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Vera-Salmerón E, Domínguez-Nogueira C, Sáez JA, Romero-Béjar JL, Mota-Romero E. Differentiating Pressure Ulcer Risk Levels through Interpretable Classification Models Based on Readily Measurable Indicators. Healthcare (Basel) 2024; 12:913. [PMID: 38727470 PMCID: PMC11083727 DOI: 10.3390/healthcare12090913] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2024] [Revised: 04/24/2024] [Accepted: 04/25/2024] [Indexed: 05/13/2024] Open
Abstract
Pressure ulcers carry a significant risk in clinical practice. This paper proposes a practical and interpretable approach to estimate the risk levels of pressure ulcers using decision tree models. In order to address the common problem of imbalanced learning in nursing classification datasets, various oversampling configurations are analyzed to improve the data quality prior to modeling. The decision trees built are based on three easily identifiable and clinically relevant pressure ulcer risk indicators: mobility, activity, and skin moisture. Additionally, this research introduces a novel tabular visualization method to enhance the usability of the decision trees in clinical practice. Thus, the primary aim of this approach is to provide nursing professionals with valuable insights for assessing the potential risk levels of pressure ulcers, which could support their decision-making and allow, for example, the application of suitable preventive measures tailored to each patient's requirements. The interpretability of the models proposed and their performance, evaluated through stratified cross-validation, make them a helpful tool for nursing care in estimating the pressure ulcer risk level.
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Affiliation(s)
- Eugenio Vera-Salmerón
- Servicio Andaluz de Salud, Distrito Sanitario Granada-Metropolitano, Centro de Salud Dr. Salvador Caballero de Granada, 18012 Granada, Spain; (E.V.-S.); (E.M.-R.)
- Instituto de Investigación Biosanitaria (ibs.GRANADA), 18014 Granada, Spain
| | - Carmen Domínguez-Nogueira
- Inspección Provincial de Servicios Sanitarios, Delegación Territorial de Granada, Consejería de Salud y Familias de la Junta de Andalucía, 41071 Sevilla, Spain;
| | - José A. Sáez
- Department of Statistics and Operations Research, University of Granada, Fuente Nueva s/n, 18071 Granada, Spain;
| | - José L. Romero-Béjar
- Instituto de Investigación Biosanitaria (ibs.GRANADA), 18014 Granada, Spain
- Department of Statistics and Operations Research, University of Granada, Fuente Nueva s/n, 18071 Granada, Spain;
- Institute of Mathematics, University of Granada (IMAG), Ventanilla 11, 18001 Granada, Spain
| | - Emilio Mota-Romero
- Servicio Andaluz de Salud, Distrito Sanitario Granada-Metropolitano, Centro de Salud Dr. Salvador Caballero de Granada, 18012 Granada, Spain; (E.V.-S.); (E.M.-R.)
- Instituto de Investigación Biosanitaria (ibs.GRANADA), 18014 Granada, Spain
- Department of Nursing, University of Granada, Avda. Ilustración 60, 18071 Granada, Spain
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Bang C, Bernard G, Le WT, Lalonde A, Kadoury S, Bahig H. Artificial intelligence to predict outcomes of head and neck radiotherapy. Clin Transl Radiat Oncol 2023; 39:100590. [PMID: 36935854 PMCID: PMC10014342 DOI: 10.1016/j.ctro.2023.100590] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Revised: 01/28/2023] [Accepted: 01/28/2023] [Indexed: 02/01/2023] Open
Abstract
Head and neck radiotherapy induces important toxicity, and its efficacy and tolerance vary widely across patients. Advancements in radiotherapy delivery techniques, along with the increased quality and frequency of image guidance, offer a unique opportunity to individualize radiotherapy based on imaging biomarkers, with the aim of improving radiation efficacy while reducing its toxicity. Various artificial intelligence models integrating clinical data and radiomics have shown encouraging results for toxicity and cancer control outcomes prediction in head and neck cancer radiotherapy. Clinical implementation of these models could lead to individualized risk-based therapeutic decision making, but the reliability of the current studies is limited. Understanding, validating and expanding these models to larger multi-institutional data sets and testing them in the context of clinical trials is needed to ensure safe clinical implementation. This review summarizes the current state of the art of machine learning models for prediction of head and neck cancer radiotherapy outcomes.
