI), Parel, Mumbai, Maharashtra, India.
| | - A Mahajan
- The Clatterbridge Cancer Centre NHS Foundation Trust, Liverpool, UK.
| |
Collapse
|
2
|
Palomba G, Fernicola A, Corte MD, Capuano M, De Palma GD, Aprea G. Artificial intelligence in screening and diagnosis of surgical diseases: A narrative review. AIMS Public Health 2024; 11:557-576. [PMID: 39027395 PMCID: PMC11252578 DOI: 10.3934/publichealth.2024028] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 03/29/2024] [Accepted: 04/02/2024] [Indexed: 07/20/2024] Open
Abstract
Artificial intelligence (AI) is playing an increasing role in several fields of medicine. It is also gaining popularity among surgeons as a valuable screening and diagnostic tool for many conditions such as benign and malignant colorectal, gastric, thyroid, parathyroid, and breast disorders. In the literature, there is no review that groups together the various application domains of AI when it comes to the screening and diagnosis of main surgical diseases. The aim of this review is to describe the use of AI in these settings. We performed a literature review by searching PubMed, Web of Science, Scopus, and Embase for all studies investigating the role of AI in the surgical setting, published between January 01, 2000, and June 30, 2023. Our focus was on randomized controlled trials (RCTs), meta-analysis, systematic reviews, and observational studies, dealing with large cohorts of patients. We then gathered further relevant studies from the reference list of the selected publications. Based on the studies reviewed, it emerges that AI could strongly enhance the screening efficiency, clinical ability, and diagnostic accuracy for several surgical conditions. Some of the future advantages of this technology include implementing, speeding up, and improving the automaticity with which AI recognizes, differentiates, and classifies the various conditions.
Collapse
Affiliation(s)
- Giuseppe Palomba
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| | - Agostino Fernicola
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| | - Marcello Della Corte
- Azienda Ospedaliera Universitaria San Giovanni di Dio e Ruggi d'Aragona - OO. RR. Scuola Medica Salernitana, Salerno, Italy
| | - Marianna Capuano
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| | - Giovanni Domenico De Palma
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| | - Giovanni Aprea
- Department of Clinical Medicine and Surgery, University of Naples, “Federico II”, Sergio Pansini 5, 80131, Naples, Italy
| |
Collapse
|
3
|
van Nassau SCMW, Bol GM, van der Baan FH, Roodhart JML, Vink GR, Punt CJA, May AM, Koopman M, Derksen JWG. Harnessing the Potential of Real-World Evidence in the Treatment of Colorectal Cancer: Where Do We Stand? Curr Treat Options Oncol 2024; 25:405-426. [PMID: 38367182 PMCID: PMC10997699 DOI: 10.1007/s11864-024-01186-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 01/17/2024] [Indexed: 02/19/2024]
Abstract
OPINION STATEMENT Treatment guidelines for colorectal cancer (CRC) are primarily based on the results of randomized clinical trials (RCTs), the gold standard methodology to evaluate safety and efficacy of oncological treatments. However, generalizability of trial results is often limited due to stringent eligibility criteria, underrepresentation of specific populations, and more heterogeneity in clinical practice. This may result in an efficacy-effectiveness gap and uncertainty regarding meaningful benefit versus treatment harm. Meanwhile, conduct of traditional RCTs has become increasingly challenging due to identification of a growing number of (small) molecular subtypes. These challenges-combined with the digitalization of health records-have led to growing interest in use of real-world data (RWD) to complement evidence from RCTs. RWD is used to evaluate epidemiological trends, quality of care, treatment effectiveness, long-term (rare) safety, and quality of life (QoL) measures. In addition, RWD is increasingly considered in decision-making by clinicians, regulators, and payers. In this narrative review, we elaborate on these applications in CRC, and provide illustrative examples. As long as the quality of RWD is safeguarded, ongoing developments, such as common data models, federated learning, and predictive modelling, will further unfold its potential. First, whenever possible, we recommend conducting pragmatic trials, such as registry-based RCTs, to optimize generalizability and answer clinical questions that are not addressed in registrational trials. Second, we argue that marketing approval should be conditional for patients who would have been ineligible for the registrational trial, awaiting planned (non) randomized evaluation of outcomes in the real world. Third, high-quality effectiveness results should be incorporated in treatment guidelines to aid in patient counseling. We believe that a coordinated effort from all stakeholders is essential to improve the quality of RWD, create a learning healthcare system with optimal use of trials and real-world evidence (RWE), and ultimately ensure personalized care for every CRC patient.
Collapse
Affiliation(s)
- Sietske C M W van Nassau
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, PO Box 85500, Utrecht, 3584 CX, The Netherlands.
| | - Guus M Bol
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, PO Box 85500, Utrecht, 3584 CX, The Netherlands
| | - Frederieke H van der Baan
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, PO Box 85500, Utrecht, 3584 CX, The Netherlands
- Department of Epidemiology & Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Jeanine M L Roodhart
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, PO Box 85500, Utrecht, 3584 CX, The Netherlands
| | - Geraldine R Vink
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, PO Box 85500, Utrecht, 3584 CX, The Netherlands
- Department of Research and Development, Netherlands Comprehensive Cancer Organisation (IKNL), Utrecht, The Netherlands
| | - Cornelis J A Punt
- Department of Epidemiology & Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Anne M May
- Department of Epidemiology & Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Miriam Koopman
- Department of Medical Oncology, University Medical Center Utrecht, Utrecht University, Heidelberglaan 100, PO Box 85500, Utrecht, 3584 CX, The Netherlands
| | - Jeroen W G Derksen
- Department of Epidemiology & Health Economics, Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, The Netherlands
| |
Collapse
|
4
|
Yang J, Huang J, Han D, Ma X. Artificial Intelligence Applications in the Treatment of Colorectal Cancer: A Narrative Review. Clin Med Insights Oncol 2024; 18:11795549231220320. [PMID: 38187459 PMCID: PMC10771756 DOI: 10.1177/11795549231220320] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2023] [Accepted: 11/26/2023] [Indexed: 01/09/2024] Open
Abstract
Colorectal cancer is the third most prevalent cancer worldwide, and its treatment has been a demanding clinical problem. Beyond traditional surgical therapy and chemotherapy, newly revealed molecular mechanisms diversify therapeutic approaches for colorectal cancer. However, the selection of personalized treatment among multiple treatment options has become another challenge in the era of precision medicine. Artificial intelligence has recently been increasingly investigated in the treatment of colorectal cancer. This narrative review mainly discusses the applications of artificial intelligence in the treatment of colorectal cancer patients. A comprehensive literature search was conducted in MEDLINE, EMBASE, and Web of Science to identify relevant papers, resulting in 49 articles being included. The results showed that, based on different categories of data, artificial intelligence can predict treatment outcomes and essential guidance information of traditional and novel therapies, thus enabling individualized treatment strategy selection for colorectal cancer patients. Some frequently implemented machine learning algorithms and deep learning frameworks have also been employed for long-term prognosis prediction in patients with colorectal cancer. Overall, artificial intelligence shows encouraging results in treatment strategy selection and prognosis evaluation for colorectal cancer patients.
Collapse
Affiliation(s)
- Jiaqing Yang
- Department of Biotherapy, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China
- West China School of Medicine, West China Hospital, Sichuan University, Chengdu, China
| | - Jing Huang
- Department of Ultrasound, West China Hospital, Sichuan University, Chengdu, China
| | - Deqian Han
- Department of Oncology, West China School of Public Health and West China Fourth Hospital, Sichuan University, Chengdu, China
| | - Xuelei Ma
- Department of Biotherapy, West China Hospital and State Key Laboratory of Biotherapy, Sichuan University, Chengdu, China
| |
Collapse
|
5
|
Das K, Paltani M, Tripathi PK, Kumar R, Verma S, Kumar S, Jain CK. Current implications and challenges of artificial intelligence technologies in therapeutic intervention of colorectal cancer. EXPLORATION OF TARGETED ANTI-TUMOR THERAPY 2023; 4:1286-1300. [PMID: 38213536 PMCID: PMC10776591 DOI: 10.37349/etat.2023.00197] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2023] [Accepted: 08/28/2023] [Indexed: 01/13/2024] Open
Abstract
Irrespective of men and women, colorectal cancer (CRC), is the third most common cancer in the population with more than 1.85 million cases annually. Fewer than 20% of patients only survive beyond five years from diagnosis. CRC is a highly preventable disease if diagnosed at the early stage of malignancy. Several screening methods like endoscopy (like colonoscopy; gold standard), imaging examination [computed tomographic colonography (CTC)], guaiac-based fecal occult blood (gFOBT), immunochemical test from faeces, and stool DNA test are available with different levels of sensitivity and specificity. The available screening methods are associated with certain drawbacks like invasiveness, cost, or sensitivity. In recent years, computer-aided systems-based screening, diagnosis, and treatment have been very promising in the early-stage detection and diagnosis of CRC cases. Artificial intelligence (AI) is an enormously in-demand, cost-effective technology, that uses various tools machine learning (ML), and deep learning (DL) to screen, diagnose, and stage, and has great potential to treat CRC. Moreover, different ML algorithms and neural networks [artificial neural network (ANN), k-nearest neighbors (KNN), and support vector machines (SVMs)] have been deployed to predict precise and personalized treatment options. This review examines and summarizes different ML and DL models used for therapeutic intervention in CRC cancer along with the gap and challenges for AI.
