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Yang Y, Yang Z, Lyu Z, Ouyang K, Wang J, Wu D, Li Y. Pathological-Features-Modified TNM Staging System Improves Prognostic Accuracy for Rectal Cancer. Dis Colon Rectum 2024; 67:645-654. [PMID: 38147435 DOI: 10.1097/dcr.0000000000003034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/28/2023]
Abstract
BACKGROUND Variations in survival outcomes are observed in the eighth edition of the American Joint Committee on Cancer TNM staging system. OBJECTIVE Machine learning ensemble methods were used to develop and evaluate the effectiveness of a pathological-features-modified TNM staging system in predicting survival for patients with rectal cancer by use of commonly reported pathological features, such as histological grade, tumor deposits, and perineural invasion, to improve the prognostic accuracy. DESIGN This was a retrospective population-based study. SETTINGS Data were assessed from the database of the Surveillance, Epidemiology, and End Results Program. PATIENTS The study cohort comprised 14,468 patients with rectal cancer diagnosed between 2010 and 2015. The development cohort included those who underwent surgery as the primary treatment, whereas patients who received neoadjuvant therapy were assigned to the validation cohort. MAIN OUTCOME MEASURES The primary outcome measures included cumulative rectal cancer survival, adjusted HRs, and both calibration and discrimination statistics to evaluate model performance and internal validation. RESULTS Multivariable Cox regression analysis identified all 3 pathological features as prognostic factors, after which patients were categorized into 4 pathological groups based on the number of pathological features (ie, 0, 1, 2, and 3). Distinct survival differences were observed among the groups, especially with patients with stage III rectal cancer. The proposed pathological-features-modified TNM staging outperformed the TNM staging in both the development and validation cohorts. LIMITATIONS Retrospective in design and lack of external validation. CONCLUSIONS The proposed pathological-features-modified TNM staging could complement the current TNM staging by improving the accuracy of survival estimation of patients with rectal cancer. See Video Abstract . EL SISTEMA DE ESTADIFICACIN TNM CON CARACTERSTICAS PATOLGICAS MODIFICADO MEJORA LA PRECISIN DEL PRONSTICO DEL CNCER DE RECTO ANTECEDENTES:Se observan variaciones en los resultados de supervivencia en el sistema de estadificación TNM del Comité Conjunto Americano del Cáncer 8º ediciónOBJETIVO:Se utilizaron métodos conjuntos de aprendizaje automático para desarrollar y evaluar la eficacia de un sistema de estadificación con características patológicas modificadas de tumores, ganglios y metástasis para predecir la supervivencia de pacientes con cáncer de recto, utilizando algunas características patológicas comúnmente informadas, como el grado histológico, depósitos tumorales e invasión perineural, para mejorar la precisión del pronóstico.DISEÑO:Este fue un estudio retrospectivo de base poblacional.ENTERNO CLINICO:Se recuperaron y evaluaron datos de la base de datos de Vigilancia, Epidemiología y Resultados Finales.PACIENTES:La cohorte del estudio estuvo compuesta por 14,468 pacientes con cáncer de recto diagnosticados entre 2010 y 2015. La cohorte de desarrollo incluyó a aquellos que se sometieron a cirugía como tratamiento primario, mientras que los pacientes que recibieron terapia neoadyuvante fueron asignados a la cohorte de validación.PRINCIPALES MEDIDAS DE RESULTADO:Las medidas de resultado primarias incluyeron supervivencia acumulada del cáncer de recto, índices de riesgo ajustados y estadísticas de calibración y discriminación para evaluar el rendimiento del modelo y la validación interna.RESULTADOS:El análisis de regresión multivariable de Cox identificó las tres características patológicas como factores pronósticos, después de lo cual los pacientes se clasificaron en cuatro grupos patológicos según el número de características patológicas (es decir, 0, 1, 2 y 3). Se observaron distintas diferencias en la supervivencia entre los grupos, especialmente en los pacientes en estadio III. La estadificación propuesta con características patológicas modificadas de tumores-ganglios-metástasis superó a la estadificación TNM tanto en las cohortes de desarrollo como en las de validación.LIMITACIONES:Diseño retrospectivo y falta de validación externa.CONCLUSIONES:La estadificación propuesta con características patológicas modificadas de tumores-ganglios-metástasis podría complementar la estadificación TNM actual al mejorar la precisión de la estimación de supervivencia de los pacientes con cáncer de recto. (Traducción- Dr. Francisco M. Abarca-Rendon ).