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Key Words
- ADASYN, adaptive synthetic sampling
- AI, artificial intelligence
- ANN, artificial neural network
- AUC, Area Under the ROC Curve
- Artificial intelligence
- BMI, body mass index
- C-Index, concordance index
- CART, Classification and Regression Tree
- CBCT, cone-beam computed tomography
- CIFE, conditional informax feature extraction
- CNN, convolutional neural network
- CRT, chemoradiation
- CT, computed tomography
- Cancer outcomes
- DL, deep learning
- DM, distant metastasis
- DSC, Dice Similarity Coefficient
- DSS, clinical decision support systems
- DT, Decision Tree
- DVH, Dose-volume histogram
- GANs, Generative Adversarial Networks
- GB, Gradient boosting
- GPU, graphical process units
- HNC, head and neck cancer
- HPV, human papillomavirus
- HR, hazard ratio
- Head and neck cancer
- IAMB, incremental association Markov blanket
- IBDM, image based data mining
- IBMs, image biomarkers
- IMRT, intensity-modulated RT
- KNN, k nearest neighbor
- LLR, Local linear forest
- LR, logistic regression
- LRR, loco-regional recurrence
- MIFS, mutual information based feature selection
- ML, machine learning
- MRI, Magnetic resonance imaging
- MRMR, Minimum redundancy feature selection
- Machine learning
- N-MLTR, Neural Multi-Task Logistic Regression
- NPC, nasopharynx
- NTCP, Normal Tissue Complication Probability
- OPC, oropharyngeal cancer
- ORN, osteoradionecrosis
- OS, overall survival
- PCA, Principal component analysis
- PET, Positron emission tomography
- PG, parotid glands
- PLR, Positive likelihood ratio
- PM, pharyngeal mucosa
- PTV, Planning target volumes
- PreSANet, deep preprocessor module and self-attention
- Predictive modeling
- QUANTEC, Quantitative Analyses of Normal Tissue Effects in the Clinic
- RF, random forest
- RFC, random forest classifier
- RFS, recurrence free survival
- RLR, Rigid logistic regression
- RRF, Regularized random forest
- RSF, random survival forest
- RT, radiotherapy
- RTLI, radiation-induced temporal lobe injury
- Radiomic
- SDM, shared decision making
- SMG, submandibular glands
- SMOTE, synthetic minority over-sampling technique
- STIC, sticky saliva
- SVC, support vector classifier
- SVM, support vector machine
- XGBoost, extreme gradient boosting
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Affiliation(s)
- Chulmin Bang
- Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
- Corresponding author at: Centre Hospitalier de l'Université de Montréal, 3840 Rue Saint-Urbain, Montréal, QC H2W 1T8, Canada.
| | - Galaad Bernard
- Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
| | - William T. Le
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
- Polytechnique Montréal, Montreal, QC, Canada
| | - Arthur Lalonde
- Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
- Université de Montréal, Montreal, QC, Canada
| | - Samuel Kadoury
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
- Polytechnique Montréal, Montreal, QC, Canada
| | - Houda Bahig
- Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
- Centre de recherche du Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
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Blake CL, Brown TE, Pelecanos A, Moroney LB, Helios J, Hughes BGM, Chua B, Kenny LM. Enteral nutrition support and treatment toxicities in patients with head and neck cancer receiving definitive or adjuvant helical intensity-modulated radiotherapy with concurrent chemotherapy. Head Neck 2023; 45:417-430. [PMID: 36433667 DOI: 10.1002/hed.27249] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2021] [Revised: 09/10/2022] [Accepted: 11/11/2022] [Indexed: 11/27/2022] Open
Abstract
BACKGROUND Enteral nutrition (EN) is often required in patients with head and neck cancer (HNSCC); however, initiation criteria is limited or inconsistent. This study aimed to describe the relationship of treatment toxicities and requirement for EN and investigate toxicity and baseline characteristics association with EN duration. METHODS Acute toxicities and baseline characteristics were collected from patients with HNSCC (n = 110) undergoing H-IMRT. Percentage EN contributing to estimated requirements and EN duration were measured. RESULTS The threshold for patients needing ≥50% of estimated requirements via EN increased from week 3 to 4 for grade ≥2 oral/pharyngeal mucositis, dysgeusia, thick saliva and nausea, and for grade 3 dysphagia. Patients with grade 2-3 dysphagia had a reduced risk of ceasing EN compared to those with grade 0-1 dysphagia. CONCLUSIONS Using acute toxicities in clinical practice may be a useful tool to inform prompt initiation of EN prior to decline in nutritional status and anticipate EN duration.
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Affiliation(s)
- Claire L Blake
- Department of Nutrition and Dietetics, Royal Brisbane & Women's Hospital, Brisbane, Queensland, Australia
| | - Teresa E Brown
- Department of Nutrition and Dietetics, Royal Brisbane & Women's Hospital, Brisbane, Queensland, Australia
| | - Anita Pelecanos
- QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Laura B Moroney
- Department of Speech Pathology and Audiology, Royal Brisbane & Women's Hospital, Brisbane, Queensland, Australia
| | - Jennifer Helios
- Department of Speech Pathology and Audiology, Royal Brisbane & Women's Hospital, Brisbane, Queensland, Australia
| | - Brett G M Hughes
- Cancer Care Services, Royal Brisbane & Women's Hospital, Brisbane, Queensland, Australia.,School of Medicine, The University of Queensland, Brisbane, Queensland, Australia
| | - Benjamin Chua
- Cancer Care Services, Royal Brisbane & Women's Hospital, Brisbane, Queensland, Australia.,School of Medicine, The University of Queensland, Brisbane, Queensland, Australia
| | - Lizbeth M Kenny
- Cancer Care Services, Royal Brisbane & Women's Hospital, Brisbane, Queensland, Australia.,School of Medicine, The University of Queensland, Brisbane, Queensland, Australia
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Fang SL, Tu YK, Kang L, Chen HW, Chang TJ, Yao MH, Kuo BJ. CART model to classify the drought status of diverse tomato genotypes by VPD, air temperature, and leaf-air temperature difference. Sci Rep 2023; 13:602. [PMID: 36635417 DOI: 10.1038/s41598-023-27798-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/16/2022] [Accepted: 01/09/2023] [Indexed: 01/14/2023] Open
Abstract
Regular water management is crucial for the cultivation of tomato (Solanum lycopersicum L.). Inadequate irrigation leads to water stress and a reduction in tomato yield and quality. Therefore, it is important to develop an efficient classification method of the drought status of tomato for the timely application of irrigation. In this study, a simple classification and regression tree (CART) model that includes air temperature, vapor pressure deficit, and leaf-air temperature difference was established to classify the drought status of three tomato genotypes (i.e., cherry type 'Tainan ASVEG No. 19', large fruits breeding line '108290', and wild accession 'LA2093'). The results indicate that the proposed CART model exhibited a higher predictive sensitivity, specificity, geometric mean, and accuracy performance compared to the logistic model. In addition, the CART model was applicable not only to three tomato genotypes but across vegetative and reproductive stages. Furthermore, while the drought status was divided into low, medium, and high, the CART model provided a higher predictive performance than that of the logistic model. The results suggest that the drought status of tomato can be accurately classified by the proposed CART model. These results will provide a useful tool of the regular water management for tomato cultivation.