Collapse
Affiliation(s)
- Kriti Das
- Department of Artificial Intelligence and Precision Medicine, School of Allied Health Sciences and Management, Delhi Pharmaceutical Sciences and Research University, New Delhi 110017, India
| | - Maanvi Paltani
- Department of Artificial Intelligence and Precision Medicine, School of Allied Health Sciences and Management, Delhi Pharmaceutical Sciences and Research University, New Delhi 110017, India
| | - Pankaj Kumar Tripathi
- Department of Biotechnology, Jaypee Institute of Information Technology, Noida 201309, Uttar Pradesh, India
| | - Rajnish Kumar
- Department of Medical Laboratory Technology, School of Allied Health Sciences, Delhi Pharmaceutical Sciences and Research University, Delhi 110017, India
| | - Saniya Verma
- Department of Medical Laboratory Technology, School of Allied Health Sciences, Delhi Pharmaceutical Sciences and Research University, Delhi 110017, India
| | - Subodh Kumar
- Department of Medical Laboratory Technology, School of Allied Health Sciences, Delhi Pharmaceutical Sciences and Research University, Delhi 110017, India
| | - Chakresh Kumar Jain
- Department of Biotechnology, Jaypee Institute of Information Technology, Noida 201309, Uttar Pradesh, India
| |
Collapse
|
6
|
Ruiz Sarrias O, Gónzalez Deza C, Rodríguez Rodríguez J, Arrizibita Iriarte O, Vizcay Atienza A, Zumárraga Lizundia T, Sayar Beristain O, Aldaz Pastor A. Predicting Severe Haematological Toxicity in Gastrointestinal Cancer Patients Undergoing 5-FU-Based Chemotherapy: A Bayesian Network Approach. Cancers (Basel) 2023; 15:4206. [PMID: 37686482 PMCID: PMC10486471 DOI: 10.3390/cancers15174206] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2023] [Revised: 07/28/2023] [Accepted: 08/16/2023] [Indexed: 09/10/2023] Open
Abstract
PURPOSE Severe toxicity is reported in about 30% of gastrointestinal cancer patients receiving 5-Fluorouracil (5-FU)-based chemotherapy. To date, limited tools exist to identify at risk patients in this setting. The objective of this study was to address this need by designing a predictive model using a Bayesian network, a probabilistic graphical model offering robust, explainable predictions. METHODS We utilized a dataset of 267 gastrointestinal cancer patients, conducting preprocessing, and splitting it into TRAIN and TEST sets (80%:20% ratio). The RandomForest algorithm assessed variable importance based on MeanDecreaseGini coefficient. The bnlearn R library helped design a Bayesian network model using a 10-fold cross-validation on the TRAIN set and the aic-cg method for network structure optimization. The model's performance was gauged based on accuracy, sensitivity, and specificity, using cross-validation on the TRAIN set and independent validation on the TEST set. RESULTS The model demonstrated satisfactory performance with an average accuracy of 0.85 (±0.05) and 0.80 on TRAIN and TEST datasets, respectively. The sensitivity and specificity were 0.82 (±0.14) and 0.87 (±0.07) for the TRAIN dataset, and 0.71 and 0.83 for the TEST dataset, respectively. A user-friendly tool was developed for clinical implementation. CONCLUSIONS Despite several limitations, our Bayesian network model demonstrated a high level of accuracy in predicting the risk of developing severe haematological toxicity in gastrointestinal cancer patients receiving 5-FU-based chemotherapy. Future research should aim at model validation in larger cohorts of patients and different clinical settings.
Collapse
Affiliation(s)
- Oskitz Ruiz Sarrias
- Department of Mathematics and Statistic, NNBi, 31191 Esquiroz, Navarra, Spain; (O.R.S.)
| | - Cristina Gónzalez Deza
- Department of Medical Oncology, Clínica Universidad De Navarra, 31008 Pamplona, Navarra, Spain; (C.G.D.); (J.R.R.); (T.Z.L.)
| | - Javier Rodríguez Rodríguez
- Department of Medical Oncology, Clínica Universidad De Navarra, 31008 Pamplona, Navarra, Spain; (C.G.D.); (J.R.R.); (T.Z.L.)
| | | | - Angel Vizcay Atienza
- Department of Medical Oncology, Clínica Universidad De Navarra, 31008 Pamplona, Navarra, Spain; (C.G.D.); (J.R.R.); (T.Z.L.)
| | - Teresa Zumárraga Lizundia
- Department of Medical Oncology, Clínica Universidad De Navarra, 31008 Pamplona, Navarra, Spain; (C.G.D.); (J.R.R.); (T.Z.L.)
| | | | | |
Collapse
|
7
|
Dhiman P, Ma J, Andaur Navarro CL, Speich B, Bullock G, Damen JAA, Hooft L, Kirtley S, Riley RD, Van Calster B, Moons KGM, Collins GS. Overinterpretation of findings in machine learning prediction model studies in oncology: a systematic review. J Clin Epidemiol 2023; 157:120-133. [PMID: 36935090 DOI: 10.1016/j.jclinepi.2023.03.012] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2022] [Revised: 03/03/2023] [Accepted: 03/14/2023] [Indexed: 03/19/2023]
Abstract
OBJECTIVES In biomedical research, spin is the overinterpretation of findings, and it is a growing concern. To date, the presence of spin has not been evaluated in prognostic model research in oncology, including studies developing and validating models for individualized risk prediction. STUDY DESIGN AND SETTING We conducted a systematic review, searching MEDLINE and EMBASE for oncology-related studies that developed and validated a prognostic model using machine learning published between 1st January, 2019, and 5th September, 2019. We used existing spin frameworks and described areas of highly suggestive spin practices. RESULTS We included 62 publications (including 152 developed models; 37 validated models). Reporting was inconsistent between methods and the results in 27% of studies due to additional analysis and selective reporting. Thirty-two studies (out of 36 applicable studies) reported comparisons between developed models in their discussion and predominantly used discrimination measures to support their claims (78%). Thirty-five studies (56%) used an overly strong or leading word in their title, abstract, results, discussion, or conclusion. CONCLUSION The potential for spin needs to be considered when reading, interpreting, and using studies that developed and validated prognostic models in oncology. Researchers should carefully report their prognostic model research using words that reflect their actual results and strength of evidence.
Collapse
Affiliation(s)
- Paula Dhiman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
| | - Jie Ma
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Constanza L Andaur Navarro
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Benjamin Speich
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK; Meta-Research Centre, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Garrett Bullock
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Johanna A A Damen
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Lotty Hooft
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Shona Kirtley
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK
| | - Richard D Riley
- Centre for Prognosis Research, School of Medicine, Keele University, Staffordshire, UK, ST5 5BG
| | - Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium; Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands; EPI-centre, KU Leuven, Leuven, Belgium
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford OX3 7LD, UK; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| |
Collapse
|
8
|
Vilalta-Lacarra A, Aldaz A, Sala-Elarre P, Urrizola A, Chopitea A, Arbea L, Rotellar F, Pardo F, Martí-Cruchaga P, Zozaya G, Subtil JC, Rodríguez-Rodríguez J, Ponz-Sarvise M. Therapeutic drug monitoring of neoadjuvant mFOLFIRINOX in resected pancreatic ductal adenocarcinoma. Pancreatology 2023:S1424-3903(23)00065-0. [PMID: 37169668 DOI: 10.1016/j.pan.2023.03.001] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 02/20/2023] [Accepted: 03/06/2023] [Indexed: 05/13/2023]
Abstract
BACKGROUND Despite a potentially curative treatment, the prognosis after upfront surgery and adjuvant chemotherapy for patients with resectable pancreatic ductal adenocarcinoma (PDAC) is poor. Modified FOLFIRINOX (mFOLFIRINOX) is a cornerstone in the systemic treatment of PDAC, including the neoadjuvant setting. Pharmacokinetic-guided (PKG) dosing has demonstrated beneficial effects in other tumors, but scarce data is available in pancreatic cancer. METHODS Forty-six patients with resected PDAC after mFOLFIRINOX neoadjuvant approach and included in an institutional protocol for anticancer drug monitoring were retrospectively analyzed. 5-Fluorouracil (5-FU) dosage was adjusted throughout neoadjuvant treatment according to pharmacokinetic parameters and Irinotecan (CPT-11) pharmacokinetic variables were retrospectively estimated. RESULTS By exploratory univariate analyses, a significantly longer progression-free survival was observed for patients with either 5-FU area under the curve (AUC) above 28 mcg·h/mL or CPT-11 AUC values below 10 mcg·h/mL. In the multivariate analyses adjusted by age, gender, performance status and resectability after stratification according to both pharmacokinetic parameters, the risk of progression was significantly reduced in patients with 5-FU AUC ≥28 mcg·h/mL [HR = 0.251, 95% CI 0.096-0.656; p = 0.005] and CPT-11 AUC <10 mcg·h/mL [HR = 0.189, 95% CI 0.073-0.486, p = 0.001]. CONCLUSIONS Pharmacokinetically-guided dose adjustment of standard chemotherapy treatments might improve survival outcomes in patients with pancreatic ductal adenocarcinoma.