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Affiliation(s)
- Yuesheng Yang
- Shantou University Medical College, Shantou, People's Republic of China
- Department of Gastrointestinal Surgery, Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
| | - Zifeng Yang
- Department of Gastrointestinal Surgery, Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
| | - Zejian Lyu
- Department of Gastrointestinal Surgery, Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
| | - Kaibo Ouyang
- Shantou University Medical College, Shantou, People's Republic of China
- Department of Gastrointestinal Surgery, Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
| | - Junjiang Wang
- Department of Gastrointestinal Surgery, Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
| | - Deqing Wu
- Department of Gastrointestinal Surgery, Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
| | - Yong Li
- Department of Gastrointestinal Surgery, Department of General Surgery, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People's Republic of China
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Li S, Yi H, Leng Q, Wu Y, Mao Y. New perspectives on cancer clinical research in the era of big data and machine learning. Surg Oncol 2024; 52:102009. [PMID: 38215544 DOI: 10.1016/j.suronc.2023.102009] [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] [Received: 08/29/2023] [Accepted: 10/16/2023] [Indexed: 01/14/2024]
Abstract
In the 21st century, the development of medical science has entered the era of big data, and machine learning has become an essential tool for mining medical big data. The establishment of the SEER database has provided a wealth of epidemiological data for cancer clinical research, and the number of studies based on SEER and machine learning has been growing in recent years. This article reviews recent research based on SEER and machine learning and finds that the current focus of such studies is primarily on the development and validation of models using machine learning algorithms, with the main directions being lymph node metastasis prediction, distant metastasis prediction, and prognosis-related research. Compared to traditional models, machine learning algorithms have the advantage of stronger adaptability, but also suffer from disadvantages such as overfitting and poor interpretability, which need to be weighed in practical applications. At present, machine learning algorithms, as the foundation of artificial intelligence, have just begun to emerge in the field of cancer clinical research. The future development of oncology will enter a more precise era of cancer research, characterized by larger data, higher dimensions, and more frequent information exchange. Machine learning is bound to shine brightly in this field.
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Affiliation(s)
- Shujun Li
- Department of Hematology, Xiangya Hospital, Central South University, Changsha, 410008, China; National Clinical Research Center for Geriatric Diseases (Xiangya Hospital), China; Hunan Hematology Oncology Clinical Medical Research Center, China
| | - Hang Yi
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China
| | - Qihao Leng
- Xiangya School of Medicine, Central South University, Changsha, 410013, Hunan Province, China
| | - You Wu
- Institute for Hospital Management, School of Medicine, Tsinghua University, 30 Shuangqing Rd, Haidian District, Beijing, China; Department of Health Policy and Management, Bloomberg School of Public Health, Johns Hopkins University, Baltimore, MD, 21205, USA.
| | - Yousheng Mao
- Department of Thoracic Surgery, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, 100021, China.