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Reche C, Pérez N, Alastuey A, Cots N, Pérez E, Querol X. 2011-2020 trends of urban and regional ammonia in and around Barcelona, NE Spain. Chemosphere 2022; 304:135347. [PMID: 35714951 DOI: 10.1016/j.chemosphere.2022.135347] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 06/08/2022] [Accepted: 06/12/2022] [Indexed: 06/15/2023]
Abstract
It is well established that in environments where NH3 abundance is limiting in secondary PM2.5 generation, a reduction of NH3 emissions can result in an important contribution to air quality control. However, as deduced from open data published by the European Environmental Agency, the availability of measurements of NH3 concentrations is very scarce, with very few countries in Europe reporting data consistently for extensive periods, this being especially true for urban background sites. In this framework, simultaneous multi-site measurements were carried out in NE (Northeast) Spain from 2011 to 2020, using diffusion tubes. The highest NH3 concentrations were recorded at the traffic site (5.3 μgm-3 on average), followed by those measured at the urban background site (2.1 μgm-3). Mean concentrations at the mountain site were 1.6 μgm-3, while the lowest concentrations were recorded at the regional site (0.9 μgm-3). This comparison highlights traffic emissions as an important source of NH3. A statistically significant time trend of this pollutant was observed at the urban background site, increasing by 9.4% per year. A season-separated analysis also revealed a significant increasing trend at the mountain site during summer periods, probably related with increasing emissions from agricultural/livestock activities. These increases in NH3 concentrations were hypothesized to be responsible for the lack of a decreasing trend of NO3- concentrations at the monitoring sites, in spite of a markedly reduction of NO2 during the period, especially at the urban background. Thus, this would in turn affect the effectiveness of current action plans to abate fine aerosols, largely made up of secondary compounds. Actions to reduce NH3 concentrations at urban backgrounds are challenging though, as predicting NH3 is subjected to a high uncertainty and complexity due to its dependence on a variety of factors. This complexity was clearly indicated by the application of a decision tree algorithm to find the parameters better predicting NH3 at the urban background under study. O3, NO, NO2, CO, SO2 and OM + EC concentrations, together with meteorological indicators, were used as independent variables, obtaining no combination of parameters evidently able to predict significant differences in NH3 concentrations, with a coefficient of determination between real and predicted measurements lower than 0.50. This emphasizes the need for highly temporally and spatially resolved NH3 measurements for an accurate design of abatement actions.
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Affiliation(s)
- C Reche
- IDAEA-CSIC, Barcelona, Spain.
| | - N Pérez
- IDAEA-CSIC, Barcelona, Spain
| | | | - N Cots
- Departament de Territori i Sostenibilitat, Generalitat de Catalunya, Barcelona, Spain
| | - E Pérez
- Departament de Territori i Sostenibilitat, Generalitat de Catalunya, Barcelona, Spain
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Volpe S, Pepa M, Zaffaroni M, Bellerba F, Santamaria R, Marvaso G, Isaksson LJ, Gandini S, Starzyńska A, Leonardi MC, Orecchia R, Alterio D, Jereczek-Fossa BA. Machine Learning for Head and Neck Cancer: A Safe Bet?-A Clinically Oriented Systematic Review for the Radiation Oncologist. Front Oncol 2021; 11:772663. [PMID: 34869010 PMCID: PMC8637856 DOI: 10.3389/fonc.2021.772663] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Accepted: 10/25/2021] [Indexed: 12/17/2022] Open
Abstract
BACKGROUND AND PURPOSE Machine learning (ML) is emerging as a feasible approach to optimize patients' care path in Radiation Oncology. Applications include autosegmentation, treatment planning optimization, and prediction of oncological and toxicity outcomes. The purpose of this clinically oriented systematic review is to illustrate the potential and limitations of the most commonly used ML models in solving everyday clinical issues in head and neck cancer (HNC) radiotherapy (RT). MATERIALS AND METHODS Electronic databases were screened up to May 2021. Studies dealing with ML and radiomics were considered eligible. The quality of the included studies was rated by an adapted version of the qualitative checklist originally developed by Luo et al. All statistical analyses were performed using R version 3.6.1. RESULTS Forty-eight studies (21 on autosegmentation, four on treatment planning, 12 on oncological outcome prediction, 10 on toxicity prediction, and one on determinants of postoperative RT) were included in the analysis. The most common imaging modality was computed tomography (CT) (40%) followed by magnetic resonance (MR) (10%). Quantitative image features were considered in nine studies (19%). No significant differences were identified in global and methodological scores when works were stratified per their task (i.e., autosegmentation). DISCUSSION AND CONCLUSION The range of possible applications of ML in the field of HN Radiation Oncology is wide, albeit this area of research is relatively young. Overall, if not safe yet, ML is most probably a bet worth making.