Collapse
Affiliation(s)
| | - Azucena Aldaz
- Pharmacy Service, Clinica Universidad de Navarra, Pamplona, Spain
| | - Pablo Sala-Elarre
- Department of Medical Oncology, Clinica Universidad de Navarra, Pamplona, Spain
| | - Amaia Urrizola
- Department of Medical Oncology, Clinica Universidad de Navarra, Pamplona, Spain
| | - Ana Chopitea
- Department of Medical Oncology, Clinica Universidad de Navarra, Pamplona, Spain
| | - Leire Arbea
- Department of Radiation Oncology, Clinica Universidad de Navarra, Pamplona, Spain
| | - Fernando Rotellar
- Hepatobiliary Surgery, Clinica Universidad de Navarra, Pamplona, Spain
| | - Fernando Pardo
- Hepatobiliary Surgery, Clinica Universidad de Navarra, Pamplona, Spain
| | | | - Gabriel Zozaya
- Hepatobiliary Surgery, Clinica Universidad de Navarra, Pamplona, Spain
| | - Jose Carlos Subtil
- Department of Gastroenterology, Clinica Universidad de Navarra, Pamplona, Spain
| | | | - Mariano Ponz-Sarvise
- Department of Medical Oncology, Clinica Universidad de Navarra, Pamplona, Spain.
| |
Collapse
|
9
|
Machine learning-based models for genomic predicting neoadjuvant chemotherapeutic sensitivity in cervical cancer. Biomed Pharmacother 2023; 159:114256. [PMID: 36652730 DOI: 10.1016/j.biopha.2023.114256] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Revised: 01/08/2023] [Accepted: 01/14/2023] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND The PI3K/Akt pathway involves in regulating resistance to platinum-based neoadjuvant chemotherapy (NACT) in locally advanced cervical cancer (LACC) patients. Single nucleotide polymorphisms (SNPs) reflect the basic genetic variation between individuals. Random forest (RF) is one of the machine-learning models that can predict drug sensitivity with high accuracy. We applied the RF model for genomic prediction of NACT sensitivity in LACC patients. MATERIALS AND METHODS A total of 259 LACC patients were separated to two groups (i) effective and (ii) ineffective NACT group, depending on the NACT response. The 24 SNPs in four genes (PTEN, PIK3CA, Akt1, and Akt2) were genotyped by the Sequenom MassArray system in these patients. We implemented the SNPs as the feature to train the RF model, calculated the feature importance using mean decreases in impurity based on the model, and further analyzed the importance of each SNP. RESULTS The importance analysis indicated that the top three SNPs (rs4558508, rs1130233, and rs7259541) and the last six loci (rs892120, rs62107593, rs34716810, rs10416620, rs41275748, and rs41275746) were all located in Akt. The patients carrying heterozygous GA in Akt2 rs4558508 had a considerably higher risk of chemoresistance than those carrying GG or AA genotype. CONCLUSION The RF model could accurately predict the response to platinum-based NACT of LACC patients. The variables of Akt2 rs4558508 and rs7259541, and Akt1 rs1130233 were major polymorphic loci for NACT inefficiency. The LACC patients carrying heterozygous GA of Akt2 rs4558508 had a significantly increased risk of chemoresistance. Akt was an important gene in PI3K/Akt pathway that could predict the response of platinum-based NACT. The study applied the basis for an individualized approach to LACC patient therapy.
Collapse
|
10
|
Zhou T, Liu Y, Lei K, Liu J, Hu M, Guo L, Guo Y, Ye Q. A "Trojan Horse" Strategy: The Preparation of Bile Acid-Modifying Irinotecan Hydrochloride Nanoliposomes for Liver-Targeted Anticancer Drug Delivery System Study. Molecules 2023; 28:molecules28041577. [PMID: 36838565 PMCID: PMC9963329 DOI: 10.3390/molecules28041577] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/02/2023] [Accepted: 02/02/2023] [Indexed: 02/10/2023] Open
Abstract
The bile acid transport system is a natural physiological cycling process between the liver and the small intestine, occurring approximately 6-15 times during the day. There are various bile acid transporter proteins on hepatocytes that specifically recognize bile acids for transport. Therefore, in this paper, a novel liposome, cholic acid-modified irinotecan hydrochloride liposomes (named CA-CPT-11-Lip), was prepared based on the "Trojan horse" strategy. The liposomes preparation process was optimized, and some important quality indicators were investigated. The distribution of irinotecan hydrochloride in mice was then analyzed by high-performance liquid chromatography (HPLC), and the toxicity of liposomes to hepatocellular carcinoma cells (HepG-2) was evaluated in vitro. As a result, CA-CPT-11-Lip was successfully prepared. It was spherical with a particle size of 154.16 ± 4.92 nm, and the drug loading and encapsulation efficiency were 3.72 ± 0.04% and 82.04 ± 1.38%, respectively. Compared with the conventional liposomes (without cholic acid modification, named CPT-11-Lip), CA-CPT-11-Lip had a smaller particle size and higher encapsulation efficiency, and the drug accumulation in the liver was more efficient, enhancing the anti-hepatocellular carcinoma activity of irinotecan hydrochloride. The novel nanoliposome modified by cholic acid may help to expand the application of irinotecan hydrochloride in the treatment of hepatocellular carcinoma and construct the drug delivery system mode of drug liver targeting.
Collapse
Affiliation(s)
- Tao Zhou
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
- College of Pharmacy, School of Modern Chinese Medicine Industry, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Yushi Liu
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
- College of Pharmacy, School of Modern Chinese Medicine Industry, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Kelu Lei
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
- College of Pharmacy, School of Modern Chinese Medicine Industry, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Junjing Liu
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
- College of Pharmacy, School of Modern Chinese Medicine Industry, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Minghao Hu
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
- College of Pharmacy, School of Modern Chinese Medicine Industry, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Li Guo
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
- College of Pharmacy, School of Modern Chinese Medicine Industry, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
| | - Yiping Guo
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
- College of Pharmacy, School of Modern Chinese Medicine Industry, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
- Correspondence: (Y.G.); (Q.Y.); Tel.: +86-13980570716 (Q.Y.)
| | - Qiang Ye
- State Key Laboratory of Southwestern Chinese Medicine Resources, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
- College of Pharmacy, School of Modern Chinese Medicine Industry, Chengdu University of Traditional Chinese Medicine, Chengdu 611137, China
- Correspondence: (Y.G.); (Q.Y.); Tel.: +86-13980570716 (Q.Y.)
| |
Collapse
|
11
|
Chan MJ, Hu CC, Huang WH, Hsu CW, Yen TH, Weng CH. An artificial intelligence algorithm for analyzing globus pallidus necrosis after carbon monoxide intoxication. Hum Exp Toxicol 2023; 42:9603271231190906. [PMID: 37491827 DOI: 10.1177/09603271231190906] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/27/2023]
Abstract
Globus pallidus necrosis (GPN) is one of typical neurological imaging features in patients with carbon monoxide (CO) poisoning. Current clinical guideline recommends neurological imaging examination for CO-intoxicated patients with conscious disturbance rather than routine screening, which may lead to undiagnosed GPN. We aimed to develop an artificial intelligence algorithm for predicting GPN in CO intoxication patients. We included CO intoxication patients with neurological images between 2000 and 2019 in Chang Gung Memorial Hospital. We collected 41 clinical and laboratory parameters on the first day of admission for algorithm development. We used fivefold cross validation and applied several machine learning algorithms. Random forest classifier (RFC) provided the best predictive performance in our cohort. Among the 261 patients with CO intoxication, 52 patients presented with GPN. The artificial intelligence algorithm using the RFC-based AI model achieved an accuracy = 79.2 ± 2.6%, sensitivity = 77.7%, precision score = 81.9 ± 3.4%, and F1 score = 73.2 ± 1.8%. The area under receiver operating characteristic was approximately 0.64. Top five weighted variables were Platelet count, carboxyhemoglobin, Glasgow Coma scale, creatinine, and hemoglobin. Our RFC-based algorithm is the first to predict GPN in patients with CO intoxication and provides fair predictive ability. Further studies are needed to validate our findings.