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Su Y, Yang DS, Li YQ, Qin J, Liu L. Early-onset locally advanced rectal cancer characteristics, a practical nomogram and risk stratification system: a population-based study. Front Oncol 2023; 13:1190327. [PMID: 37260988 PMCID: PMC10228826 DOI: 10.3389/fonc.2023.1190327] [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: 03/20/2023] [Accepted: 04/26/2023] [Indexed: 06/02/2023] Open
Abstract
Background The purpose of this study is to construct a novel and practical nomogram and risk stratification system to accurately predict cancer-specific survival (CSS) of early-onset locally advanced rectal cancer (EO-LARC) patients. Methods A total of 2440 patients diagnosed with EO-LARC between 2010 and 2019 were screened from the Surveillance, Epidemiology, and End Results (SEER) database. The pool of potentially eligible patients was randomly divided into two groups: a training cohort (N=1708) and a validation cohort (N=732). The nomogram was developed and calibrated using various methods, including the coherence index (C-index), receiver operating characteristic curve (ROC), calibration curves, and decision curves (DCA). A new risk classification system was established based on the nomogram. To compare the performance of this nomogram to that of the American Joint Committee on Cancer (AJCC) staging system, DCA, net reclassification index (NRI), and integrated discrimination improvement (IDI) were employed. Result Seven variables were included in the model. The area under the ROC curve (AUC) for the training cohort was 0.766, 0.736, and 0.731 at 3, 6, and 9 years, respectively. Calibration plots displayed good consistency between actual observations and the nomogram's predictions. The DCA curve further demonstrated the validity of the nomination form in clinical practice. Based on the scores of the nomogram, all patients were divided into a low-risk group, a middle-risk group, and a high-risk group. NRI for the 3-, 6-, and 9-year CSS(training cohort: 0.48, 0.45, 0.52; validation cohort: 0.42, 0.37, 0.37), IDI for the 3-, 6-, and 9-year CSS (training cohort: 0.09, 0.10, 0.11; validation cohort: 0.07, 0.08, 0.08). The Kaplan-Meier curve revealed that the new risk classification system possesses a more extraordinary ability to identify patients in different risk groups than the AJCC staging. Conclusion A practical prognostic nomogram and novel risk classification system have been developed to efficiently predict the prognosis of EO-LARC. These tools can serve as a guide to individualize patient treatment and improve clinical decision-making.
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Affiliation(s)
- Yang Su
- Department of Gastrointestinal Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Da Shuai Yang
- Department of Hepatobiliary Surgery, Renmin Hospital of Wuhan University, Wuhan, China
| | - Yan qi Li
- Department of Gastrointestinal Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Jichao Qin
- Department of Gastrointestinal Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Lu Liu
- Department of Gastrointestinal Surgery Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Calaf GM, Crispin LA, Muñoz JP, Aguayo F, Bleak TC. Muscarinic Receptors Associated with Cancer. Cancers (Basel) 2022; 14:cancers14092322. [PMID: 35565451 PMCID: PMC9100020 DOI: 10.3390/cancers14092322] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2022] [Revised: 04/26/2022] [Accepted: 04/30/2022] [Indexed: 02/01/2023] Open
Abstract
Simple Summary Recently, cancer research has described the presence of the cholinergic machinery, specifically muscarinic receptors, in a wide variety of cancers due to their activation and signaling pathways associated with tumor progression and metastasis, providing a wide overview of their contribution to different cancer formation and development for new antitumor targets. This review focused on determining the molecular signatures associated with muscarinic receptors in breast and other cancers and the need for pharmacological, molecular, biochemical, technological, and clinical approaches to improve new therapeutic targets. Abstract Cancer has been considered the pathology of the century and factors such as the environment may play an important etiological role. The ability of muscarinic agonists to stimulate growth and muscarinic receptor antagonists to inhibit tumor growth has been demonstrated for breast, melanoma, lung, gastric, colon, pancreatic, ovarian, prostate, and brain cancer. This work aimed to study the correlation between epidermal growth factor receptors and cholinergic muscarinic receptors, the survival differences adjusted by the stage clinical factor, and the association between gene expression and immune infiltration level in breast, lung, stomach, colon, liver, prostate, and glioblastoma human cancers. Thus, targeting cholinergic muscarinic receptors appears to be an attractive therapeutic alternative due to the complex signaling pathways involved.
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Affiliation(s)
- Gloria M. Calaf
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica 1000000, Chile; (L.A.C.); (J.P.M.); (T.C.B.)
