1
|
Xu L, Cao F, Wang L, Liu W, Gao M, Zhang L, Hong F, Lin M. Machine learning model and nomogram to predict the risk of heart failure hospitalization in peritoneal dialysis patients. Ren Fail 2024; 46:2324071. [PMID: 38494197 PMCID: PMC10946267 DOI: 10.1080/0886022x.2024.2324071] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/19/2024] Open
Abstract
INTRODUCTION The study presented here aimed to establish a predictive model for heart failure (HF) and all-cause mortality in peritoneal dialysis (PD) patients with machine learning (ML) algorithm. METHODS We retrospectively included 1006 patients who initiated PD from 2010 to 2016. XGBoost, random forest (RF), and AdaBoost were used to train models for assessing risk for 1-year and 5-year HF hospitalization and mortality. The performance was validated using fivefold cross-validation. The optimal ML algorithm was used to construct the models to predictive the risk of the HF and all-cause mortality. The prediction performance of ML methods and Cox regression was compared. RESULTS Over a median follow-up of 49 months. Two hundred and ninety-eight patients developed HF required hospitalization; 199 patients died during the follow-up. The RF model (AUC = 0.853) was the best performing model for predicting HF, and the XGBoost model (AUC = 0.871) was the best model for predicting mortality. Baseline moderate or severe renal disease, systolic blood pressure (SBP), body mass index (BMI), age, Charlson Comorbidity Index (CCI) score were strongly associated with HF hospitalization, whereas age, CCI score, creatinine, age, high-density lipoprotein cholesterol (HDL-C), total cholesterol, baseline estimated glomerular filtration rate (eGFR) were the most significant predictors of mortality. For all the above endpoints, the ML models demonstrated better discrimination than Cox regression. CONCLUSIONS We developed and validated a novel method to predict the risk factors of HF and all-cause mortality that integrates readily available clinical, laboratory, and electrocardiographic variables to predict the risk of HF among PD patients.
Collapse
Affiliation(s)
- Liping Xu
- Department of Nephrology, The Second Affiliated Hospital of Xiamen Medical College, Xiamen, China
| | - Fang Cao
- Department of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
- Department of Nursing, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
| | - Lian Wang
- Department of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
| | - Weihua Liu
- Department of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
| | - Meizhu Gao
- Department of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
| | - Li Zhang
- Department of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
| | - Fuyuan Hong
- Department of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
| | - Miao Lin
- Department of Nephrology, Provincial Clinical College, Fujian Medical University, Fujian Provincial Hospital, Fuzhou, Fujian, China
| |
Collapse
|
2
|
Mo S, Huang C, Wang Y, Zhao H, Wei H, Qin H, Jiang H, Qin S. Construction and validation of an endoscopic ultrasonography-based ultrasomics nomogram for differentiating pancreatic neuroendocrine tumors from pancreatic cancer. Front Oncol 2024; 14:1359364. [PMID: 38854733 PMCID: PMC11158619 DOI: 10.3389/fonc.2024.1359364] [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: 12/21/2023] [Accepted: 05/06/2024] [Indexed: 06/11/2024] Open
Abstract
Objectives To develop and validate various ultrasomics models based on endoscopic ultrasonography (EUS) for retrospective differentiating pancreatic neuroendocrine tumors (PNET) from pancreatic cancer. Methods A total of 231 patients, comprising 127 with pancreatic cancer and 104 with PNET, were retrospectively enrolled. These patients were randomly divided into either a training or test cohort at a ratio of 7:3. Ultrasomics features were extracted from conventional EUS images, focusing on delineating the region of interest (ROI) for pancreatic lesions. Subsequently, dimensionality reduction of the ultrasomics features was performed by applying the Mann-Whitney test and least absolute shrinkage and selection operator (LASSO) algorithm. Eight machine learning algorithms, namely logistic regression (LR), light gradient boosting machine (LightGBM), multilayer perceptron (MLP), random forest (RF), extra trees, k nearest neighbors (KNN), support vector machine (SVM), and extreme gradient boosting (XGBoost), were employed to train prediction models using nonzero coefficient features. The optimal ultrasomics model was determined using a ROC curve and utilized for subsequent analysis. Clinical-ultrasonic features were assessed using both univariate and multivariate logistic regression. An ultrasomics nomogram model, integrating both ultrasomics and clinical-ultrasonic features, was developed. Results A total of 107 EUS-based ultrasomics features were extracted, and 6 features with nonzero coefficients were ultimately retained. Among the eight ultrasomics models based on machine learning algorithms, the RF model exhibited superior performance with an AUC= 0.999 (95% CI 0.9977 - 1.0000) in the training cohort and an AUC= 0.649 (95% CI 0.5215 - 0.7760) in the test cohort. A clinical-ultrasonic model was established and evaluated, yielding an AUC of 0.999 (95% CI 0.9961 - 1.0000) in the training cohort and 0.847 (95% CI 0.7543 - 0.9391) in the test cohort. Subsequently, the ultrasomics nomogram demonstrated a significant improvement in prediction accuracy in the test cohort, as evidenced by an AUC of 0.884 (95% CI 0.8047 - 0.9635) and confirmed by the Delong test. The calibration curve and decision curve analysis (DCA) depicted this ultrasomics nomogram demonstrated superior accuracy. They also yielded the highest net benefit for clinical decision-making compared to alternative models. Conclusions A novel ultrasomics nomogram was proposed and validated, that integrated clinical-ultrasonic and ultrasomics features obtained through EUS, aiming to accurately and efficiently identify pancreatic cancer and PNET.