Collapse
Affiliation(s)
- Ming-Jen Chan
- Kidney Research Center, Department of Nephrology, Linkou Chang Gung Memorial Hospital, Tao-Yuan, Taiwan
- Clinical Poison Center, Chang Gung Memorial Hospital, Linkou Medical Center, Tao-Yuan, Taiwan
- College of Medicine, Chang Gung University, Tao-Yuan, Taiwan
- Graduate Institute of Clinical Medical Sciences, College of Medicine, Chang Gung University, Tao-Yuan, Taiwan
| | - Ching-Chih Hu
- College of Medicine, Chang Gung University, Tao-Yuan, Taiwan
- Department of Hepatogastroenterology and Liver Research Unit, Chang Gung Memorial Hospital, Keelung, Taiwan
| | - Wen-Hung Huang
- Kidney Research Center, Department of Nephrology, Linkou Chang Gung Memorial Hospital, Tao-Yuan, Taiwan
- Clinical Poison Center, Chang Gung Memorial Hospital, Linkou Medical Center, Tao-Yuan, Taiwan
- College of Medicine, Chang Gung University, Tao-Yuan, Taiwan
| | - Ching-Wei Hsu
- Kidney Research Center, Department of Nephrology, Linkou Chang Gung Memorial Hospital, Tao-Yuan, Taiwan
- Clinical Poison Center, Chang Gung Memorial Hospital, Linkou Medical Center, Tao-Yuan, Taiwan
- College of Medicine, Chang Gung University, Tao-Yuan, Taiwan
| | - Tzung-Hai Yen
- Kidney Research Center, Department of Nephrology, Linkou Chang Gung Memorial Hospital, Tao-Yuan, Taiwan
- Clinical Poison Center, Chang Gung Memorial Hospital, Linkou Medical Center, Tao-Yuan, Taiwan
- College of Medicine, Chang Gung University, Tao-Yuan, Taiwan
| | - Cheng-Hao Weng
- Kidney Research Center, Department of Nephrology, Linkou Chang Gung Memorial Hospital, Tao-Yuan, Taiwan
- Clinical Poison Center, Chang Gung Memorial Hospital, Linkou Medical Center, Tao-Yuan, Taiwan
- College of Medicine, Chang Gung University, Tao-Yuan, Taiwan
- Graduate Institute of Clinical Medical Sciences, College of Medicine, Chang Gung University, Tao-Yuan, Taiwan
| |
Collapse
|
12
|
Hepatic ROS Mediated Macrophage Activation Is Responsible for Irinotecan Induced Liver Injury. Cells 2022; 11:cells11233791. [PMID: 36497051 PMCID: PMC9739808 DOI: 10.3390/cells11233791] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 11/22/2022] [Accepted: 11/23/2022] [Indexed: 11/29/2022] Open
Abstract
Irinotecan is the first line chemotherapy drug used for treatment of metastatic colorectal cancer worldwide. There is increasing evidence suggesting that liver damage, including steatosis and steatohepatitis, can be caused during the treatment involving irinotecan. However, molecular mechanisms by which irinotecan-induced liver injury remain elusive. In this study, we found that irinotecan treatment caused significant elevation of ALT, inflammation, and fat accumulation in the liver, which are associated with hepatic macrophage activation. Depletion of macrophages by clodronate liposome improved irinotecan induced liver injury and inflammatory response in mice. In vitro data indicated that irinotecan induced intracellular ROS production in primary hepatocyte and upregulating of toll-like receptor (TLRs) family expression in macrophages. Supernatant from irinotecan treated hepatocyte triggered macrophage activation and upregulation of TLRs in macrophage, and N-acetylcysteine (NAC) abolished these effects. By using co-culture system, we further revealed that irinotecan activated macrophage induced impairment of lipid metabolism and promoted apoptosis in hepatocyte and NAC prevented macrophage-induced cell death and partially revered impaired lipid metabolism in hepatocytes. By using the irinotecan liver injury model, we demonstrated that combining NAC with irinotecan prevented irinotecan-induced macrophage activation, TLR upregulation, liver injury, and partially prevented the accumulation of triglycerides in liver. Our results thus indicated that macrophages play a critical role in irinotecan-induced liver injury, and targeting ROS provides new options for development of hepatoprotective drugs in clinical practice.
Collapse
|
13
|
Russo V, Lallo E, Munnia A, Spedicato M, Messerini L, D’Aurizio R, Ceroni EG, Brunelli G, Galvano A, Russo A, Landini I, Nobili S, Ceppi M, Bruzzone M, Cianchi F, Staderini F, Roselli M, Riondino S, Ferroni P, Guadagni F, Mini E, Peluso M. Artificial Intelligence Predictive Models of Response to Cytotoxic Chemotherapy Alone or Combined to Targeted Therapy for Metastatic Colorectal Cancer Patients: A Systematic Review and Meta-Analysis. Cancers (Basel) 2022; 14:4012. [PMID: 36011003 PMCID: PMC9406544 DOI: 10.3390/cancers14164012] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Revised: 07/26/2022] [Accepted: 08/12/2022] [Indexed: 12/24/2022] Open
Abstract
Tailored treatments for metastatic colorectal cancer (mCRC) have not yet completely evolved due to the variety in response to drugs. Therefore, artificial intelligence has been recently used to develop prognostic and predictive models of treatment response (either activity/efficacy or toxicity) to aid in clinical decision making. In this systematic review, we have examined the ability of learning methods to predict response to chemotherapy alone or combined with targeted therapy in mCRC patients by targeting specific narrative publications in Medline up to April 2022 to identify appropriate original scientific articles. After the literature search, 26 original articles met inclusion and exclusion criteria and were included in the study. Our results show that all investigations conducted on this field have provided generally promising results in predicting the response to therapy or toxic side-effects. By a meta-analytic approach we found that the overall weighted means of the area under the receiver operating characteristic (ROC) curve (AUC) were 0.90, 95% C.I. 0.80-0.95 and 0.83, 95% C.I. 0.74-0.89 in training and validation sets, respectively, indicating a good classification performance in discriminating response vs. non-response. The calculation of overall HR indicates that learning models have strong ability to predict improved survival. Lastly, the delta-radiomics and the 74 gene signatures were able to discriminate response vs. non-response by correctly identifying up to 99% of mCRC patients who were responders and up to 100% of patients who were non-responders. Specifically, when we evaluated the predictive models with tests reaching 80% sensitivity (SE) and 90% specificity (SP), the delta radiomics showed an SE of 99% and an SP of 94% in the training set and an SE of 85% and SP of 92 in the test set, whereas for the 74 gene signatures the SE was 97.6% and the SP 100% in the training set.
Collapse
Affiliation(s)
- Valentina Russo
- Research and Development Branch, Regional Cancer Prevention Laboratory, ISPRO-Study, Prevention and Oncology Network Institute, 50139 Florence, Italy
| | - Eleonora Lallo
- Research and Development Branch, Regional Cancer Prevention Laboratory, ISPRO-Study, Prevention and Oncology Network Institute, 50139 Florence, Italy
| | - Armelle Munnia
- Research and Development Branch, Regional Cancer Prevention Laboratory, ISPRO-Study, Prevention and Oncology Network Institute, 50139 Florence, Italy
| | - Miriana Spedicato
- Research and Development Branch, Regional Cancer Prevention Laboratory, ISPRO-Study, Prevention and Oncology Network Institute, 50139 Florence, Italy
| | - Luca Messerini
- Department of Experimental and Clinical Medicine, University of Florence, 50134 Florence, Italy
| | - Romina D’Aurizio
- Institute of Informatics and Telematics, National Research Council, 56124 Pisa, Italy
| | - Elia Giuseppe Ceroni
- Institute of Informatics and Telematics, National Research Council, 56124 Pisa, Italy
| | - Giulia Brunelli
- Institute of Informatics and Telematics, National Research Council, 56124 Pisa, Italy
| | - Antonio Galvano
- Department of Surgical, Oncological and Oral Sciences, University of Palermo, 90127 Palermo, Italy
| | - Antonio Russo
- Department of Surgical, Oncological and Oral Sciences, University of Palermo, 90127 Palermo, Italy
| | - Ida Landini
- Department of Health Sciences, University of Florence, 50139 Florence, Italy
| | - Stefania Nobili
- Department of Neurosciences, Imaging and Clinical Sciences, “G. D’Annunzio” Chieti-Pescara, 66100 Chieti, Italy
| | - Marcello Ceppi
- Clinical Epidemiology Unit, IRCCS-Ospedale Policlinico San Martino, 16131 Genova, Italy
| | - Marco Bruzzone
- Clinical Epidemiology Unit, IRCCS-Ospedale Policlinico San Martino, 16131 Genova, Italy
| | - Fabio Cianchi
- Department of Experimental and Clinical Medicine, University of Florence, 50134 Florence, Italy
| | - Fabio Staderini
- Department of Experimental and Clinical Medicine, University of Florence, 50134 Florence, Italy
| | - Mario Roselli
- Medical Oncology Unit, Department of Systems Medicine, Tor Vergata University, 00133 Rome, Italy
| | - Silvia Riondino
- Medical Oncology Unit, Department of Systems Medicine, Tor Vergata University, 00133 Rome, Italy
| | - Patrizia Ferroni
- BioBIM (InterInstitutional Multidisciplinary Biobank), IRCCS San Raffaele Roma, 00166 Rome, Italy
- Department of Human Sciences & Quality of Life Promotion, San Raffaele Roma Open University, 00166 Rome, Italy
| | - Fiorella Guadagni
- BioBIM (InterInstitutional Multidisciplinary Biobank), IRCCS San Raffaele Roma, 00166 Rome, Italy
- Department of Human Sciences & Quality of Life Promotion, San Raffaele Roma Open University, 00166 Rome, Italy
| | - Enrico Mini
- Department of Health Sciences, University of Florence, 50139 Florence, Italy
| | - Marco Peluso
- Research and Development Branch, Regional Cancer Prevention Laboratory, ISPRO-Study, Prevention and Oncology Network Institute, 50139 Florence, Italy
| |
Collapse
|
14
|
Yamada H, Ohmori R, Okada N, Nakamura S, Kagawa K, Fujii S, Miki H, Ishizawa K, Abe M, Sato Y. A machine learning model using SNPs obtained from a genome-wide association study predicts the onset of vincristine-induced peripheral neuropathy. THE PHARMACOGENOMICS JOURNAL 2022; 22:241-246. [PMID: 35752658 DOI: 10.1038/s41397-022-00282-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Revised: 05/10/2022] [Accepted: 06/01/2022] [Indexed: 06/15/2023]
Abstract
Vincristine treatment may cause peripheral neuropathy. In this study, we identified the genes associated with the development of peripheral neuropathy due to vincristine therapy using a genome-wide association study (GWAS) and constructed a predictive model for the development of peripheral neuropathy using genetic information-based machine learning. The study included 72 patients admitted to the Department of Hematology, Tokushima University Hospital, who received vincristine. Of these, 56 were genotyped using the Illumina Asian Screening Array-24 Kit, and a GWAS for the onset of peripheral neuropathy caused by vincristine was conducted. Using Sanger sequencing for 16 validation samples, the top three single nucleotide polymorphisms (SNPs) associated with the onset of peripheral neuropathy were determined. Machine learning was performed using the statistical software R package "caret". The 56 GWAS and 16 validation samples were used as the training and test sets, respectively. Predictive models were constructed using random forest, support vector machine, naive Bayes, and neural network algorithms. According to the GWAS, rs2110179, rs7126100, and rs2076549 were associated with the development of peripheral neuropathy on vincristine administration. Machine learning was performed using these three SNPs to construct a prediction model. A high accuracy of 93.8% was obtained with the support vector machine and neural network using rs2110179 and rs2076549. Thus, peripheral neuropathy development due to vincristine therapy can be effectively predicted by a machine learning prediction model using SNPs associated with it.