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Affiliation(s)
- Stefania Volpe
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Matteo Pepa
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Mattia Zaffaroni
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Federica Bellerba
- Molecular and Pharmaco-Epidemiology Unit, Department of Experimental Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Riccardo Santamaria
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Giulia Marvaso
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
| | - Lars Johannes Isaksson
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Sara Gandini
- Molecular and Pharmaco-Epidemiology Unit, Department of Experimental Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Anna Starzyńska
- Department of Oral Surgery, Medical University of Gdańsk, Gdańsk, Poland
| | - Maria Cristina Leonardi
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Roberto Orecchia
- Scientific Directorate, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Daniela Alterio
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
| | - Barbara Alicja Jereczek-Fossa
- Division of Radiation Oncology, European Institute of Oncology (IEO) Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS), Milan, Italy
- Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
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Pozoulakis EC, Cheng Z, Han P, Quon H. Radiation-Induced Skin Dermatitis: Treatment With CamWell® Herb to Soothe® Cream in Patients With Head and Neck Cancer Receiving Radiation Therapy. Clin J Oncol Nurs 2021; 25:E44-E49. [PMID: 34269339 DOI: 10.1188/21.cjon.e44-e49] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND Radiation-induced skin dermatitis (RISD) is a common outcome experienced by adult patients with head and neck cancer (HNC) who have undergone radiation therapy. There is no standardized recommended agent for the prevention or management of RISD. OBJECTIVES The primary objective of this study was to retrospectively evaluate for effectiveness of a botanical topical agent, CamWell® Herb to Soothe® cream, on RISD. METHODS 112 patients with HNC undergoing radiation therapy self-reported their RISD topical skin care agent during treatment as standard of care, CamWell used prophylactically, or CamWell use started after the first week of treatment. The primary endpoint was impact of RISD on the patient, as measured by mean Skindex-16 score throughout treatment. Measures were completed weekly. FINDINGS The mean Skindex score was statistically significantly lower for the prophylactic group than for the standard-of-care group. CamWell may have played a role in managing RISD when compared to standard-of-care agents.
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Roy S, Whitehead TD, Li S, Ademuyiwa FO, Wahl RL, Dehdashti F, Shoghi KI. Co-clinical FDG-PET radiomic signature in predicting response to neoadjuvant chemotherapy in triple-negative breast cancer. Eur J Nucl Med Mol Imaging 2021; 49:550-562. [PMID: 34328530 PMCID: PMC8800941 DOI: 10.1007/s00259-021-05489-8] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 07/04/2021] [Indexed: 02/07/2023]
Abstract
Purpose We sought to exploit the heterogeneity afforded by patient-derived tumor xenografts (PDX) to first, optimize and identify robust radiomic features to predict response to therapy in subtype-matched triple negative breast cancer (TNBC) PDX, and second, to implement PDX-optimized image features in a TNBC co-clinical study to predict response to therapy using machine learning (ML) algorithms. Methods TNBC patients and subtype-matched PDX were recruited into a co-clinical FDG-PET imaging trial to predict response to therapy. One hundred thirty-one imaging features were extracted from PDX and human-segmented tumors. Robust image features were identified based on reproducibility, cross-correlation, and volume independence. A rank importance of predictors using ReliefF was used to identify predictive radiomic features in the preclinical PDX trial in conjunction with ML algorithms: classification and regression tree (CART), Naïve Bayes (NB), and support vector machines (SVM). The top four PDX-optimized image features, defined as radiomic signatures (RadSig), from each task were then used to predict or assess response to therapy. Performance of RadSig in predicting/assessing response was compared to SUVmean, SUVmax, and lean body mass-normalized SULpeak measures. Results Sixty-four out of 131 preclinical imaging features were identified as robust. NB-RadSig performed highest in predicting and assessing response to therapy in the preclinical PDX trial. In the clinical study, the performance of SVM-RadSig and NB-RadSig to predict and assess response was practically identical and superior to SUVmean, SUVmax, and SULpeak measures. Conclusions We optimized robust FDG-PET radiomic signatures (RadSig) to predict and assess response to therapy in the context of a co-clinical imaging trial. Supplementary Information The online version contains supplementary material available at 10.1007/s00259-021-05489-8.
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Affiliation(s)
- Sudipta Roy
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Timothy D Whitehead
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Shunqiang Li
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Foluso O Ademuyiwa
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Richard L Wahl
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA.,Department of Radiation Oncology, Washington University School of Medicine, St. Louis, MO, USA
| | - Farrokh Dehdashti
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA
| | - Kooresh I Shoghi
- Department of Radiology, Washington University School of Medicine, St. Louis, MO, USA. .,Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO, USA.
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Zannou OM, Ouedraogo AS, Biguezoton AS, Abatih E, Coral-Almeida M, Farougou S, Yao KP, Lempereur L, Saegerman C. Models for Studying the Distribution of Ticks and Tick-Borne Diseases in Animals: A Systematic Review and a Meta-Analysis with a Focus on Africa. Pathogens 2021; 10:893. [PMID: 34358043 DOI: 10.3390/pathogens10070893] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2021] [Revised: 06/30/2021] [Accepted: 07/12/2021] [Indexed: 11/17/2022] Open
Abstract
Ticks and tick-borne diseases (TTBD) are constraints to the development of livestock and induce potential human health problems. The worldwide distribution of ticks is not homogenous. Some places are ecologically suitable for ticks but they are not introduced in these areas yet. The absence or low density of hosts is a factor affecting the dissemination of the parasite. To understand the process of introduction and spread of TTBD in different areas, and forecast their presence, scientists developed different models (e.g., predictive models and explicative models). This study aimed to identify models developed by researchers to analyze the TTBD distribution and to assess the performance of these various models with a meta-analysis. A literature search was implemented with PRISMA protocol in two online databases (Scopus and PubMed). The selected articles were classified according to country, type of models and the objective of the modeling. Sensitivity, specificity and accuracy available data of these models were used to evaluate their performance using a meta-analysis. One hundred studies were identified in which seven tick genera were modeled, with Ixodes the most frequently modeled. Additionally, 13 genera of tick-borne pathogens were also modeled, with Borrelia the most frequently modeled. Twenty-three different models were identified and the most frequently used are the generalized linear model representing 26.67% and the maximum entropy model representing 24.17%. A focus on TTBD modeling in Africa showed that, respectively, genus Rhipicephalus and Theileria parva were the most modeled. A meta-analysis on the quality of 20 models revealed that maximum entropy, linear discriminant analysis, and the ecological niche factor analysis models had, respectively, the highest sensitivity, specificity, and area under the curve effect size among all the selected models. Modeling TTBD is highly relevant for predicting their distribution and preventing their adverse effect on animal and human health and the economy. Related results of such analyses are useful to build prevention and/or control programs by veterinary and public health authorities.