Collapse
Affiliation(s)
- Shuangyang Mo
- Gastroenterology Department, Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, China
- Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Cheng Huang
- Oncology Department, Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, China
| | - Yingwei Wang
- Gastroenterology Department, Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, China
| | - Huaying Zhao
- Gastroenterology Department, Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, China
| | - Haixiao Wei
- Gastroenterology Department, Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, China
| | - Haiyan Qin
- Gastroenterology Department, Liuzhou People’s Hospital Affiliated to Guangxi Medical University, Liuzhou, China
| | - Haixing Jiang
- Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| | - Shanyu Qin
- Gastroenterology Department, The First Affiliated Hospital of Guangxi Medical University, Nanning, China
| |
Collapse
|
3
|
Fan G, Yang S, Qin J, Huang L, Li Y, Liu H, Liao X. Machine Learning Predict Survivals of Spinal and Pelvic Ewing's Sarcoma with the SEER Database. Global Spine J 2024; 14:1125-1136. [PMID: 36281905 PMCID: PMC11289541 DOI: 10.1177/21925682221134049] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022] Open
Abstract
STUDY DESIGN Retrospective Cohort Study. OBJECTIVES This study aimed to develop survival prediction models for spinal Ewing's sarcoma (EWS) based on machine learning (ML). METHODS We extracted the SEER registry's clinical data of EWS diagnosed between 1975 and 2016. Three feature selection methods extracted clinical features. Four ML algorithms (Cox, random survival forest (RSF), CoxBoost, DeepCox) were trained to predict the overall survival (OS) and cancer-specific survival (CSS) of spinal EWS. The concordance index (C-index), integrated Brier score (IBS) and mean area under the curves (AUC) were used to assess the prediction performance of different ML models. The top initial ML models with best performance from each evaluation index (C-index, IBS and mean AUC) were finally stacked to ensemble models which were compared with the traditional TNM stage model by 3-/5-/10-year Receiver Operating Characteristic (ROC) curves and Decision Curve Analysis (DCA). RESULTS A total of 741 patients with spinal EWS were identified. C-index, IBS and mean AUC for the final ensemble ML model in predicting OS were .693/0.158/0.829 during independent testing, while .719/0.171/0.819 in predicting CSS. The ensemble ML model also achieved an AUC of .705/0.747/0.851 for predicting 3-/5-/10-year OS during independent testing, while .734/0.779/0.830 for predicting 3-/5-/10-year CSS, both of which outperformed the traditional TNM stage. DCA curves also showed the advantages of the ensemble models over the traditional TNM stage. CONCLUSION ML was an effective and promising technique in predicting survival of spinal EWS, and the ensemble models were superior to the traditional TNM stage model.
Collapse
Affiliation(s)
- Guoxin Fan
- National Key Clinical Pain Medicine of China, Huazhong University of Science and Technology Union Shenzhen Hospital, China
- Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Shenzhen University Health Science Center, China
- Department of Pain Medicine and Shenzhen Municipal Key Laboratory for Pain Medicine, The 6th Affiliated Hospital of Shenzhen University Health Science Center, China
- Department of Spine Surgery, Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Sheng Yang
- Department of Orthopedics, Shanghai Tenth Peoples Hospital, Tongji University School of Medicine, China
| | - Jiaqi Qin
- Artificial Intelligence Innovation Center, Research Institute of Tsinghua, Pearl River Delta, China
| | - Longfei Huang
- Department of Orthopedics, Nanchang Hongdu Hospital of Traditional Chinese Medicine, China
| | - Yufeng Li
- Department of Sports Medicine, The Eighth Affiliated Hospital Sun Yat-sen University, China
| | - Huaqing Liu
- Artificial Intelligence Innovation Center, Research Institute of Tsinghua, Pearl River Delta, China
| | - Xiang Liao
- National Key Clinical Pain Medicine of China, Huazhong University of Science and Technology Union Shenzhen Hospital, China
- Department of Pain Medicine and Shenzhen Municipal Key Laboratory for Pain Medicine, The 6th Affiliated Hospital of Shenzhen University Health Science Center, China
| |
Collapse
|
4
|
Wang W, Wang W, Zhang D, Zeng P, Wang Y, Lei M, Hong Y, Cai C. Creation of a machine learning-based prognostic prediction model for various subtypes of laryngeal cancer. Sci Rep 2024; 14:6484. [PMID: 38499632 PMCID: PMC10948902 DOI: 10.1038/s41598-024-56687-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 03/09/2024] [Indexed: 03/20/2024] Open
Abstract
Depending on the source of the blastophore, there are various subtypes of laryngeal cancer, each with a unique metastatic risk and prognosis. The forecasting of their prognosis is a pressing issue that needs to be resolved. This study comprised 5953 patients with glottic carcinoma and 4465 individuals with non-glottic type (supraglottic and subglottic). Five clinicopathological characteristics of glottic and non-glottic carcinoma were screened using univariate and multivariate regression for CoxPH (Cox proportional hazards); for other models, 10 (glottic) and 11 (non-glottic) clinicopathological characteristics were selected using least absolute shrinkage and selection operator (LASSO) regression analysis, respectively; the corresponding survival models were established; and the best model was evaluated. We discovered that RSF (Random survival forest) was a superior model for both glottic and non-glottic carcinoma, with a projected concordance index (C-index) of 0.687 for glottic and 0.657 for non-glottic, respectively. The integrated Brier score (IBS) of their 1-year, 3-year, and 5-year time points is, respectively, 0.116, 0.182, 0.195 (glottic), and 0.130, 0.215, 0.220 (non-glottic), demonstrating the model's effective correction. We represented significant variables in a Shapley Additive Explanations (SHAP) plot. The two models are then combined to predict the prognosis for two distinct individuals, which has some effectiveness in predicting prognosis. For our investigation, we established separate models for glottic carcinoma and non-glottic carcinoma that were most effective at predicting survival. RSF is used to evaluate both glottic and non-glottic cancer, and it has a considerable impact on patient prognosis and risk factor prediction.
Collapse
Affiliation(s)
- Wei Wang
- Department of Otolaryngology-Head and Neck Surgery, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
- School of Medicine, Xiamen University, Xiamen, China
| | - Wenhui Wang
- School of Medicine, Xiamen University, Xiamen, China
| | | | - Peiji Zeng
- Department of Otolaryngology-Head and Neck Surgery, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Yue Wang
- Department of Otolaryngology-Head and Neck Surgery, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Min Lei
- School of Medicine, Xiamen University, Xiamen, China
| | - Yongjun Hong
- Department of Otolaryngology-Head and Neck Surgery, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China
| | - Chengfu Cai
- Department of Otolaryngology-Head and Neck Surgery, Zhongshan Hospital of Xiamen University, School of Medicine, Xiamen University, Xiamen, China.
- School of Medicine, Xiamen University, Xiamen, China.
- Otorhinolaryngology Head and Neck Surgery, Xiamen Medical College Affiliated Haicang Hospital, Xiamen, China.
| |
Collapse
|
5
|
Sun Y, Hu S, Li X, Wu Y. Development and Application of a Novel Machine Learning Model Predicting Pancreatic Cancer-Specific Mortality. Cureus 2024; 16:e57161. [PMID: 38681451 PMCID: PMC11056009 DOI: 10.7759/cureus.57161] [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] [Accepted: 03/28/2024] [Indexed: 05/01/2024] Open
Abstract
Precise prognostication is vital for guiding treatment decisions in people diagnosed with pancreatic cancer. Existing models depend on predetermined variables, constraining their effectiveness. Our objective was to explore a novel machine learning approach to enhance a prognostic model for predicting pancreatic cancer-specific mortality and, subsequently, to assess its performance against Cox regression models. Datasets were retrospectively collected and analyzed for 9,752 patients diagnosed with pancreatic cancer and with surgery performed. The primary outcomes were the mortality of patients with pancreatic carcinoma at one year, three years, and five years. Model discrimination was assessed using the concordance index (C-index), and calibration was assessed using Brier scores. The Survival Quilts model was compared with Cox regression models in clinical use, and decision curve analysis was done. The Survival Quilts model demonstrated robust discrimination for one-year (C-index 0.729), three-year (C-index 0.693), and five-year (C-index 0.672) pancreatic cancer-specific mortality. In comparison to Cox models, the Survival Quilts models exhibited a higher C-index up to 32 months but displayed inferior performance after 33 months. A subgroup analysis was conducted, revealing that within the subset of individuals without metastasis, the Survival Quilts models showcased a significant advantage over the Cox models. In the cohort with metastatic pancreatic cancer, Survival Quilts outperformed the Cox model before 24 months but exhibited a weaker performance after 25 months. This study has developed and validated a novel machine learning-based Survival Quilts model to predict pancreatic cancer-specific mortality that outperforms the Cox regression model.