Collapse
Affiliation(s)
- Hiroki Yamada
- Department of Pharmaceutical Information Science, Tokushima University Graduate School of Biomedical Sciences, Tokushima, 770-8505, Japan
| | - Rio Ohmori
- Department of Pharmaceutical Information Science, Tokushima University Graduate School of Biomedical Sciences, Tokushima, 770-8505, Japan
| | - Naoto Okada
- Department of Pharmacy, Tokushima University Hospital, Tokushima, 770-8503, Japan
| | - Shingen Nakamura
- Department of Community Medicine and Medical Science, Tokushima University Graduate School of Biomedical Sciences, Tokushima, 770-8503, Japan
| | - Kumiko Kagawa
- Department of Hematology, Endocrinology and Metabolism, Tokushima University Graduate School of Biomedical Sciences, Tokushima, 770-8503, Japan
| | - Shiro Fujii
- Department of Hematology, Endocrinology and Metabolism, Tokushima University Graduate School of Biomedical Sciences, Tokushima, 770-8503, Japan
| | - Hirokazu Miki
- Division of Transfusion Medicine and Cell Therapy, Tokushima University Hospital, Tokushima, 770-8503, Japan
| | - Keisuke Ishizawa
- Department of Pharmacy, Tokushima University Hospital, Tokushima, 770-8503, Japan
- Department of Clinical Pharmacology and Therapeutics, Tokushima University Graduate School of Biomedical Sciences, Tokushima, 770-8503, Japan
- Clinical Research Center for Developmental Therapeutics, Tokushima University Hospital, Tokushima, 770-8503, Japan
| | - Masahiro Abe
- Department of Hematology, Endocrinology and Metabolism, Tokushima University Graduate School of Biomedical Sciences, Tokushima, 770-8503, Japan
| | - Youichi Sato
- Department of Pharmaceutical Information Science, Tokushima University Graduate School of Biomedical Sciences, Tokushima, 770-8505, Japan.
| |
Collapse
|
15
|
Awidi M, Bagga A. Artificial intelligence and machine learning in colorectal cancer. Artif Intell Gastrointest Endosc 2022; 3:31-43. [DOI: 10.37126/aige.v3.i3.31] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Revised: 03/24/2022] [Accepted: 06/20/2022] [Indexed: 02/06/2023] Open
Abstract
Colorectal cancer (CRC) is a heterogeneous illness characterized by various epigenetic and microenvironmental changes and is the third-highest cause of cancer-related death in the US. Artificial intelligence (AI) with its ability to allow automatic learning and improvement from experiences using statistical methods and Deep learning has made a distinctive contribution to the diagnosis and treatment of several cancer types. This review discusses the uses and application of AI in CRC screening using automated polyp detection assistance technologies to the development of computer-assisted diagnostic algorithms capable of accurately detecting polyps during colonoscopy and classifying them. Furthermore, we summarize the current research initiatives geared towards building computer-assisted diagnostic algorithms that aim at improving the diagnostic accuracy of benign from premalignant lesions. Considering the evolving transition to more personalized and tailored treatment strategies for CRC, the review also discusses the development of machine learning algorithms to understand responses to therapies and mechanisms of resistance as well as the future roles that AI applications may play in assisting in the treatment of CRC with the aim to improve disease outcomes. We also discuss the constraints and limitations of the use of AI systems. While the medical profession remains enthusiastic about the future of AI and machine learning, large-scale randomized clinical trials are needed to analyze AI algorithms before they can be used.
Collapse
Affiliation(s)
- Muhammad Awidi
- Internal Medicine, Beth Israel Lahey Health, Burlington, MA 01805, United States
| | - Arindam Bagga
- Internal Medicine, Tufts Medical Center, Boston, MA 02111, United States
| |
Collapse
|
16
|
Dhiman P, Ma J, Andaur Navarro CL, Speich B, Bullock G, Damen JAA, Hooft L, Kirtley S, Riley RD, Van Calster B, Moons KGM, Collins GS. Methodological conduct of prognostic prediction models developed using machine learning in oncology: a systematic review. BMC Med Res Methodol 2022; 22:101. [PMID: 35395724 PMCID: PMC8991704 DOI: 10.1186/s12874-022-01577-x] [Citation(s) in RCA: 34] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2021] [Accepted: 03/18/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Describe and evaluate the methodological conduct of prognostic prediction models developed using machine learning methods in oncology. METHODS We conducted a systematic review in MEDLINE and Embase between 01/01/2019 and 05/09/2019, for studies developing a prognostic prediction model using machine learning methods in oncology. We used the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) statement, Prediction model Risk Of Bias ASsessment Tool (PROBAST) and CHecklist for critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS) to assess the methodological conduct of included publications. Results were summarised by modelling type: regression-, non-regression-based and ensemble machine learning models. RESULTS Sixty-two publications met inclusion criteria developing 152 models across all publications. Forty-two models were regression-based, 71 were non-regression-based and 39 were ensemble models. A median of 647 individuals (IQR: 203 to 4059) and 195 events (IQR: 38 to 1269) were used for model development, and 553 individuals (IQR: 69 to 3069) and 50 events (IQR: 17.5 to 326.5) for model validation. A higher number of events per predictor was used for developing regression-based models (median: 8, IQR: 7.1 to 23.5), compared to alternative machine learning (median: 3.4, IQR: 1.1 to 19.1) and ensemble models (median: 1.7, IQR: 1.1 to 6). Sample size was rarely justified (n = 5/62; 8%). Some or all continuous predictors were categorised before modelling in 24 studies (39%). 46% (n = 24/62) of models reporting predictor selection before modelling used univariable analyses, and common method across all modelling types. Ten out of 24 models for time-to-event outcomes accounted for censoring (42%). A split sample approach was the most popular method for internal validation (n = 25/62, 40%). Calibration was reported in 11 studies. Less than half of models were reported or made available. CONCLUSIONS The methodological conduct of machine learning based clinical prediction models is poor. Guidance is urgently needed, with increased awareness and education of minimum prediction modelling standards. Particular focus is needed on sample size estimation, development and validation analysis methods, and ensuring the model is available for independent validation, to improve quality of machine learning based clinical prediction models.
Collapse
Affiliation(s)
- Paula Dhiman
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK.
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
| | - Jie Ma
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
| | - Constanza L Andaur Navarro
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Benjamin Speich
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
- Basel Institute for Clinical Epidemiology and Biostatistics, Department of Clinical Research, University Hospital Basel, University of Basel, Basel, Switzerland
| | - Garrett Bullock
- Nuffield Department of Orthopaedics, Rheumatology, and Musculoskeletal Sciences, University of Oxford, Oxford, UK
| | - Johanna A A Damen
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Lotty Hooft
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Shona Kirtley
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
| | - Richard D Riley
- Centre for Prognosis Research, School of Medicine, Keele University, Staffordshire, ST5 5BG, UK
| | - Ben Van Calster
- Department of Development and Regeneration, KU Leuven, Leuven, Belgium
- Department of Biomedical Data Sciences, Leiden University Medical Center, Leiden, the Netherlands
- EPI-centre, KU Leuven, Leuven, Belgium
| | - Karel G M Moons
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
- Cochrane Netherlands, University Medical Center Utrecht, Utrecht University, Utrecht, The Netherlands
| | - Gary S Collins
- Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, OX3 7LD, UK
- NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| |
Collapse
|
17
|
Farina E, Nabhen JJ, Dacoregio MI, Batalini F, Moraes FY. An overview of artificial intelligence in oncology. Future Sci OA 2022; 8:FSO787. [PMID: 35369274 PMCID: PMC8965797 DOI: 10.2144/fsoa-2021-0074] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2021] [Accepted: 01/19/2022] [Indexed: 11/23/2022] Open
Abstract
Cancer is associated with significant morbimortality globally. Advances in screening, diagnosis, management and survivorship were substantial in the last decades, however, challenges in providing personalized and data-oriented care remain. Artificial intelligence (AI), a branch of computer science used for predictions and automation, has emerged as potential solution to improve the healthcare journey and to promote precision in healthcare. AI applications in oncology include, but are not limited to, optimization of cancer research, improvement of clinical practice (eg., prediction of the association of multiple parameters and outcomes - prognosis and response) and better understanding of tumor molecular biology. In this review, we examine the current state of AI in oncology, including fundamentals, current applications, limitations and future perspectives.