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Wang Y, Yang Y, Sun J, Wang L, Song X, Zhao X. Development and Validation of the Predictive Model for Esophageal Squamous Cell Carcinoma Differentiation Degree. Front Genet 2020; 11:595638. [PMID: 33193745 PMCID: PMC7645151 DOI: 10.3389/fgene.2020.595638] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Accepted: 09/25/2020] [Indexed: 11/13/2022] Open
Abstract
The diagnosis of the degree of differentiation of tumor cells can help physicians to make timely detection and take appropriate treatment for the patient's condition. In this study, the original dataset is clustered into two independent types by the Kohonen clustering algorithm. One type is used as the development sets to find correlation indicators and establish predictive models of differentiation, while the other type is used as the validation sets to test the correlation indicators and models. In the development sets, thirteen indicators significantly associated with the degree of differentiation of esophageal squamous cell carcinoma are found by the Kohonen clustering algorithm. Thirteen relevant indicators are used as input features and the degree of tumor differentiations is used as output. Ten classification algorithms are used to predict the differentiation of esophageal squamous cell carcinoma. Artificial bee colony-support vector machine (ABC-SVM) predicts better than the other nine algorithms, with an average accuracy of 81.5% for the 10-fold cross-validation. Based on logistic regression and ReliefF algorithm, five models with the greater merit for the degree of differentiation are found in the development sets. The AUC values of the five models are 0.672, 0.628, 0.630, 0.628, and 0.608 (P < 0.05). The AUC values of the five models in the validation sets are 0.753, 0.728, 0.744, 0.776, and 0.868 (P < 0.0001). The predicted values of the five models are constructed as the input features of ABC-SVM. The accuracy of the 10-fold cross-validation reached 82.0 and 86.5% in the development sets and the validation sets, respectively.
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Affiliation(s)
- Yanfeng Wang
- Henan Key Lab of Information-Based Electrical Appliances, Zhengzhou University of Light Industry, Zhengzhou, China
| | - Yuli Yang
- Henan Key Lab of Information-Based Electrical Appliances, Zhengzhou University of Light Industry, Zhengzhou, China
| | - Junwei Sun
- Henan Key Lab of Information-Based Electrical Appliances, Zhengzhou University of Light Industry, Zhengzhou, China
| | - Lidong Wang
- State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of The First Affiliated Hospital, Zhengzhou University, Zhengzhou, China
| | - Xin Song
- State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of The First Affiliated Hospital, Zhengzhou University, Zhengzhou, China
| | - Xueke Zhao
- State Key Laboratory of Esophageal Cancer Prevention & Treatment and Henan Key Laboratory for Esophageal Cancer Research of The First Affiliated Hospital, Zhengzhou University, Zhengzhou, China
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11
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Sinha A, Washington R, Sethumadhavan R, Perumal V, Potty RS, Isac S. Modified Integrated Algorithm for Detection of HIV Among Sick Children Aged 0–14 Year Seeking Care at Healthcare Facilities in India. Indian Pediatr 2020. [DOI: 10.1007/s13312-020-1891-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
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12
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Starr LT, Ulrich CM, Junker P, Huang L, O’Connor NR, Meghani SH. Patient Risk Factor Profiles Associated With the Timing of Goals-of-Care Consultation Before Death: A Classification and Regression Tree Analysis. Am J Hosp Palliat Care 2020; 37:767-778. [DOI: 10.1177/1049909120934292] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023] Open
Abstract
Background: Early palliative care consultation (“PCC”) to discuss goals-of-care benefits seriously ill patients. Risk factor profiles associated with the timing of conversations in hospitals, where late conversations most likely occur, are needed. Objective: To identify risk factor patient profiles associated with PCC timing before death. Methods: Secondary analysis of an observational study was conducted at an urban, academic medical center. Patients aged 18 years and older admitted to the medical center, who had PCC, and died July 1, 2014 to October 31, 2016, were included. Patients admitted for childbirth or rehabilitationand patients whose date of death was unknown were excluded. Classification and Regression Tree modeling was employed using demographic and clinical variables. Results: Of 1141 patients, 54% had PCC “close to death” (0-14 days before death); 26% had PCC 15 to 60 days before death; 21% had PCC >60 days before death (median 13 days before death). Variables associated with receiving PCC close to death included being Hispanic or “Other” race/ethnicity intensive care patients with extreme illness severity (85%), with age <46 or >75 increasing this probability (98%). Intensive care patients with extreme illness severity were also likely to receive PCC close to death (64%) as were 50% of intensive care patients with less than extreme illness severity. Conclusions: A majority of patients received PCC close to death. A complex set of variable interactions were associated with PCC timing. A systematic process for engaging patients with PCC earlier in the care continuum, and in intensive care regardless of illness severity, is needed.