Collapse
Affiliation(s)
- Yongji Sun
- Department of Surgery, Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, CHN
| | - Sien Hu
- Department of Surgery, Fourth Affiliated Hospital, Zhejiang University School of Medicine, Yiwu, CHN
| | - Xiawei Li
- Department of Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, CHN
| | - Yulian Wu
- Department of Surgery, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, CHN
| |
Collapse
|
6
|
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.
Collapse
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.
| |
Collapse
|
7
|
De Muzio F, Pellegrino F, Fusco R, Tafuto S, Scaglione M, Ottaiano A, Petrillo A, Izzo F, Granata V. Prognostic Assessment of Gastropancreatic Neuroendocrine Neoplasm: Prospects and Limits of Radiomics. Diagnostics (Basel) 2023; 13:2877. [PMID: 37761243 PMCID: PMC10529975 DOI: 10.3390/diagnostics13182877] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Revised: 08/28/2023] [Accepted: 08/30/2023] [Indexed: 09/29/2023] Open
Abstract
Neuroendocrine neoplasms (NENs) are a group of lesions originating from cells of the diffuse neuroendocrine system. NENs may involve different sites, including the gastrointestinal tract (GEP-NENs). The incidence and prevalence of GEP-NENs has been constantly rising thanks to the increased diagnostic power of imaging and immuno-histochemistry. Despite the plethora of biochemical markers and imaging techniques, the prognosis and therapeutic choice in GEP-NENs still represents a challenge, mainly due to the great heterogeneity in terms of tumor lesions and clinical behavior. The concept that biomedical images contain information about tissue heterogeneity and pathological processes invisible to the human eye is now well established. From this substrate comes the idea of radiomics. Computational analysis has achieved promising results in several oncological settings, and the use of radiomics in different types of GEP-NENs is growing in the field of research, yet with conflicting results. The aim of this narrative review is to provide a comprehensive update on the role of radiomics on GEP-NEN management, focusing on the main clinical aspects analyzed by most existing reports: predicting tumor grade, distinguishing NET from other tumors, and prognosis assessment.
Collapse
Affiliation(s)
- Federica De Muzio
- Department of Medicine and Health Sciences “V. Tiberio”, University of Molise, 86100 Campobasso, Italy;
| | | | - Roberta Fusco
- Medical Oncology Division, Igea SpA, 80013 Napoli, Italy;
| | - Salvatore Tafuto
- Unit of Sarcomi e Tumori Rari, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy;
| | - Mariano Scaglione
- Department of Medical, Surgical and Experimental Sciences, University of Sassari, 07100 Sassari, Italy
| | - Alessandro Ottaiano
- Unit for Innovative Therapies of Abdominal Metastastes, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy;
| | - Antonella Petrillo
- Division of Radiology, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy;
| | - Francesco Izzo
- Division of Hepatobiliary Surgery, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy
| | - Vincenza Granata
- Division of Radiology, Istituto Nazionale Tumori, IRCCS, Fondazione G. Pascale, 80131 Naples, Italy;
| |
Collapse
|
8
|
Murakami M, Fujimori N, Nakata K, Nakamura M, Hashimoto S, Kurahara H, Nishihara K, Abe T, Hashigo S, Kugiyama N, Ozawa E, Okamoto K, Ishida Y, Okano K, Takaki R, Shimamatsu Y, Ito T, Miki M, Oza N, Yamaguchi D, Yamamoto H, Takedomi H, Kawabe K, Akashi T, Miyahara K, Ohuchida J, Ogura Y, Nakashima Y, Ueki T, Ishigami K, Umakoshi H, Ueda K, Oono T, Ogawa Y. Machine learning-based model for prediction and feature analysis of recurrence in pancreatic neuroendocrine tumors G1/G2. J Gastroenterol 2023; 58:586-597. [PMID: 37099152 DOI: 10.1007/s00535-023-01987-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/17/2023] [Accepted: 03/28/2023] [Indexed: 04/27/2023]
Abstract
BACKGROUND Pancreatic neuroendocrine neoplasms (PanNENs) are a heterogeneous group of tumors. Although the prognosis of resected PanNENs is generally considered to be good, a relatively high recurrence rate has been reported. Given the scarcity of large-scale reports about PanNEN recurrence due to their rarity, we aimed to identify the predictors for recurrence in patients with resected PanNENs to improve prognosis. METHODS We established a multicenter database of 573 patients with PanNENs, who underwent resection between January 1987 and July 2020 at 22 Japanese centers, mainly in the Kyushu region. We evaluated the clinical characteristics of 371 patients with localized non-functioning pancreatic neuroendocrine tumors (G1/G2). We also constructed a machine learning-based prediction model to analyze the important features to determine recurrence. RESULTS Fifty-two patients experienced recurrence (14.0%) during the follow-up period, with the median time of recurrence being 33.7 months. The random survival forest (RSF) model showed better predictive performance than the Cox proportional hazards regression model in terms of the Harrell's C-index (0.841 vs. 0.820). The Ki-67 index, residual tumor, WHO grade, tumor size, and lymph node metastasis were the top five predictors in the RSF model; tumor size above 20 mm was the watershed with increased recurrence probability, whereas the 5-year disease-free survival rate decreased linearly as the Ki-67 index increased. CONCLUSIONS Our study revealed the characteristics of resected PanNENs in real-world clinical practice. Machine learning techniques can be powerful analytical tools that provide new insights into the relationship between the Ki-67 index or tumor size and recurrence.