- Correspondence:
| | - Leodan A. Crispin
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica 1000000, Chile; (L.A.C.); (J.P.M.); (T.C.B.)
| | - Juan P. Muñoz
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica 1000000, Chile; (L.A.C.); (J.P.M.); (T.C.B.)
| | - Francisco Aguayo
- Laboratorio de Oncovirología, Programa de Virología, Instituto de Ciencias Biomédicas (ICBM), Facultad de Medicina, Universidad de Chile, Santiago 8380000, Chile;
| | - Tammy C. Bleak
- Instituto de Alta Investigación, Universidad de Tarapacá, Arica 1000000, Chile; (L.A.C.); (J.P.M.); (T.C.B.)
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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.
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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.)
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Using Weighted Differences in Hazards as Effect Sizes for Survival Data. JOURNAL OF STATISTICAL THEORY AND PRACTICE 2022. [DOI: 10.1007/s42519-021-00234-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
AbstractSensitive to the change in sample sizes, traditional measures such as values of test statistics or p values can fail to quantify the difference in survival between populations for time-to-event data. We thereby propose to use effect sizes defined as the weighted differences in hazards to evaluate the survival difference between two groups. On the basis of the logrank test statistic, Gehan–Wilcoxon test statistic and Prentice–Wilcoxon test statistic, we developed three effect sizes that compare the survival experiences over the time period of investigation. Estimates of these three effect sizes were provided and their large sample behaviors were studied. In light of the Mann–Whitney parameter, we presented an effect size that compares the survival experiences over the entire possible/hypothetical time period. Two estimates of this effect size were constructed. We compared the proposed effect sizes and illuminated their use by theoretical studies, simulations and real cancer data. The effect sizes proposed in this article can help understand the survival difference in populations and are expected to have promising applications in the field of survival analysis.
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Yang CQ, Wang H, Liu Z, Hueman MT, Bhaskaran A, Henson DE, Sheng L, Chen D. Integrating additional factors into the TNM staging for cutaneous melanoma by machine learning. PLoS One 2021; 16:e0257949. [PMID: 34591891 PMCID: PMC8483349 DOI: 10.1371/journal.pone.0257949] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 09/14/2021] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND Integrating additional factors into the TNM staging system is needed for more accurate risk classification and survival prediction for patients with cutaneous melanoma. In the present study, we introduce machine learning as a novel tool that incorporates additional prognostic factors to improve the current TNM staging system. METHODS AND FINDINGS Cancer-specific survival data for cutaneous melanoma with at least a 5 years follow-up were extracted from the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute and split into the training set (40,781 cases) and validation set (5,390 cases). Five factors were studied: the primary tumor (T), regional lymph nodes (N), distant metastasis (M), age (A), and sex (S). The Ensemble Algorithm for Clustering Cancer Data (EACCD) was applied to the training set to generate prognostic groups. Utilizing only T, N, and M, a basic prognostic system was built where patients were stratified into 10 prognostic groups with well-separated survival curves, similar to 10 AJCC stages. These 10 groups had a significantly higher accuracy in survival prediction than 10 stages (C-index = 0.7682 vs 0.7643; increase in C-index = 0.0039, 95% CI = (0.0032, 0.0047); p-value = 7.2×10-23). Nevertheless, a positive association remained between the EACCD grouping and the AJCC staging (Spearman's rank correlation coefficient = 0.8316; p-value = 4.5×10-13). With additional information from A and S, a more advanced prognostic system was established using the training data that stratified patients into 10 groups and further improved the prediction accuracy (C-index = 0.7865 vs 0.7643; increase in C-index = 0.0222, 95% CI = (0.0191, 0.0254); p-value = 8.8×10-43). Both internal validation using the training set and temporal validation using the validation set showed good stratification and a high predictive accuracy of the prognostic systems. CONCLUSIONS The EACCD allows additional factors to be integrated into the TNM to create a prognostic system that improves patient stratification and survival prediction for cutaneous melanoma. This integration separates favorable from unfavorable clinical outcomes for patients and improves both cohort selection for clinical trials and treatment management.