Collapse
Affiliation(s)
- Eduardo Farina
- Department of Radiology, Federal University of São Paulo, SP, 04021-001, Brazil; Diagnósticos da America SA (Dasa), 05425-020, Brazil
| | - Jacqueline J Nabhen
- School of Medicine, Federal University of Paraná, Curitiba, PR, 80060-000, Brazil
| | - Maria Inez Dacoregio
- School of Medicine, State University of Centro-Oeste, Guarapuava, PR, 85040-167, Brazil
| | - Felipe Batalini
- Department of Medicine, Division of Medical Oncology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA 02215, USA
| | - Fabio Y Moraes
- Department of Oncology, Division of Radiation Oncology, Queen's University, Kingston, ON, K7L 3N6, Canada
| |
Collapse
|
18
|
Qiu H, Ding S, Liu J, Wang L, Wang X. Applications of Artificial Intelligence in Screening, Diagnosis, Treatment, and Prognosis of Colorectal Cancer. Curr Oncol 2022; 29:1773-1795. [PMID: 35323346 PMCID: PMC8947571 DOI: 10.3390/curroncol29030146] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2022] [Revised: 02/28/2022] [Accepted: 03/03/2022] [Indexed: 12/29/2022] Open
Abstract
Colorectal cancer (CRC) is one of the most common cancers worldwide. Accurate early detection and diagnosis, comprehensive assessment of treatment response, and precise prediction of prognosis are essential to improve the patients’ survival rate. In recent years, due to the explosion of clinical and omics data, and groundbreaking research in machine learning, artificial intelligence (AI) has shown a great application potential in clinical field of CRC, providing new auxiliary approaches for clinicians to identify high-risk patients, select precise and personalized treatment plans, as well as to predict prognoses. This review comprehensively analyzes and summarizes the research progress and clinical application value of AI technologies in CRC screening, diagnosis, treatment, and prognosis, demonstrating the current status of the AI in the main clinical stages. The limitations, challenges, and future perspectives in the clinical implementation of AI are also discussed.
Collapse
Affiliation(s)
- Hang Qiu
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China;
- School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
- Correspondence: (H.Q.); (X.W.)
| | - Shuhan Ding
- School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA;
| | - Jianbo Liu
- West China School of Medicine, Sichuan University, Chengdu 610041, China;
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
| | - Liya Wang
- Big Data Research Center, University of Electronic Science and Technology of China, Chengdu 611731, China;
| | - Xiaodong Wang
- West China School of Medicine, Sichuan University, Chengdu 610041, China;
- Department of Gastrointestinal Surgery, West China Hospital, Sichuan University, Chengdu 610041, China
- Correspondence: (H.Q.); (X.W.)
| |
Collapse
|
19
|
Matsuzaki T, Kato Y, Mizoguchi H, Yamada K. A machine learning model that emulates experts’ decision making in vancomycin initial dose planning. J Pharmacol Sci 2022; 148:358-363. [DOI: 10.1016/j.jphs.2022.02.005] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 01/26/2022] [Accepted: 02/14/2022] [Indexed: 10/19/2022] Open
|
20
|
PrACTiC: A Predictive Algorithm for Chemoradiotherapy-Induced Cytopenia in Glioblastoma Patients. JOURNAL OF ONCOLOGY 2022; 2022:1438190. [PMID: 35111223 PMCID: PMC8803420 DOI: 10.1155/2022/1438190] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Accepted: 01/06/2022] [Indexed: 11/18/2022]
Abstract
Background Chemotherapy-induced cytopenia is the most frequent side effect of chemoradiotherapy in glioblastoma patients which may lead to reduced delivery of treatment. This study aims to develop a predictive model that is able to forecast the cytopenia induced by temozolomide (TMZ) during concomitant chemoradiotherapy. Methods Medical records of 128 patients with newly diagnosed glioblastoma were evaluated to extract the baseline complete blood test before and during the six weeks of chemoradiotherapy to create a dataset for the development of ML models. Using the constructed dataset, different ML algorithms were trained and tested. Results Our proposed algorithm achieved accuracies of 85.6%, 88.7%, and 89.3% in predicting thrombocytopenia, lymphopenia, and neutropenia, respectively. Conclusions The algorithm designed and developed in this study, called PrACTiC, showed promising results in the accurate prediction of thrombocytopenia, neutropenia, and lymphopenia induced by TMZ in glioblastoma patients. PrACTiC can provide valuable insight for physicians and help them to make the necessary treatment modifications and prevent the toxicities.
Collapse
|
21
|
Pérez Santín E, Rodríguez Solana R, González García M, García Suárez MDM, Blanco Díaz GD, Cima Cabal MD, Moreno Rojas JM, López Sánchez JI. Toxicity prediction based on artificial intelligence: A multidisciplinary overview. WIRES COMPUTATIONAL MOLECULAR SCIENCE 2021. [DOI: 10.1002/wcms.1516] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Affiliation(s)
- Efrén Pérez Santín
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - Raquel Rodríguez Solana
- Department of Food Science and Health Andalusian Institute of Agricultural and Fisheries Research and Training (IFAPA), Alameda del Obispo Avda Córdoba, Andalucía Spain
| | - Mariano González García
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - María Del Mar García Suárez
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - Gerardo David Blanco Díaz
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - María Dolores Cima Cabal
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| | - José Manuel Moreno Rojas
- Department of Food Science and Health Andalusian Institute of Agricultural and Fisheries Research and Training (IFAPA), Alameda del Obispo Avda Córdoba, Andalucía Spain
| | - José Ignacio López Sánchez
- Escuela Superior de Ingeniería y Tecnología (ESIT) Universidad Internacional de La Rioja (UNIR) Logroño Spain
| |
Collapse
|
22
|
Khaiwa N, Maarouf NR, Darwish MH, Alhamad DWM, Sebastian A, Hamad M, Omar HA, Orive G, Al-Tel TH. Camptothecin's journey from discovery to WHO Essential Medicine: Fifty years of promise. Eur J Med Chem 2021; 223:113639. [PMID: 34175539 DOI: 10.1016/j.ejmech.2021.113639] [Citation(s) in RCA: 50] [Impact Index Per Article: 16.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Revised: 05/24/2021] [Accepted: 06/06/2021] [Indexed: 12/16/2022]
Abstract
Nature represents a rich source of compounds used for the treatment of many diseases. Camptothecin (CPT), isolated from the bark of Camptotheca acuminata, is a cytotoxic alkaloid that attenuates cancer cell replication by inhibiting DNA topoisomerase 1. Despite its promising and wide spectrum antiproliferative activity, its use is limited due to low solubility, instability, acquired tumour cell resistance, and remarkable toxicity. This has led to the development of numerous CPT analogues with improved pharmacodynamic and pharmacokinetic profiles. Three natural product-inspired drugs, namely, topotecan, irinotecan, and belotecan, are clinically approved and prescribed drugs for the treatment of several types of cancer, whereas other derivatives are in clinical trials. In this review, which covers literature from 2015 to 2020, we aim to provide a comprehensive overview and describe efforts that led to the development of a variety of CPT analogues. These efforts have led to the discovery of potent, first-in-class chemotherapeutic agents inspired by CPT. In addition, the mechanism of action, SAR studies, and recent advances of novel CPT drug delivery systems and antibody drug conjugates are discussed.
Collapse
Affiliation(s)
- Noura Khaiwa
- College of Pharmacy, University of Sharjah, 27272, Sharjah, United Arab Emirates
| | - Noor R Maarouf
- College of Pharmacy, University of Sharjah, 27272, Sharjah, United Arab Emirates
| | - Mhd H Darwish
- College of Pharmacy, University of Sharjah, 27272, Sharjah, United Arab Emirates
| | - Dima W M Alhamad
- Sharjah Institute for Medical Research, 27272, Sharjah, United Arab Emirates
| | - Anusha Sebastian
- Sharjah Institute for Medical Research, 27272, Sharjah, United Arab Emirates
| | - Mohamad Hamad
- Sharjah Institute for Medical Research, 27272, Sharjah, United Arab Emirates; College of Health Sciences, 27272, Sharjah, United Arab Emirates
| | - Hany A Omar
- College of Pharmacy, University of Sharjah, 27272, Sharjah, United Arab Emirates; Sharjah Institute for Medical Research, 27272, Sharjah, United Arab Emirates
| | - Gorka Orive
- NanoBioCel Group, Laboratory of Pharmaceutics, School of Pharmacy, University of the Basque Country (UPV/EHU), Paseo de la Universidad 7, 01006, Vitoria-Gasteiz, Spain
| | - Taleb H Al-Tel
- College of Pharmacy, University of Sharjah, 27272, Sharjah, United Arab Emirates; Sharjah Institute for Medical Research, 27272, Sharjah, United Arab Emirates.
| |
Collapse
|
23
|
Yen JS, Hu CC, Huang WH, Hsu CW, Yen TH, Weng CH. An artificial intelligence algorithm for analyzing acetaminophen-associated toxic hepatitis. Hum Exp Toxicol 2021; 40:1947-1954. [PMID: 33955253 DOI: 10.1177/09603271211014587] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
INTRODUCTION Very little artificial intelligence (AI) work has been performed to investigate acetaminophen-associated hepatotoxicity. The objective of this study was to develop an AI algorithm for analyzing weighted features for toxic hepatitis after acetaminophen poisoning. METHODS The medical records of 187 patients with acetaminophen poisoning treated at Chang Gung Memorial Hospital were reviewed. Patients were sorted into two groups according to their status of toxic hepatitis. A total of 40 clinical and laboratory features recorded on the first day of admission were selected for algorithm development. The random forest classifier (RFC) and logistic regression (LR) were used for artificial intelligence algorithm development. RESULTS The RFC-based AI model achieved the following results: accuracy = 92.5 ± 2.6%; sensitivity = 100%; specificity = 60%; precision = 92.3 ± 3.4%; and F1 = 96.0 ± 1.8%. The area under the receiver operating characteristic curve (AUROC) was approximately 0.98. The LR-based AI model achieved the following results: accuracy = 92.00 ± 2.9%; sensitivity = 100%; specificity = 20%; precision = 92.8 ± 3.4%; recall = 98.8 ± 3.4%; and F1 = 95.6 ± 1.5%. The AUROC was approximately 0.68. The weighted features were calculated, and the 10 most important weighted features for toxic hepatitis were aspartate aminotransferase (ALT), prothrombin time, alanine aminotransferase (AST), time to hospital, platelet count, lymphocyte count, albumin, total bilirubin, body temperature and acetaminophen level. CONCLUSION The top five weighted features for acetaminophen-associated toxic hepatitis were ALT, prothrombin time, AST, time to hospital and platelet count.