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Affiliation(s)
- Lauren T. Starr
- NewCourtland Center for Transitions and Health, University of Pennsylvania School of Nursing, Philadelphia, PA, USA
- Center for Bioethics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Connie M. Ulrich
- NewCourtland Center for Transitions and Health, University of Pennsylvania School of Nursing, Philadelphia, PA, USA
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Paul Junker
- Program for Clinical Effectiveness and Quality Improvement, University of Pennsylvania Health System, Philadelphia, PA, USA
| | - Liming Huang
- BECCA Lab, University of Pennsylvania School of Nursing, Philadelphia, PA, USA
| | - Nina R. O’Connor
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Salimah H. Meghani
- NewCourtland Center for Transitions and Health, University of Pennsylvania School of Nursing, Philadelphia, PA, USA
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13
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Mupangwa W, Chipindu L, Nyagumbo I, Mkuhlani S, Sisito G. Evaluating machine learning algorithms for predicting maize yield under conservation agriculture in Eastern and Southern Africa. SN Appl Sci 2020. [DOI: 10.1007/s42452-020-2711-6] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022] Open
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15
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Sheikh K, Lee SH, Cheng Z, Lakshminarayanan P, Peng L, Han P, McNutt TR, Quon H, Lee J. Predicting acute radiation induced xerostomia in head and neck Cancer using MR and CT Radiomics of parotid and submandibular glands. Radiat Oncol 2019; 14:131. [PMID: 31358029 PMCID: PMC6664784 DOI: 10.1186/s13014-019-1339-4] [Citation(s) in RCA: 55] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Accepted: 07/17/2019] [Indexed: 12/24/2022] Open
Abstract
Purpose To analyze baseline CT/MR-based image features of salivary glands to predict radiation-induced xerostomia 3-months after head-and-neck cancer (HNC) radiotherapy. Methods A retrospective analysis was performed on 266 HNC patients who were treated using radiotherapy at our institution between 2009 and 2018. CT and T1 post-contrast MR images along with NCI-CTCAE xerostomia grade (3-month follow-up) were prospectively collected at our institution. CT and MR images were registered on which parotid/submandibular glands were contoured. Image features were extracted for ipsilateral/contralateral parotid and submandibular glands relative to the location of the primary tumor. Dose-volume-histogram (DVH) parameters were also acquired. Features were pre-selected based on Spearman correlation before modelling by examining the correlation with xerostomia (p < 0.05). A shrinkage regression analysis of the pre-selected features was performed using LASSO. The internal validity of the variable selection was estimated by repeating the entire variable selection procedure using a leave-one-out-cross-validation. The most frequently selected variables were considered in the final model. A generalized linear regression with repeated ten-fold cross-validation was developed to predict radiation-induced xerostomia at 3-months after radiotherapy. This model was tested in an independent dataset (n = 50) of patients who were treated at the same institution in 2017–2018. We compared the prediction performances under eight conditions (DVH-only, CT-only, MR-only, CT + MR, DVH + CT, DVH + CT + MR, Clinical+CT + MR, and Clinical+DVH + CT + MR) using the area under the receiver operating characteristic curve (ROC-AUC). Results Among extracted features, 7 CT, 5 MR, and 2 DVH features were selected. The internal cohort (n = 216) ROC-AUC values for DVH, CT, MR, and Clinical+DVH + CT + MR features were 0.73 ± 0.01, 0.69 ± 0.01, 0.70 ± 0.01, and 0.79 ± 0.01, respectively. The validation cohort (n = 50) ROC-AUC values for DVH, CT, MR, and Clinical+DVH + CT + MR features were 0.63, 0.57, 0.66, and 0.68, respectively. The DVH-ROC was not significantly different than the CT-ROC (p = 0.8) or MR-ROC (p = 0.4). However, the CT + MR-ROC was significantly different than the CT-ROC (p = 0.03), but not the Clinical+DVH + CT + MR model (p = 0.5). Conclusion Our results suggest that baseline CT and MR image features may reflect baseline salivary gland function and potential risk for radiation injury. The integration of baseline image features into prediction models has the potential to improve xerostomia risk stratification with the ultimate goal of truly personalized HNC radiotherapy. Electronic supplementary material The online version of this article (10.1186/s13014-019-1339-4) contains supplementary material, which is available to authorized users.
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Affiliation(s)
- Khadija Sheikh
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD, 21287-5678, USA
| | - Sang Ho Lee
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD, 21287-5678, USA
| | - Zhi Cheng
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD, 21287-5678, USA
| | - Pranav Lakshminarayanan
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD, 21287-5678, USA
| | - Luke Peng
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD, 21287-5678, USA
| | - Peijin Han
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD, 21287-5678, USA
| | - Todd R McNutt
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD, 21287-5678, USA
| | - Harry Quon
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD, 21287-5678, USA
| | - Junghoon Lee
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University School of Medicine, 401 North Broadway, Suite 1440, Baltimore, MD, 21287-5678, USA.