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Affiliation(s)
- Charles Q. Yang
- Department of Surgery, Walter Reed National Military Medical Center, Bethesda, MD, United States of America
| | - Huan Wang
- Department of Biostatistics, The George Washington University, Washington, DC, United States of America
| | - Zhenqiu Liu
- Department of Public Health Sciences, Penn State Cancer Institute, Hershey, PA, United States of America
| | - Matthew T. Hueman
- Department of Surgical Oncology, John P. Murtha Cancer Center, Walter Reed National Military Medical Center, Bethesda, MD, United States of America
| | - Aadya Bhaskaran
- Department of Quantitative Theory and Methods, Emory University, Atlanta, GA, United States of America
| | - Donald E. Henson
- Deceased, was with The Department of Preventive Medicine & Biostatistics, F. Edward Hébert School of Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, United States of America
| | - Li Sheng
- Department of Mathematics, Drexel University, Philadelphia, PA, United States of America
| | - Dechang Chen
- Department of Preventive Medicine & Biostatistics, F. Edward Hébert School of Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, United States of America
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Berbecka M, Forma A, Baj J, Furtak-Niczyporuk M, Maciejewski R, Sitarz R. A Systematic Review of the Cyclooxygenase-2 (COX-2) Expression in Rectal Cancer Patients Treated with Preoperative Radiotherapy or Radiochemotherapy. J Clin Med 2021; 10:4443. [PMID: 34640461 PMCID: PMC8509380 DOI: 10.3390/jcm10194443] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 09/14/2021] [Accepted: 09/24/2021] [Indexed: 02/05/2023] Open
Abstract
The main objective of this systematic review is to investigate the expression level of the cyclooxygenase-2 (COX-2) in rectal cancer treated with either preoperative radiotherapy or radiochemotherapy. In addition, we have summarized the effects of preoperative treatment of rectal cancer with regards to the expression levels of COX-2. A systematic literature review was performed in The Cochrane Library, PubMed, Web of Science, and Scopus databases on 1 January 2021 with the usage of the following search string-(cyclooxygenase-2) OR (COX-2) AND (rectal cancer) AND (preoperative radiochemotherapy) OR (preoperative radiotherapy). Among the 176 included in the analysis, only 13 studies were included for data extraction with a total number of 2095 patients. The results of the analysis are based on the articles concerning the expression of COX-2 in rectal cancer among patients treated with preoperative radiotherapy or radiochemotherapy. A COX-2 expression is an early event involved in rectal cancer development. In cases of negative COX-2 expression, radiotherapy and radiochemotherapy might contribute to the reduction of a local recurrence. Therefore, COX-2 may be considered as a biologic factor while selecting patients for more effective, less time-consuming and less expensive preoperative treatment. However, the utility of the administration of COX-2 inhibitors to patients with COX-2 overexpression, in an attempt to improve the patients' response rate to the neoadjuvant treatment, needs an assessment in further clinical trials.
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Affiliation(s)
- Monika Berbecka
- Department of Normal Anatomy, Medical University of Lublin, 20-090 Lublin, Poland; (M.B.); (J.B.); (R.M.)
| | - Alicja Forma
- Department of Forensic Medicine, Medical University of Lublin, 20-090 Lublin, Poland;
| | - Jacek Baj
- Department of Normal Anatomy, Medical University of Lublin, 20-090 Lublin, Poland; (M.B.); (J.B.); (R.M.)
| | | | - Ryszard Maciejewski
- Department of Normal Anatomy, Medical University of Lublin, 20-090 Lublin, Poland; (M.B.); (J.B.); (R.M.)
| | - Robert Sitarz
- Department of Normal Anatomy, Medical University of Lublin, 20-090 Lublin, Poland; (M.B.); (J.B.); (R.M.)