Collapse
Affiliation(s)
- Masatoshi Murakami
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Nao Fujimori
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan.
| | - Kohei Nakata
- Department of Surgery and Oncology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Masafumi Nakamura
- Department of Surgery and Oncology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Shinichi Hashimoto
- Digestive and Lifestyle Diseases, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Hiroshi Kurahara
- Department of Digestive Surgery, Breast and Thyroid Surgery, Kagoshima University Graduate School of Medical and Dental Sciences, Kagoshima, Japan
| | - Kazuyoshi Nishihara
- Department of Surgery, Kitakyushu Municipal Medical Center, Kitakyushu, Japan
| | - Toshiya Abe
- Department of Surgery, Kitakyushu Municipal Medical Center, Kitakyushu, Japan
| | - Shunpei Hashigo
- Department of Gastroenterology and Hepatology, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Naotaka Kugiyama
- Department of Gastroenterology and Hepatology, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan
| | - Eisuke Ozawa
- Department of Gastroenterology and Hepatology, Nagasaki University Graduate School of Biomedical Sciences, Nagasaki, Japan
| | - Kazuhisa Okamoto
- Department of Gastroenterology, Faculty of Medicine, Oita University, Oita, Japan
| | - Yusuke Ishida
- Department of Gastroenterology and Medicine, Faculty of Medicine, Fukuoka University, Fukuoka, Japan
| | - Keiichi Okano
- Department of Gastroenterological Surgery, Faculty of Medicine, Kagawa University, Kita-gun, Japan
| | - Ryo Takaki
- Department of Gastroenterology, Urasoe General Hospital, Urasoe, Japan
| | - Yutaka Shimamatsu
- Division of Gastroenterology, Department of Medicine, Kurume University School of Medicine, Kurume, Japan
| | - Tetsuhide Ito
- Neuroendocrine Tumor Centre, Fukuoka Sanno Hospital, Fukuoka, Japan
- Department of Gastroenterology, Graduate School of Medical Sciences, International University of Health and Welfare, Fukuoka, Japan
| | - Masami Miki
- Department of Gastroenterology, National Hospital Organization Kyushu Cancer Center, Fukuoka, Japan
| | - Noriko Oza
- Department of Hepato-Biliary-Pancreatology, Saga-Ken Medical Centre Koseikan, Saga, Japan
| | - Daisuke Yamaguchi
- Department of Gastroenterology, National Hospital Organization Ureshino Medical Center, Ureshino, Japan
| | | | - Hironobu Takedomi
- Division of Gastroenterology, Department of Internal Medicine, Faculty of Medicine, Saga University, Saga, Japan
| | - Ken Kawabe
- Department of Gastroenterology, National Hospital Organization Kyushu Medical Center, Fukuoka, Japan
| | - Tetsuro Akashi
- Department of Internal Medicine, Saiseikai Fukuoka General Hospital, Fukuoka, Japan
| | - Koichi Miyahara
- Department of Internal Medicine, Karatsu Red Cross Hospital, Karatsu, Japan
| | - Jiro Ohuchida
- Department of Surgery, Miyazaki Prefectural Miyazaki Hospital, Miyazaki, Japan
| | - Yasuhiro Ogura
- Department of Surgery, Fukuoka Red Cross Hospital, Fukuoka, Japan
| | - Yohei Nakashima
- Department of Surgery, Japan Community Health Care Organization, Kyushu Hospital, Kitakyushu, Japan
| | - Toshiharu Ueki
- Department of Gastroenterology, Fukuoka University Chikushi Hospital, Chikushino, Japan
| | - Kousei Ishigami
- Department of Clinical Radiology, Graduate School of Medical Sciences, Kyushu University, Fukuoka, Japan
| | - Hironobu Umakoshi
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Keijiro Ueda
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Takamasa Oono
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| | - Yoshihiro Ogawa
- Department of Medicine and Bioregulatory Science, Graduate School of Medical Sciences, Kyushu University, 3-1-1 Maidashi, Higashi-Ku, Fukuoka, 812-8582, Japan
| |
Collapse
|
9
|
Yu W, Lu Y, Shou H, Xu H, Shi L, Geng X, Song T. A 5‐year survival status prognosis of nonmetastatic cervical cancer patients through machine learning algorithms. Cancer Med 2022; 12:6867-6876. [PMID: 36479910 PMCID: PMC10067071 DOI: 10.1002/cam4.5477] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2022] [Revised: 10/31/2022] [Accepted: 11/11/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Prediction models with high accuracy rates for nonmetastatic cervical cancer (CC) patients are limited. This study aimed to construct and compare predictive models on the basis of machine learning (ML) algorithms for predicting the 5-year survival status of CC patients through using the Surveillance, Epidemiology, and End Results public database of the National Cancer Institute. METHODS The data registered from 2004 to 2016 were extracted and randomly divided into training and validation cohorts (8:2). The least absolute shrinkage and selection operator (LASSO) regression was employed to identify significant factors. Then, four predictive models were constructed, including logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost). The predictive models were evaluated and compared using Receiver-operating characteristics with areas under the curves (AUCs) and decision curve analysis (DCA), respectively. RESULTS A total of 13,802 patients were involved and classified into training (N = 11,041) and validation (N = 2761) cohorts. By using the LASSO regression method, seven factors were identified. In the training cohort, the XGBoost model showed the best performance (AUC = 0.8400) compared to the other three models (all p < 0.05 by Delong's test). In the validation cohort, the XGBoost model also demonstrated a superior prediction ability (AUC = 0.8365) than LR and SVM models (both p < 0.05 by Delong's test), although the difference was not statistically significant between the XGBoost and the RF models (p = 0.4251 by Delong's test). Based on the DCA results, the XGBoost model was also superior, and feature importance analysis indicated that the tumor stage was the most important variable among the seven factors. CONCLUSIONS The XGBoost model proved to be an effective algorithm with better prediction abilities. This model is proposed to support better decision-making for nonmetastatic CC patients in the future.