Collapse
Affiliation(s)
- J-S Yen
- Department of Nephrology and Clinical Poison Center, Chang Gung Memorial Hospital, Linkou
| | - C-C Hu
- College of Medicine, Chang Gung University, Taoyuan.,Department of Hepatogastroenterology and Liver Research Unit, Chang Gung Memorial Hospital, Keelung
| | - W-H Huang
- Department of Nephrology and Clinical Poison Center, Chang Gung Memorial Hospital, Linkou.,College of Medicine, Chang Gung University, Taoyuan.,Kidney Research Center, Chang Gung Memorial Hospital, Linkou
| | - C-W Hsu
- Department of Nephrology and Clinical Poison Center, Chang Gung Memorial Hospital, Linkou.,College of Medicine, Chang Gung University, Taoyuan.,Kidney Research Center, Chang Gung Memorial Hospital, Linkou
| | - T-H Yen
- Department of Nephrology and Clinical Poison Center, Chang Gung Memorial Hospital, Linkou.,College of Medicine, Chang Gung University, Taoyuan.,Kidney Research Center, Chang Gung Memorial Hospital, Linkou.,Center for Tissue Engineering, Chang Gung Memorial Hospital, Linkou
| | - C-H Weng
- Department of Nephrology and Clinical Poison Center, Chang Gung Memorial Hospital, Linkou.,College of Medicine, Chang Gung University, Taoyuan.,Kidney Research Center, Chang Gung Memorial Hospital, Linkou
| |
Collapse
|
24
|
Deyme L, Barbolosi D, Mbatchi LC, Tubiana-Mathieu N, Ychou M, Evrard A, Gattacceca F. Population pharmacokinetic model of irinotecan and its four main metabolites in patients treated with FOLFIRI or FOLFIRINOX regimen. Cancer Chemother Pharmacol 2021; 88:247-258. [PMID: 33912999 DOI: 10.1007/s00280-021-04255-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Accepted: 03/07/2021] [Indexed: 11/28/2022]
Abstract
PURPOSE The aim of the present study was to characterize the pharmacokinetics of irinotecan and its four main metabolites (SN-38, SN-38G, APC and NPC) in metastatic colorectal cancer patients treated with FOLFIRI and FOLFIRINOX regimens and to quantify and explain the inter-individual pharmacokinetic variability in this context. METHODS A multicenter study including 109 metastatic colorectal cancer patients treated with FOLFIRI or FOLFIRINOX regimen, associated or not with a monoclonal antibody, was conducted. Concentrations of irinotecan and its four main metabolites were measured in 506 blood samples during the first cycle of treatment. Collected data were analyzed using the population approach. First, fixed and random effects models were selected using statistical and graphical methods; second, the impact of covariates on pharmacokinetic parameters was evaluated to explain the inter-individual variability in pharmacokinetic parameters. RESULTS A seven-compartment model best described the pharmacokinetics of irinotecan and its four main metabolites. First-order rates were assigned to distribution, elimination, and metabolism processes, except for the transformation of irinotecan to NPC which was nonlinear. Addition of a direct conversion of NPC into SN-38 significantly improved the model. Co-administration of oxaliplatin significantly modified the distribution of SN-38. CONCLUSION To our knowledge, the present model is the first to allow a simultaneous description of irinotecan pharmacokinetics and of its four main metabolites. Moreover, a direct conversion of NPC into SN-38 had never been described before in a population pharmacokinetic model of irinotecan. The model will be useful to develop pharmacokinetic-pharmacodynamic models relating SN-38 concentrations to efficacy and digestive toxicities. CLINICAL TRIALS REGISTRATION NUMBER ClinicalTrials.gov identifier: NCT00559676.
Collapse
Affiliation(s)
- Laure Deyme
- SMARTc, Centre de Recherche en Cancérologie de Marseille (CRCM), Faculté de Pharmacie, INSERM U1068, CNRS UMR 7258, Aix-Marseille Université and Institut Paoli-Calmettes, Marseille, France.
| | - Dominique Barbolosi
- SMARTc, Centre de Recherche en Cancérologie de Marseille (CRCM), Faculté de Pharmacie, INSERM U1068, CNRS UMR 7258, Aix-Marseille Université and Institut Paoli-Calmettes, Marseille, France
| | - Litaty Céphanoée Mbatchi
- Laboratoire de Biochimie et Biologie Moléculaire, CHU Nîmes-Carémeau, Nîmes, France.,IRCM, Inserm U1194, Université de Montpellier, Montpellier, France
| | | | - Marc Ychou
- Institut Régional du Cancer de Montpellier (ICM)-Val d'Aurelle, Montpellier, France
| | - Alexandre Evrard
- Laboratoire de Biochimie et Biologie Moléculaire, CHU Nîmes-Carémeau, Nîmes, France.,IRCM, Inserm U1194, Université de Montpellier, Montpellier, France
| | - Florence Gattacceca
- SMARTc, Centre de Recherche en Cancérologie de Marseille (CRCM), Faculté de Pharmacie, INSERM U1068, CNRS UMR 7258, Aix-Marseille Université and Institut Paoli-Calmettes, Marseille, France
| |
Collapse
|
25
|
Yakar M, Etiz D. Artificial intelligence in rectal cancer. Artif Intell Gastroenterol 2021; 2:10-26. [DOI: 10.35712/aig.v2.i2.10] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/23/2021] [Revised: 03/03/2021] [Accepted: 03/16/2021] [Indexed: 02/06/2023] Open
Abstract
Accurate and rapid diagnosis is essential for correct treatment in rectal cancer. Determining the optimal treatment plan for a patient with rectal cancer is a complex process, and the oncological results and toxicity are not the same in every patient with the same treatment at the same stage. In recent years, the increasing interest in artificial intelligence in all fields of science has also led to the development of innovative tools in oncology. Artificial intelligence studies have increased in many steps from diagnosis to follow-up in rectal cancer. It is thought that artificial intelligence will provide convenience in many ways from personalized treatment to reducing the workload of the physician. Prediction algorithms can be standardized by sharing data between centers, diversifying data, and creating big data.
Collapse
Affiliation(s)
- Melek Yakar
- Department of Radiation Oncology, Eskisehir Osmangazi University Faculty of Medicine, Eskisehir 26040, Turkey
- Eskisehir Osmangazi University Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir 26040, Turkey
| | - Durmus Etiz
- Department of Radiation Oncology, Eskisehir Osmangazi University Faculty of Medicine, Eskisehir 26040, Turkey
- Eskisehir Osmangazi University Center of Research and Application for Computer Aided Diagnosis and Treatment in Health, Eskisehir 26040, Turkey
| |
Collapse
|
26
|
Alloro R, Sinagra E. Artificial intelligence and colorectal cancer: How far can you go? Artif Intell Cancer 2021; 2:7-11. [DOI: 10.35713/aic.v2.i2.7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/24/2021] [Revised: 04/01/2021] [Accepted: 04/20/2021] [Indexed: 02/06/2023] Open
Abstract
Artificial intelligence is an emerging technology whose application is rapidly increasing in several medical fields. The numerous applications of artificial intelligence in gastroenterology have shown promising results, especially in the setting of gastrointestinal oncology. Therefore, we would like to highlight and summarize the research progress and clinical application value of artificial intelligence in the diagnosis, treatment, and prognosis of colorectal cancer to provide evidence for its use as a promising diagnostic and therapeutic tool in this setting.