- Department of Surgical Oncology, St. John’s Cancer Center, 20-090 Lublin, Poland
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Grimley PM, Liu Z, Darcy KM, Hueman MT, Wang H, Sheng L, Henson DE, Chen D. A prognostic system for epithelial ovarian carcinomas using machine learning. Acta Obstet Gynecol Scand 2021; 100:1511-1519. [PMID: 33665831 PMCID: PMC8360140 DOI: 10.1111/aogs.14137] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Accepted: 03/01/2021] [Indexed: 11/28/2022]
Abstract
Introduction Integrating additional factors into the International Federation of Gynecology and Obstetrics (FIGO) staging system is needed for accurate patient classification and survival prediction. In this study, we tested machine learning as a novel tool for incorporating additional prognostic parameters into the conventional FIGO staging system for stratifying patients with epithelial ovarian carcinomas and evaluating their survival. Material and methods Cancer‐specific survival data for epithelial ovarian carcinomas were extracted from the Surveillance, Epidemiology, and End Results (SEER) program. Two datasets were constructed based upon the year of diagnosis. Dataset 1 (39 514 cases) was limited to primary tumor (T), regional lymph nodes (N) and distant metastasis (M). Dataset 2 (25 291 cases) included additional parameters of age at diagnosis (A) and histologic type and grade (H). The Ensemble Algorithm for Clustering Cancer Data (EACCD) was applied to generate prognostic groups with depiction in dendrograms. C‐indices provided dendrogram cutoffs and comparisons of prediction accuracy. Results Dataset 1 was stratified into nine epithelial ovarian carcinoma prognostic groups, contrasting with 10 groups from FIGO methodology. The EACCD grouping had a slightly higher accuracy in survival prediction than FIGO staging (C‐index = 0.7391 vs 0.7371, increase in C‐index = 0.0020, 95% confidence interval [CI] 0.0012–0.0027, p = 1.8 × 10−7). Nevertheless, there remained a strong inter‐system association between EACCD and FIGO (rank correlation = 0.9480, p = 6.1 × 10−15). Analysis of Dataset 2 demonstrated that A and H could be smoothly integrated with the T, N and M criteria. Survival data were stratified into nine prognostic groups with an even higher prediction accuracy (C‐index = 0.7605) than when using only T, N and M. Conclusions EACCD was successfully applied to integrate A and H with T, N and M for stratification and survival prediction of epithelial ovarian carcinoma patients. Additional factors could be advantageously incorporated to test the prognostic impact of emerging diagnostic or therapeutic advances.
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Affiliation(s)
- Philip M Grimley
- Department of Pathology, F. Edward Hébert School of Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Zhenqiu Liu
- Department of Public Health Sciences, Penn State Cancer Institute, Hershey, PA, USA
| | - Kathleen M Darcy
- Department of Obstetrics & Gynecology, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Matthew T Hueman
- Department of Surgical Oncology, John P. Murtha Cancer Center, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Huan Wang
- Department of Biostatistics, George Washington University, Washington, DC, USA
| | - Li Sheng
- Department of Mathematics, Drexel University, Philadelphia, PA, USA
| | - Donald E Henson
- Department of Preventive Medicine & Biostatistics, F. Edward Hébert School of Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
| | - Dechang Chen
- Department of Preventive Medicine & Biostatistics, F. Edward Hébert School of Medicine, Uniformed Services University of the Health Sciences, Bethesda, MD, USA
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Hueman M, Wang H, Liu Z, Henson D, Nguyen C, Park D, Sheng L, Chen D. Expanding TNM for lung cancer through machine learning. Thorac Cancer 2021; 12:1423-1430. [PMID: 33713568 PMCID: PMC8088955 DOI: 10.1111/1759-7714.13926] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 02/20/2021] [Accepted: 02/21/2021] [Indexed: 01/05/2023] Open
Abstract