Collapse
Affiliation(s)
- Wenke Yu
- Department of Radiology Qingchun Hospital of Zhejiang Province Hangzhou Zhejiang China
| | - Yanwei Lu
- Cancer Center, Department of Radiation Oncology Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College Hangzhou Zhejiang China
| | - Huafeng Shou
- Department of Gynecology Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College Hangzhou Zhejiang China
| | - Hong’en Xu
- Cancer Center, Department of Radiation Oncology Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College Hangzhou Zhejiang China
| | - Lei Shi
- Cancer Center, Department of Radiation Oncology Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College Hangzhou Zhejiang China
| | - Xiaolu Geng
- Department of Radiology Qingchun Hospital of Zhejiang Province Hangzhou Zhejiang China
| | - Tao Song
- Cancer Center, Department of Radiation Oncology Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College Hangzhou Zhejiang China
| |
Collapse
|
10
|
Sex-Based Clinicopathologic and Survival Differences Among Patients with Pancreatic Neuroendocrine Tumors. J Gastrointest Surg 2022; 26:2321-2329. [PMID: 35915373 DOI: 10.1007/s11605-022-05345-6] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Accepted: 04/30/2022] [Indexed: 01/31/2023]
Abstract
INTRODUCTION Sex-based differences in survival have emerged among patients with pancreatic neuroendocrine tumors (PNETs). Mechanisms driving these differences remain poorly understood. We aimed to further characterize sex-based clinicopathologic and survival differences among patients with PNETs and correlate divergent mutational signatures in these patients. METHODS The National Cancer Database (NCDB) was queried for PNET patients diagnosed 2004-2017 who underwent surgery. Clinicopathologic features were analyzed by sex. The overall survival (OS) of men and women by disease stage was compared using the Kaplan-Meier method. Differences in PNET mutational signatures were analyzed by querying the American Association for Cancer Research Genomics Evidence Neoplasia Information (AACR-GENIE) Cohort v11.0-public. Frequencies of mutational signatures were compared by Fischer's exact (FE) test, adjusting for multiple testing via the Benjamini-Hochberg correction. RESULTS About 15,202 patients met inclusion criteria from the NCDB; 51.9% were men and 48.1% were women. Men more frequently had tumors > 2 cm than women and more commonly had poorly or undifferentiated tumors. Despite this, lymph node positivity and distant metastases were similar. Differences in OS were only seen among those with early stage rather than stage 3 or 4 disease. MEN1 and DAXX mutations were more frequent among men with PNETs, whereas TP53 mutations were more frequent among women when assessed by FE test. However, neither of these mutational differences maintained statistical significance when adjusted for multiple testing. CONCLUSION Compared to women, men have larger tumors but similar rates of distant metastases at time of surgery. OS differences appear to be driven by patients with early-stage disease without clearly identifiable differences in mutational signatures between the sexes.
Collapse
|
11
|
Peng J, Lu Y, Chen L, Qiu K, Chen F, Liu J, Xu W, Zhang W, Zhao Y, Yu Z, Ren J. The prognostic value of machine learning techniques versus cox regression model for head and neck cancer. Methods 2022; 205:123-132. [PMID: 35798257 DOI: 10.1016/j.ymeth.2022.07.001] [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: 12/08/2021] [Revised: 05/18/2022] [Accepted: 07/01/2022] [Indexed: 10/17/2022] Open
Abstract
BACKGROUND Accurate prognostic prediction for head and neck cancer (HNC) is important for the improvement of clinical management. We aimed to compare the prognostic value of various machine learning techniques (MLTs) and statistical Cox regression model for different types of HNC. METHODS Clinical data of HNC patients were extracted from the Surveillance, Epidemiology, and End Results (SEER) database from 1974 to 2016. The prediction performance of five ML models, including random forest (RF), gradient boosting decision tree (GBDT), support vector machine (SVM), neural network (NN) and deep learning (DL), were compared with the statistical Cox regression model by estimating the concordance index (C-index), integrated Brier score (IBS), time-dependent receiver operating characteristic (ROC) curve and the area under the curve (AUC). RESULTS Our results showed that the RF model outperformed all other models in prognostic prediction for all tumor sites of HNC, particularly for major salivary gland cancer (MSGC, C-index: 88.730 ± 0.8700, IBS: 7.680 ± 0.4800), oral cavity cancer (OCC, C-index: 84.250 ± 0.6700, IBS: 11.480 ± 0.3300) and oropharyngeal cancer (OPC, C-index: 82.510 ± 0.5400, IBS: 10.120 ± 0.1400). Meanwhile, we analyzed the importance of each clinical variable in the RF model, in which age and tumor size presented the strongest positive prognostic effects. Additionally, similar results can be observed in the internal (6th edition of the AJCC TNM staging system cohort) and external validations (the TCGA HNC cohort). CONCLUSIONS The RF model is a promising prognostic prediction tool for HNC patients, regardless of the anatomic subsites.
Collapse
Affiliation(s)
- Jiajia Peng
- Department of Oto-Rhino-Laryngology, West China Hospital, Sichuan University, Chengdu, China; West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yongmei Lu
- Department of Computer Science, Sichuan University, Chengdu, China
| | - Li Chen
- Department of Computer Science, Sichuan University, Chengdu, China
| | - Ke Qiu
- Department of Oto-Rhino-Laryngology, West China Hospital, Sichuan University, Chengdu, China
| | - Fei Chen
- Department of Oto-Rhino-Laryngology, West China Hospital, Sichuan University, Chengdu, China
| | - Jun Liu
- Department of Oto-Rhino-Laryngology, West China Hospital, Sichuan University, Chengdu, China
| | - Wei Xu
- Department of Computer Science, Sichuan University, Chengdu, China
| | - Wei Zhang
- West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China
| | - Yu Zhao
- Department of Oto-Rhino-Laryngology, West China Hospital, Sichuan University, Chengdu, China; West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China.
| | - Zhonghua Yu
- Department of Computer Science, Sichuan University, Chengdu, China.
| | - Jianjun Ren
- Department of Oto-Rhino-Laryngology, West China Hospital, Sichuan University, Chengdu, China; West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, China; Department of Biostatistics, Princess Margaret Cancer Centre and Dalla Lana School of Public Health, Toronto, Ontario, Canada.
| |
Collapse
|
12
|
Chen X, Yang J, Lu Z, Ding Y. A 70‑RNA model based on SVR and RFE for predicting the pancreatic cancer clinical prognosis. Methods 2022; 204:278-285. [PMID: 35248692 DOI: 10.1016/j.ymeth.2022.02.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 02/09/2022] [Accepted: 02/27/2022] [Indexed: 12/12/2022] Open
Abstract
Researches on the prognosis of pancreatic cancer is of great significance to improve the patient treatment effect and survival. Current researches mainly focus on the prediction of the survival status and the determination of prognostic markers. Each patient has its own characteristics, there is no report about the prediction of survival time. However, accurate prediction of survival time is critical for personalized medicine. In this paper, a hybrid algorithm of Support Vector Regression (SVR) and Recursive Feature Elimination (RFE) was used to construct a quantitative prediction model of Overall Survival (OS) for pancreatic cancer patients, 70 RNAs related to OS were determined, including 33 mRNAs, 28 lncRNAs, and 9 miRNAs. The results of 10-fold cross-validation (R2 is 0.9693) and the generalization ability (R2 is 0.9666) showed that the model has reliable predictive performance and these 70 RNAs are important factors influencing the OS of pancreatic cancer patients. To further study the relationship between RNA-RNA interaction and the survival, competitive endogenous RNA (ceRNA) regulation network was constructed. Degree centrality, betweenness centrality and closeness centrality of nodes in the ceRNA network showed that hsa-mir-570, hsa-mir-944, hsa-mir-6506, hsa-mir-3136, MMP16, PLGLB2, HPGD, FUT1, MFSD2A, SULT1E1, SLC13A5, ZNF488, F2RL2, TNFRSF8, TNFSF11, FHDC1, ISLR2 and THSD7B are hub nodes, which are key RNAs closely determining the OS of pancreatic cancer patients.