Collapse
Affiliation(s)
- Rita Alloro
- Department of Surgical, Oncological and Oral Sciences (Di.Chir.On.S.), Unit of General and Oncological Surgery, Paolo Giaccone University Hospital, University of Palermo, Palermo 90127, Italy
| | - Emanuele Sinagra
- Gastroenterology and Endoscopy Unit, Fondazione Istituto G. Giglio, Palermo 90015, Italy
| |
Collapse
|
27
|
Zhan M, Chen ZB, Ding CC, Qu Q, Wang GQ, Liu S, Wen FQ. Machine learning to predict high-dose methotrexate-related neutropenia and fever in children with B-cell acute lymphoblastic leukemia. Leuk Lymphoma 2021; 62:2502-2513. [PMID: 33899650 DOI: 10.1080/10428194.2021.1913140] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Methotrexate (MTX), an antimetabolite for the treatment of leukemia, could cause neutropenia and subsequently fever, which might lead to treatment delay and affect prognosis. Here, we aimed to predict neutropenia and fever related to high-dose MTX using artificial intelligence. This study included 139 pediatric patients newly diagnosed with standard- or intermediate risk B-cell acute lymphoblastic leukemia. Fifty-seven SNPs of 16 genes were genotyped. Univariate and multivariate analysis were used to select SNPs and clinical covariates for model developing. Five machine learning algorithms combined with four resampling techniques were used to build optimal predictive model. The combination of random forest with adaptive synthetic appeared to be the best model for neutropenia (sensitivity = 0.935, specificity = 0.920, AUC = 0.927) and performed best for fever (sensitivity = 0.818, specificity = 0.924, AUC = 0.870). By machine learning, we have developed and validated comprehensive models to predict the risk of neutropenia and fever. Such models may be helpful for medical oncologists in quick decision-making.
Collapse
Affiliation(s)
- Min Zhan
- Department of Pharmacy, Shenzhen Children's Hospital, Shenzhen, People's Republic of China
| | - Ze-Bin Chen
- Department of Pharmacy, Shenzhen Children's Hospital, Shenzhen, People's Republic of China
| | - Chang-Cai Ding
- Department of Research and Development, Shenzhen Advanced precision medical CO., LTD, Shenzhen, People's Republic of China
| | - Qiang Qu
- Department of Pharmacy, Xiangya Hospital, Central South University, Changsha, People's Republic of China
| | - Guo-Qiang Wang
- Department of Pharmacy, Shenzhen Children's Hospital, Shenzhen, People's Republic of China
| | - Sixi Liu
- Department of Hematology/Oncology, Shenzhen Children's Hospital, Shenzhen, People's Republic of China
| | - Fei-Qiu Wen
- Department of Hematology/Oncology, Shenzhen Children's Hospital, Shenzhen, People's Republic of China
| |
Collapse
|
28
|
Mitsala A, Tsalikidis C, Pitiakoudis M, Simopoulos C, Tsaroucha AK. Artificial Intelligence in Colorectal Cancer Screening, Diagnosis and Treatment. A New Era. ACTA ACUST UNITED AC 2021; 28:1581-1607. [PMID: 33922402 PMCID: PMC8161764 DOI: 10.3390/curroncol28030149] [Citation(s) in RCA: 77] [Impact Index Per Article: 25.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2021] [Revised: 04/09/2021] [Accepted: 04/20/2021] [Indexed: 12/24/2022]
Abstract
The development of artificial intelligence (AI) algorithms has permeated the medical field with great success. The widespread use of AI technology in diagnosing and treating several types of cancer, especially colorectal cancer (CRC), is now attracting substantial attention. CRC, which represents the third most commonly diagnosed malignancy in both men and women, is considered a leading cause of cancer-related deaths globally. Our review herein aims to provide in-depth knowledge and analysis of the AI applications in CRC screening, diagnosis, and treatment based on current literature. We also explore the role of recent advances in AI systems regarding medical diagnosis and therapy, with several promising results. CRC is a highly preventable disease, and AI-assisted techniques in routine screening represent a pivotal step in declining incidence rates of this malignancy. So far, computer-aided detection and characterization systems have been developed to increase the detection rate of adenomas. Furthermore, CRC treatment enters a new era with robotic surgery and novel computer-assisted drug delivery techniques. At the same time, healthcare is rapidly moving toward precision or personalized medicine. Machine learning models have the potential to contribute to individual-based cancer care and transform the future of medicine.
Collapse
Affiliation(s)
- Athanasia Mitsala
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
- Correspondence: ; Tel.: +30-6986423707
| | - Christos Tsalikidis
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
| | - Michail Pitiakoudis
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
| | - Constantinos Simopoulos
- Second Department of Surgery, University General Hospital of Alexandroupolis, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece; (C.T.); (M.P.); (C.S.)
| | - Alexandra K. Tsaroucha
- Laboratory of Experimental Surgery & Surgical Research, Democritus University of Thrace Medical School, Dragana, 68100 Alexandroupolis, Greece;
| |
Collapse
|
29
|
Wang Y, Nie H, He X, Liao Z, Zhou Y, Zhou J, Ou C. The emerging role of super enhancer-derived noncoding RNAs in human cancer. Theranostics 2020; 10:11049-11062. [PMID: 33042269 PMCID: PMC7532672 DOI: 10.7150/thno.49168] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2020] [Accepted: 08/23/2020] [Indexed: 02/06/2023] Open
Abstract
Super enhancers (SEs) are large clusters of adjacent enhancers that drive the expression of genes which regulate cellular identity; SE regions can be enriched with a high density of transcription factors, co-factors, and enhancer-associated epigenetic modifications. Through enhanced activation of their target genes, SEs play an important role in various diseases and conditions, including cancer. Recent studies have shown that SEs not only activate the transcriptional expression of coding genes to directly regulate biological functions, but also drive the transcriptional expression of non-coding RNAs (ncRNAs) to indirectly regulate biological functions. SE-derived ncRNAs play critical roles in tumorigenesis, including malignant proliferation, metastasis, drug resistance, and inflammatory response. Moreover, the abnormal expression of SE-derived ncRNAs is closely related to the clinical and pathological characterization of tumors. In this review, we summarize the functions and roles of SE-derived ncRNAs in tumorigenesis and discuss their prospective applications in tumor therapy. A deeper understanding of the potential mechanism underlying the action of SE-derived ncRNAs in tumorigenesis may provide new strategies for the early diagnosis of tumors and targeted therapy.
Collapse
|
30
|
Cuplov V, André N. Machine Learning Approach to Forecast Chemotherapy-Induced Haematological Toxicities in Patients with Rhabdomyosarcoma. Cancers (Basel) 2020; 12:cancers12071944. [PMID: 32709121 PMCID: PMC7409066 DOI: 10.3390/cancers12071944] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2020] [Revised: 07/08/2020] [Accepted: 07/14/2020] [Indexed: 01/09/2023] Open
Abstract
Developing precision medicine is a major trend in clinical oncology. The main adverse effects of ifosfamide, actinomycin D and vincristine (IVA) treatment for rhabdomyosarcoma are haematological toxicities such as neutropenia or thrombocytopenia. The severity of these effects vary among patients but their dynamic profiles are similar. A non-empirical adjustment of the chemotherapy dose to avoid severe toxicities could help secure the treatment administration. Twenty-four patients with rhabdomyosarcoma treated with IVA chemotherapy courses were selected. Before and during each cycle, routine multiple blood cell counts were performed allowing for a dynamic study of the haematological toxicities. We developed a machine learning analysis using a gradient boosting regression technique to forecast the ifosfamide induced haematological toxicities as a function of neutrophils and platelets initial levels and the initial ifosfamide dose. To validate models’ accuracy, predicted and observed neutrophils and platelets levels were compared. The model was able to reproduce the dynamic profiles of the haematological toxicities. Among all cycles, the mean absolute errors between predicted and observed neutrophils and platelets levels were 1.0 and 72.8 G/L, respectively. Adjusting a patient’s ifosfamide dose based upon the predicted haematological toxicity levels at the end of a treatment cycle could enable tailored treatment.
Collapse
Affiliation(s)
- Vesna Cuplov
- SMARTc, Marseille Cancer Research Center (CRCM), UMR Inserm 1068, CNRS UMR 7258, Aix Marseille Université U105, Institut Paoli Calmettes & APHM, 13385 Marseille, France
- Correspondence:
| | - Nicolas André
- Paediatric Haematology and Oncology Department, La Timone Children’s Hospital, AP-HM, 13385 Marseille, France;
| |
Collapse
|
31
|
Abstract
Twenty-five years ago, the cytotoxic drug irinotecan (IRT) was first approved in Japan for the treatment of cancer. For more than two decades, the IRT prodrug has largely contributed to the treatment of solid tumors worldwide. Nowadays, this camptothecin derivative targeting topoisomerase 1 remains largely used in combination regimen, like FOLFIRI and FOLFIRINOX, to treat metastatic or advanced solid tumors, such as colon, gastric and pancreatic cancers and others. This review highlights recent discoveries in the field of IRT and its derivatives, including analogues of the active metabolite SN38 (such as FL118), the recently approved liposomal form Nal-IRI and SN38-based immuno-conjugates currently in development (such as sacituzumab govitecan). New information about the IRT mechanism of action are presented, including the discovery of a new protein target, the single-stranded DNA-binding protein FUBP1. Significant progress has been made also to better understand and manage the main limiting toxicities of IRT, chiefly neutropenia and diarrhea. The role of drug-induced inflammation and dysbiosis is underlined and strategies to limit the intestinal toxicity of IRT are discussed (use of β-glucuronidase inhibitors, plant extracts, probiotics). The detailed knowledge of the metabolism of IRT has enabled the identification of potential biomarkers to guide patient selection and to limit drug-induced toxicities, but no robust IRT-specific therapeutic biomarker has been approved yet. IRT is a versatile chemotherapeutic agent which combines well with a variety of anticancer drugs. It offers a large range of drug combinations with cytotoxic agents, targeted products and immuno-active biotherapeutics, to treat a variety of advanced solid carcinoma, sarcoma and cancers with progressive central nervous system diseases. A quarter of century after its first launch, IRT remains an essential anticancer drug, largely prescribed, useful to many patients and scientifically inspiring.
Collapse
|