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Luo Y, Tseng HH, Cui S, Wei L, Ten Haken RK, El Naqa I. Balancing accuracy and interpretability of machine learning approaches for radiation treatment outcomes modeling. BJR Open 2019; 1:20190021. [PMID: 33178948 PMCID: PMC7592485 DOI: 10.1259/bjro.20190021] [Citation(s) in RCA: 32] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2019] [Revised: 06/18/2019] [Accepted: 06/25/2019] [Indexed: 12/17/2022] Open
Abstract
Radiation outcomes prediction (ROP) plays an important role in personalized prescription and adaptive radiotherapy. A clinical decision may not only depend on an accurate radiation outcomes’ prediction, but also needs to be made based on an informed understanding of the relationship among patients’ characteristics, radiation response and treatment plans. As more patients’ biophysical information become available, machine learning (ML) techniques will have a great potential for improving ROP. Creating explainable ML methods is an ultimate task for clinical practice but remains a challenging one. Towards complete explainability, the interpretability of ML approaches needs to be first explored. Hence, this review focuses on the application of ML techniques for clinical adoption in radiation oncology by balancing accuracy with interpretability of the predictive model of interest. An ML algorithm can be generally classified into an interpretable (IP) or non-interpretable (NIP) (“black box”) technique. While the former may provide a clearer explanation to aid clinical decision-making, its prediction performance is generally outperformed by the latter. Therefore, great efforts and resources have been dedicated towards balancing the accuracy and the interpretability of ML approaches in ROP, but more still needs to be done. In this review, current progress to increase the accuracy for IP ML approaches is introduced, and major trends to improve the interpretability and alleviate the “black box” stigma of ML in radiation outcomes modeling are summarized. Efforts to integrate IP and NIP ML approaches to produce predictive models with higher accuracy and interpretability for ROP are also discussed.
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Affiliation(s)
- Yi Luo
- Department of Radiation Oncology, University of Michigan, 519 W William Street, Ann Arbor, MI, USA
| | - Huan-Hsin Tseng
- Department of Radiation Oncology, University of Michigan, 519 W William Street, Ann Arbor, MI, USA
| | - Sunan Cui
- Department of Radiation Oncology, University of Michigan, 519 W William Street, Ann Arbor, MI, USA
| | - Lise Wei
- Department of Radiation Oncology, University of Michigan, 519 W William Street, Ann Arbor, MI, USA
| | - Randall K Ten Haken
- Department of Radiation Oncology, University of Michigan, 519 W William Street, Ann Arbor, MI, USA
| | - Issam El Naqa
- Department of Radiation Oncology, University of Michigan, 519 W William Street, Ann Arbor, MI, USA
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Quon H, McNutt T, Lee J, Bowers M, Jiang W, Lakshminarayanan P, Cheng Z, Han P, Hui X, Shah V, Moore J, Nakatsugawa M, Robertson S, Cecil E, Page B, Kiess A, Wong J, DeWeese T. Needs and Challenges for Radiation Oncology in the Era of Precision Medicine. Int J Radiat Oncol Biol Phys 2018; 103:809-817. [PMID: 30562547 DOI: 10.1016/j.ijrobp.2018.11.017] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2018] [Revised: 09/17/2018] [Accepted: 11/10/2018] [Indexed: 01/19/2023]
Abstract
Modern medicine, including the care of the cancer patient, has significantly advanced, with the evidence-based medicine paradigm serving to guide clinical care decisions. Yet we now also recognize the tremendous heterogeneity not only of disease states but of the patient and his or her environment as it influences treatment outcomes and toxicities. These reasons and many others have led to a reevaluation of the generalizability of randomized trials and growing interest in accounting for this heterogeneity under the rubric of precision medicine as it relates to personalizing clinical care predictions, decisions, and therapy for the disease state. For the cancer patient treated with radiation therapy, characterizing the spatial treatment heterogeneity has been a fundamental tenet of routine clinical care facilitated by established database and imaging platforms. Leveraging these platforms to further characterize and collate all clinically relevant sources of heterogeneity that affect the longitudinal health outcomes of the irradiated cancer patient provides an opportunity to generate a critical informatics infrastructure on which precision radiation therapy may be realized. In doing so, data science-driven insight discoveries, personalized clinical decisions, and the potential to accelerate translational efforts may be realized ideally within a network of institutions with locally developed yet coordinated informatics infrastructures. The path toward realizing these goals has many needs and challenges, which we summarize, with many still to be realized and understood. Early efforts by our group have identified the feasibility of this approach using routine clinical data sets and offer promise that this transformation can be successfully realized in radiation oncology.
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Affiliation(s)
- Harry Quon
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland.
| | - Todd McNutt
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Junghoon Lee
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Michael Bowers
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Wei Jiang
- Department of Civil Engineering, Johns Hopkins University, Baltimore, Maryland
| | - Pranav Lakshminarayanan
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Zhi Cheng
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Peijin Han
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Xuan Hui
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Veeraj Shah
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Joseph Moore
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Minoru Nakatsugawa
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Scott Robertson
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Emilie Cecil
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Brandi Page
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Ana Kiess
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - John Wong
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
| | - Theodore DeWeese
- Department of Radiation Oncology and Molecular Radiation Sciences, Johns Hopkins University, Baltimore, Maryland
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Kim JS, Jeong JS, Lee KB, Kim SR, Choe YH, Kwon SH, Cho SH, Lee YC. Phosphoinositide 3-kinase-delta could be a biomarker for eosinophilic nasal polyps. Sci Rep 2018; 8:15990. [PMID: 30375439 PMCID: PMC6207677 DOI: 10.1038/s41598-018-34345-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Accepted: 10/15/2018] [Indexed: 11/29/2022] Open
Abstract
Nasal polyps (NP) cause diverse clinical symptoms of chronic rhinosinusitis (CRS). Chronic inflammation of sinonasal mucosa is known to be crucial in NP formation. We aimed to define the implications of phosphoinositide 3-kinase (PI3K)-δ in nasal inflammation associated with NP by analyzing NP tissue obtained from CRS patients. Results showed that expression of p110δ, a regulatory subunit of PI3K-δ, in NP tissue was increased compared to control tissue. Increased p110δ expression was closely correlated with more severe CRS features. Interestingly, p110δ expression was increased in eosinophilic NP, which are closely related to more complicated clinical courses of the disease. Furthermore, CRS patients possessing NP with higher p110δ expression displayed more eosinophils in NP tissue and blood, higher levels of IL-5 in NP tissue, and more severe features of the disease. Therefore, PI3K-δ may contribute to the formation of NP, especially eosinophilic NP associated with more severe clinical presentations and radiological features.