Collapse
Affiliation(s)
- Xu Chen
- School of Science, Jiangnan University, Wuxi, Jiangsu 214122, PR China; Laboratory of Media Design and Software Technology, Jiangnan University, Wuxi, Jiangsu 214122, PR China
| | - Jing Yang
- School of Science, Jiangnan University, Wuxi, Jiangsu 214122, PR China; Laboratory of Media Design and Software Technology, Jiangnan University, Wuxi, Jiangsu 214122, PR China
| | - Zhengshu Lu
- School of Science, Jiangnan University, Wuxi, Jiangsu 214122, PR China; Laboratory of Media Design and Software Technology, Jiangnan University, Wuxi, Jiangsu 214122, PR China
| | - Yanrui Ding
- School of Science, Jiangnan University, Wuxi, Jiangsu 214122, PR China; Key Laboratory of Industrial Biotechnology, Jiangnan University, Wuxi, Jiangsu, 214122, PR China.
| |
Collapse
|
13
|
Painuli D, Bhardwaj S, Köse U. Recent advancement in cancer diagnosis using machine learning and deep learning techniques: A comprehensive review. Comput Biol Med 2022; 146:105580. [PMID: 35551012 DOI: 10.1016/j.compbiomed.2022.105580] [Citation(s) in RCA: 41] [Impact Index Per Article: 20.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2022] [Revised: 04/14/2022] [Accepted: 04/30/2022] [Indexed: 02/07/2023]
Abstract
Being a second most cause of mortality worldwide, cancer has been identified as a perilous disease for human beings, where advance stage diagnosis may not help much in safeguarding patients from mortality. Thus, efforts to provide a sustainable architecture with proven cancer prevention estimate and provision for early diagnosis of cancer is the need of hours. Advent of machine learning methods enriched cancer diagnosis area with its overwhelmed efficiency & low error-rate then humans. A significant revolution has been witnessed in the development of machine learning & deep learning assisted system for segmentation & classification of various cancers during past decade. This research paper includes a review of various types of cancer detection via different data modalities using machine learning & deep learning-based methods along with different feature extraction techniques and benchmark datasets utilized in the recent six years studies. The focus of this study is to review, analyse, classify, and address the recent development in cancer detection and diagnosis of six types of cancers i.e., breast, lung, liver, skin, brain and pancreatic cancer, using machine learning & deep learning techniques. Various state-of-the-art technique are clustered into same group and results are examined through key performance indicators like accuracy, area under the curve, precision, sensitivity, dice score on benchmark datasets and concluded with future research work challenges.
Collapse
Affiliation(s)
- Deepak Painuli
- Department of Computer Science and Engineering, Gurukula Kangri Vishwavidyalaya, Haridwar, India.
| | - Suyash Bhardwaj
- Department of Computer Science and Engineering, Gurukula Kangri Vishwavidyalaya, Haridwar, India
| | - Utku Köse
- Department of Computer Engineering, Suleyman Demirel University, Isparta, Turkey
| |
Collapse
|
14
|
Yang L, Fan X, Qin W, Xu Y, Zou B, Fan B, Wang S, Dong T, Wang L. A novel deep learning prognostic system improves survival predictions for stage III non-small cell lung cancer. Cancer Med 2022; 11:4246-4255. [PMID: 35491970 PMCID: PMC9678103 DOI: 10.1002/cam4.4782] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 03/14/2022] [Accepted: 04/10/2022] [Indexed: 11/30/2022] Open
Abstract
Background Accurate prognostic prediction plays a crucial role in the clinical setting. However, the TNM staging system fails to provide satisfactory individual survival prediction for stage III non‐small cell lung cancer (NSCLC). The performance of the deep learning network for survival prediction in stage III NSCLC has not been explored. Objectives This study aimed to develop a deep learning‐based prognostic system that could achieve better predictive performance than the existing staging system for stage III NSCLC. Methods In this study, a deep survival learning model (DSLM) for stage III NSCLC was developed based on the Surveillance, Epidemiology, and End Results (SEER) database and was independently tested with another external cohort from our institute. DSLM was compared with the Cox proportional hazard (CPH) and random survival forest (RSF) models. A new prognostic system for stage III NSCLC was also proposed based on the established deep learning model. Results The study included 16,613 patients with stage III NSCLC from the SEER database. DSLM showed the best performance in survival prediction, with a C‐index of 0.725 in the validation set, followed by RSF (0.688) and CPH (0.683). DSLM also showed C‐indices of 0.719 and 0.665 in the internal and real‐world external testing datasets, respectively. In addition, the new prognostic system based on DSLM (AUROC = 0.744) showed better performance than the TNM staging system (AUROC = 0.561). Conclusion In this study, a new, integrated deep learning‐based prognostic model was developed and evaluated for stage III NSCLC. This novel approach may be valuable in improving patient stratification and potentially provide meaningful prognostic information that contributes to personalized therapy.
Collapse
Affiliation(s)
- Linlin Yang
- Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Xinyu Fan
- Cheeloo College of Medicine, Shandong University, Jinan, China
| | - Wenru Qin
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan, China.,Weifang Medical University, Weifang, China
| | - Yiyue Xu
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan, China
| | - Bing Zou
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan, China
| | - Bingjie Fan
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan, China
| | - Shijiang Wang
- Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan, China
| | - Taotao Dong
- Department of Obstetrics and Gynecology, Qilu Hospital of Shandong University, Jinan, China
| | - Linlin Wang
- Cheeloo College of Medicine, Shandong University, Jinan, China.,Department of Radiation Oncology, Shandong Cancer Hospital and Institute, Shandong First Medical University and Shandong Academy of Medical Science, Jinan, China
| |
Collapse
|
15
|
Pantelis AG, Panagopoulou PA, Lapatsanis DP. Artificial Intelligence and Machine Learning in the Diagnosis and Management of Gastroenteropancreatic Neuroendocrine Neoplasms—A Scoping Review. Diagnostics (Basel) 2022; 12:diagnostics12040874. [PMID: 35453922 PMCID: PMC9027316 DOI: 10.3390/diagnostics12040874] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 03/27/2022] [Accepted: 03/29/2022] [Indexed: 12/21/2022] Open
Abstract
Neuroendocrine neoplasms (NENs) and tumors (NETs) are rare neoplasms that may affect any part of the gastrointestinal system. In this scoping review, we attempt to map existing evidence on the role of artificial intelligence, machine learning and deep learning in the diagnosis and management of NENs of the gastrointestinal system. After implementation of inclusion and exclusion criteria, we retrieved 44 studies with 53 outcome analyses. We then classified the papers according to the type of studied NET (26 Pan-NETs, 59.1%; 3 metastatic liver NETs (6.8%), 2 small intestinal NETs, 4.5%; colorectal, rectal, non-specified gastroenteropancreatic and non-specified gastrointestinal NETs had from 1 study each, 2.3%). The most frequently used AI algorithms were Supporting Vector Classification/Machine (14 analyses, 29.8%), Convolutional Neural Network and Random Forest (10 analyses each, 21.3%), Random Forest (9 analyses, 19.1%), Logistic Regression (8 analyses, 17.0%), and Decision Tree (6 analyses, 12.8%). There was high heterogeneity on the description of the prediction model, structure of datasets, and performance metrics, whereas the majority of studies did not report any external validation set. Future studies should aim at incorporating a uniform structure in accordance with existing guidelines for purposes of reproducibility and research quality, which are prerequisites for integration into clinical practice.