Background Expanding the tumor, lymph node, metastasis (TNM) staging system by accommodating new prognostic and predictive factors for cancer will improve patient stratification and survival prediction. Here, we introduce machine learning for incorporating additional prognostic factors into the conventional TNM for stratifying patients with lung cancer and evaluating survival. Methods Data were extracted from SEER. A total of 77 953 patients were analyzed using factors including primary tumor (T), regional lymph node (N), distant metastasis (M), age, and histology type. Ensemble algorithm for clustering cancer data (EACCD) and C‐index were applied to generate prognostic groups and expand the current staging system. Results With T, N, and M, EACCD stratified patients into 11 groups, resulting in a significantly higher accuracy in survival prediction than the 10 AJCC stages (C‐index = 0.7346 vs. 0.7247, increase in C‐index = 0.0099, 95% CI: 0.0091–0.0106, p‐value = 9.2 × 10−147). There nevertheless remained a strong association between the EACCD grouping and AJCC staging (rank correlation = 0.9289; p‐value = 6.7 × 10−22). A further analysis demonstrated that age and histological tumor could be integrated with the TNM. Data were stratified into 12 prognostic groups with an even higher prediction accuracy (C‐index = 0.7468 vs. 0.7247, increase in C‐index = 0.0221, 95% CI: 0.0212–0.0231, p‐value <5 × 10−324). Conclusions EACCD can be successfully applied to integrate additional factors with T, N, M for lung cancer patients.
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Affiliation(s)
- Matthew Hueman
- Department of Surgical Oncology, John P. Murtha Cancer Center, Walter Reed National Military Medical Center, Bethesda, Maryland, USA
| | - Huan Wang
- Department of Biostatistics, George Washington University, Washington, District of Columbia, USA
| | - Zhenqiu Liu
- Department of Public Health Sciences, Penn State Cancer Institute, Hershey, Pennsylvania, USA
| | - Donald Henson
- Department of Preventive Medicine & Biostatistics, F. Edward Hébert School of Medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
| | - Cuong Nguyen
- Department of Pathology, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
| | - Dean Park
- Department of Hematology-Oncology, John P. Murtha Cancer Center, Walter Reed National Military Medical Center, Bethesda, Maryland, USA
| | - Li Sheng
- Department of Mathematics, Drexel University, Philadelphia, Pennsylvania, USA
| | - Dechang Chen
- Department of Preventive Medicine & Biostatistics, F. Edward Hébert School of Medicine, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA
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Using machine learning to create prognostic systems for endometrial cancer. Gynecol Oncol 2020; 159:744-750. [DOI: 10.1016/j.ygyno.2020.09.047] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Accepted: 09/27/2020] [Indexed: 12/13/2022]
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12
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Prediction of Colon Cancer Stages and Survival Period with Machine Learning Approach. Cancers (Basel) 2019; 11:cancers11122007. [PMID: 31842486 PMCID: PMC6966646 DOI: 10.3390/cancers11122007] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2019] [Revised: 12/01/2019] [Accepted: 12/09/2019] [Indexed: 12/11/2022] Open
Abstract
The prediction of tumor in the TNM staging (tumor, node, and metastasis) stage of colon cancer using the most influential histopathology parameters and to predict the five years disease-free survival (DFS) period using machine learning (ML) in clinical research have been studied here. From the colorectal cancer (CRC) registry of Chang Gung Memorial Hospital, Linkou, Taiwan, 4021 patients were selected for the analysis. Various ML algorithms were applied for the tumor stage prediction of the colon cancer by considering the Tumor Aggression Score (TAS) as a prognostic factor. Performances of different ML algorithms were evaluated using five-fold cross-validation, which is an effective way of the model validation. The accuracy achieved by the algorithms taking both cases of standard TNM staging and TNM staging with the Tumor Aggression Score was determined. It was observed that the Random Forest model achieved an F-measure of 0.89, when the Tumor Aggression Score was considered as an attribute along with the standard attributes normally used for the TNM stage prediction. We also found that the Random Forest algorithm outperformed all other algorithms, with an accuracy of approximately 84% and an area under the curve (AUC) of 0.82 ± 0.10 for predicting the five years DFS.
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