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Affiliation(s)
- Jong Seung Kim
- Department of Otorhinolaryngology-Head and Neck Surgery, Chonbuk National University Medical School, Jeonju, South Korea.,Research Institute of Clinical Medicine, Chonbuk National University-Biomedical Research Institute, Chonbuk National University Hospital, Jeonju, South Korea
| | - Jae Seok Jeong
- Department of Internal Medicine, Research Center for Pulmonary Disorders, Chonbuk National University Medical School, Jeonju, South Korea
| | - Kyung Bae Lee
- Department of Internal Medicine, Research Center for Pulmonary Disorders, Chonbuk National University Medical School, Jeonju, South Korea
| | - So Ri Kim
- Department of Internal Medicine, Research Center for Pulmonary Disorders, Chonbuk National University Medical School, Jeonju, South Korea.,Research Institute of Clinical Medicine, Chonbuk National University-Biomedical Research Institute, Chonbuk National University Hospital, Jeonju, South Korea
| | - Yeong Hun Choe
- Department of Internal Medicine, Research Center for Pulmonary Disorders, Chonbuk National University Medical School, Jeonju, South Korea
| | - Sam Hyun Kwon
- Department of Otorhinolaryngology-Head and Neck Surgery, Chonbuk National University Medical School, Jeonju, South Korea.,Research Institute of Clinical Medicine, Chonbuk National University-Biomedical Research Institute, Chonbuk National University Hospital, Jeonju, South Korea
| | - Seong Ho Cho
- Division of Allergy and Immunology, Internal Medicine, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
| | - Yong Chul Lee
- Department of Internal Medicine, Research Center for Pulmonary Disorders, Chonbuk National University Medical School, Jeonju, South Korea. .,Research Institute of Clinical Medicine, Chonbuk National University-Biomedical Research Institute, Chonbuk National University Hospital, Jeonju, South Korea.
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Nakatsugawa M, Cheng Z, Kiess A, Choflet A, Bowers M, Utsunomiya K, Sugiyama S, Wong J, Quon H, McNutt T. The Needs and Benefits of Continuous Model Updates on the Accuracy of RT-Induced Toxicity Prediction Models Within a Learning Health System. Int J Radiat Oncol Biol Phys 2019; 103:460-7. [PMID: 30300689 DOI: 10.1016/j.ijrobp.2018.09.038] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2018] [Accepted: 10/06/2018] [Indexed: 12/14/2022]
Abstract
PURPOSE Clinical data collection and development of outcome prediction models by machine learning can form the foundation for a learning health system offering precision radiation therapy. However, changes in clinical practice over time can affect the measures and patient outcomes and, hence, the collected data. We hypothesize that regular prediction model updates and continuous prospective data collection are important to prevent the degradation of a model's predication accuracy. METHODS AND MATERIALS Clinical and dosimetric data from head and neck patients receiving intensity modulated radiation therapy from 2008 to 2015 were prospectively collected as a routine clinical workflow and anonymized for this analysis. Prediction models for grade ≥2 xerostomia at 3 to 6 months of follow-up were developed by bivariate logistic regression using the dose-volume histogram of parotid and submandibular glands. A baseline prediction model was developed with a training data set from 2008 to 2009. The selected predictor variables and coefficients were updated by 4 different model updating methods. (A) The prediction model was updated by using only recent 2-year data and applied to patients in the following test year. (B) The model was updated by increasing the training data set yearly. (C) The model was updated by increasing the training data set on the condition that the area under the curve (AUC) of the recent test year was less than 0.6. (D) The model was not updated. The AUC of the test data set was compared among the 4 model updating methods. RESULTS Dose to parotid and submandibular glands and grade of xerostomia showed decreasing trends over the years (2008-2015, 297 patients; P < .001). The AUC of predicting grade ≥2 xerostomia for the initial training data set (2008-2009, 41 patients) was 0.6196. The AUC for the test data set (2010-2015, 256 patients) decreased to 0.5284 when the initial model was not updated (D). However, the AUC was significantly improved by model updates (A: 0.6164; B: 0.6084; P < .05). When the model was conditionally updated, the AUC was 0.6072 (C). CONCLUSIONS Our preliminary results demonstrate that updating prediction models with prospective data collection is effective for maintaining the performance of xerostomia prediction. This suggests that a machine learning framework can handle the dynamic changes in a radiation oncology clinical practice and may be an important component for the construction of a learning health system.
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McNutt TR, Benedict SH, Low DA, Moore K, Shpitser I, Jiang W, Lakshminarayanan P, Cheng Z, Han P, Hui X, Nakatsugawa M, Lee J, Moore JA, Robertson SP, Shah V, Taylor R, Quon H, Wong J, DeWeese T. Using Big Data Analytics to Advance Precision Radiation Oncology. Int J Radiat Oncol Biol Phys 2018; 101:285-291. [DOI: 10.1016/j.ijrobp.2018.02.028] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Revised: 02/13/2018] [Accepted: 02/20/2018] [Indexed: 11/25/2022]
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