Collapse
Affiliation(s)
- Athanasios G. Pantelis
- 4th Department of Surgery, Evaggelismos General Hospital of Athens, 10676 Athens, Greece;
- Correspondence:
| | | | - Dimitris P. Lapatsanis
- 4th Department of Surgery, Evaggelismos General Hospital of Athens, 10676 Athens, Greece;
| |
Collapse
|
16
|
Abstract
Artificial intelligence (AI) has illuminated a clear path towards an evolving health-care system replete with enhanced precision and computing capabilities. Medical imaging analysis can be strengthened by machine learning as the multidimensional data generated by imaging naturally lends itself to hierarchical classification. In this Review, we describe the role of machine intelligence in image-based endocrine cancer diagnostics. We first provide a brief overview of AI and consider its intuitive incorporation into the clinical workflow. We then discuss how AI can be applied for the characterization of adrenal, pancreatic, pituitary and thyroid masses in order to support clinicians in their diagnostic interpretations. This Review also puts forth a number of key evaluation criteria for machine learning in medicine that physicians can use in their appraisals of these algorithms. We identify mitigation strategies to address ongoing challenges around data availability and model interpretability in the context of endocrine cancer diagnosis. Finally, we delve into frontiers in systems integration for AI, discussing automated pipelines and evolving computing platforms that leverage distributed, decentralized and quantum techniques.
Collapse
Affiliation(s)
| | - Ihab R Kamel
- Department of Imaging & Imaging Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Harrison X Bai
- Department of Imaging & Imaging Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| |
Collapse
|
17
|
Cheng X, Li J, Xu T, Li K, Li J. Predicting Survival of Patients With Rectal Neuroendocrine Tumors Using Machine Learning: A SEER-Based Population Study. Front Surg 2021; 8:745220. [PMID: 34805260 PMCID: PMC8595336 DOI: 10.3389/fsurg.2021.745220] [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: 07/26/2021] [Accepted: 10/04/2021] [Indexed: 12/24/2022] Open
Abstract
Background: The number of patients diagnosed with rectal neuroendocrine tumors (R-NETs) is increasing year by year. An integrated survival predictive model is required to predict the prognosis of R-NETs. The present study is aimed at exploring epidemiological characteristics of R-NETs based on a retrospective study from the Surveillance, Epidemiology, and End Results (SEER) database and predicting survival of R-NETs with machine learning. Methods: Data of patients with R-NETs were extracted from the SEER database (2000–2017), and data were also retrospectively collected from a single medical center in China. The main outcome measure was the 5-year survival status. Risk factors affecting survival were analyzed by Cox regression analysis, and six common machine learning algorithms were chosen to build the predictive models. Data from the SEER database were divided into a training set and an internal validation set according to the year 2010 as a time point. Data from China were chosen as an external validation set. The best machine learning predictive model was compared with the American Joint Committee on Cancer (AJCC) seventh staging system to evaluate its predictive performance in the internal validation dataset and external validation dataset. Results: A total of 10,580 patients from the SEER database and 68 patients from a single medical center were included in the analysis. Age, gender, race, histologic type, tumor size, tumor number, summary stage, and surgical treatment were risk factors affecting survival status. After the adjustment of parameters and algorithms comparison, the predictive model using the eXtreme Gradient Boosting (XGBoost) algorithm had the best predictive performance in the training set [area under the curve (AUC) = 0.87, 95%CI: 0.86–0.88]. In the internal validation, the predictive ability of XGBoost was better than that of the AJCC seventh staging system (AUC: 0.90 vs. 0.78). In the external validation, the XGBoost predictive model (AUC = 0.89) performed better than the AJCC seventh staging system (AUC = 0.83). Conclusions: The XGBoost algorithm had better predictive power than the AJCC seventh staging system, which had a potential value of the clinical application.
Collapse
Affiliation(s)
- Xiaoyun Cheng
- Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Key Laboratory of Gut Microbiota Translational Medicine Research, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jinzhang Li
- Department of Physiology and Pathophysiology, School of Basic Medical Sciences, Capital Medical University, Beijing, China
| | - Tianming Xu
- Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Key Laboratory of Gut Microbiota Translational Medicine Research, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Kemin Li
- Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Key Laboratory of Gut Microbiota Translational Medicine Research, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Jingnan Li
- Department of Gastroenterology, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.,Key Laboratory of Gut Microbiota Translational Medicine Research, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| |
Collapse
|
18
|
Ultrasound Images Guided under Deep Learning in the Anesthesia Effect of the Regional Nerve Block on Scapular Fracture Surgery. JOURNAL OF HEALTHCARE ENGINEERING 2021; 2021:6231116. [PMID: 34659690 PMCID: PMC8516573 DOI: 10.1155/2021/6231116] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 09/01/2021] [Accepted: 09/04/2021] [Indexed: 11/18/2022]
Abstract
In order to discuss the clinical characteristics of patients with scapular fracture, deep learning model was adopted in ultrasound images of patients to locate the anesthesia point of patients during scapular fracture surgery treated with the regional nerve block. 100 patients with scapular fracture who were hospitalized for emergency treatment in the hospital were recruited. Patients in the algorithm group used ultrasound-guided regional nerve block puncture, and patients in the control group used traditional body surface anatomy for anesthesia positioning. The ultrasound images of the scapula of the contrast group were used for the identification of the deep learning model and analysis of anesthesia acupuncture sites. The ultrasound images of the scapula anatomy of the patients in the contrast group were extracted, and the convolutional neural network model was employed for training and test. Moreover, the model performance was evaluated. It was found that the adoption of deep learning greatly improved the accuracy of the image. It took an average of 7.5 ± 2.07 minutes from the time the puncture needle touched the skin to the completion of the injection in the algorithm group (treated with artificial intelligence ultrasound positioning). The operation time of the control group (anatomical positioning) averaged 10.2 ± 2.62 min. Moreover, there was a significant difference between the two groups (p < 0.05). The method adopted in the contrast group had high positioning accuracy and good anesthesia effect, and the patients had reduced postoperative complications of patients (all P < 0.005). The deep learning model can effectively improve the accuracy of ultrasound images and measure and assist the treatment of future clinical cases of scapular fractures. While improving medical efficiency, it can also accurately identify patient fractures, which has great adoption potential in improving the effect of surgical anesthesia.
Collapse
|
19
|
A Machine Learning-Based Investigation of Gender-Specific Prognosis of Lung Cancers. ACTA ACUST UNITED AC 2021; 57:medicina57020099. [PMID: 33499377 PMCID: PMC7911834 DOI: 10.3390/medicina57020099] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 01/13/2021] [Accepted: 01/15/2021] [Indexed: 01/21/2023]
Abstract
Background and Objective: Primary lung cancer is a lethal and rapidly-developing cancer type and is one of the most leading causes of cancer deaths. Materials and Methods: Statistical methods such as Cox regression are usually used to detect the prognosis factors of a disease. This study investigated survival prediction using machine learning algorithms. The clinical data of 28,458 patients with primary lung cancers were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Results: This study indicated that the survival rate of women with primary lung cancer was often higher than that of men (p < 0.001). Seven popular machine learning algorithms were utilized to evaluate one-year, three-year, and five-year survival prediction The two classifiers extreme gradient boosting (XGB) and logistic regression (LR) achieved the best prediction accuracies. The importance variable of the trained XGB models suggested that surgical removal (feature “Surgery”) made the largest contribution to the one-year survival prediction models, while the metastatic status (feature “N” stage) of the regional lymph nodes was the most important contributor to three-year and five-year survival prediction. The female patients’ three-year prognosis model achieved a prediction accuracy of 0.8297 on the independent future samples, while the male model only achieved the accuracy 0.7329. Conclusions: This data suggested that male patients may have more complicated factors in lung cancer than females, and it is necessary to develop gender-specific diagnosis and prognosis models.
Collapse
|
20
|
Lu L, Shang Y, Mullins CS, Zhang X, Linnebacher M. Epidemiologic trends and prognostic risk factors of patients with pancreatic neuroendocrine neoplasms in the US: an updated population-based study. Future Oncol 2021; 17:549-563. [PMID: 33401958 DOI: 10.2217/fon-2020-0543] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023] Open
Abstract
Background: We aimed to evaluate the incidence, mortality and survival outcome for patients with pancreatic neuroendocrine neoplasms (pNEN). Methods: Patients with pNEN were collected from the Surveillance, Epidemiology, and End Results (SEER) database. Incidence, mortality and average annual percentage change (AAPC) were calculated using SEER stat 8.3.6 and Joinpoint software. Survival outcome was estimated using Kaplan-Meier and Cox proportional hazard model. Results: During 2000-2016, the incidence of pNEN significantly rose from 0.2647 to 1.0618 per 100,000 persons with an AAPC of 9.4; AAPC of mortality was 6.7. Prognostic improvement was revealed in 2010-2016, but not for late-stage pNEN, which had the highest risk of death. Conclusion: Efforts to improve prognosis of pNEN patients must focus on not only early detection, but also on improving therapy for late-stage disease.
Collapse
Affiliation(s)
- Lili Lu
- Department of General Surgery, Molecular Oncology & Immunotherapy, Rostock University Medical Center, Schillingallee 69, 18057, Rostock, Germany
| | - Yuru Shang
- Department of Plastic Surgery, Shenzhen University General Hospital, Xueyuan Road 1098, 518055, Shenzhen, PR China
| | - Christina Susanne Mullins
- Department of General Surgery, Molecular Oncology & Immunotherapy, Rostock University Medical Center, Schillingallee 69, 18057, Rostock, Germany
| | - Xianbin Zhang
- Department of General Surgery, Shenzhen University General Hospital & Carson International Cancer Research Centre, Xueyuan Road 1098, 518055, Shenzhen, PR China
| | - Michael Linnebacher
- Department of General Surgery, Molecular Oncology & Immunotherapy, Rostock University Medical Center, Schillingallee 69, 18057, Rostock, Germany
| |
Collapse
|
21
|
Wang J, Deng F, Zeng F, Shanahan AJ, Li WV, Zhang L. Predicting long-term multicategory cause of death in patients with prostate cancer: random forest versus multinomial model. Am J Cancer Res 2020; 10:1344-1355. [PMID: 32509383 PMCID: PMC7269775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2020] [Accepted: 04/07/2020] [Indexed: 06/11/2023] Open
Abstract
The majority of patients with prostate cancer die of non-cancer causes of death (COD). It is thus important to accurately predict multi-category COD in these patients. Random forest (RF), a popular machine learning model, has been shown useful for predicting binary cancer-specific deaths. However, its accuracy for predicting multi-category COD in cancer patients is unclear. We included patients in Surveillance, Epidemiology, and End Results-18 cancer registry-program with prostate cancer diagnosed in 2004 (followed-up through 2016). They were randomly divided into training and testing sets with equal sizes. We evaluated prediction accuracies of RF and conventional statistical/multinomial models for 6-category COD by data-encoding types using the 2-fold cross-validation approach. Among 49,864 prostate cancer patients, 29,611 (59.4%) were alive at the end of follow-up, and 5,448 (10.9%) died of cardiovascular disease, 4,607 (9.2%) of prostate cancer, 3,681 (7.4%) of non-prostate cancer, 717 (1.4%) of infection, and 5,800 (11.6%) of other causes. We predicted 6-category COD among these patients with a mean accuracy of 59.1% (n=240, 95% CI, 58.7%-59.4%) in RF models with one-hot encoding, and 50.4% (95% CI, 49.7%-51.0%) in multinomial models. Tumor characteristics, prostate-specific antigen level, and diagnosis confirmation-method were important in RF and multinomial models. In RF models, no statistical differences were found between the accuracies of training versus cross-validation phases, and those of categorical versus one-hot encoding. We here report that RF models can outperform multinomial logistic models (absolute accuracy-difference, 8.7%) in predicting long-term 6-category COD among prostate cancer patients, while pathology diagnosis itself and tumor pathology remain important factors.
Collapse
Affiliation(s)
- Jianwei Wang
- Department of Urology, Beijing Jishuitan Hospital, The Fourth Medical College of Peking UniversityBeijing, China
| | - Fei Deng
- School of Electrical and Electronic Engineering, Shanghai Institute of TechnologyShanghai, China
| | - Fuqing Zeng
- Department of Urology, Wuhan Union Hospital of Tongji Medical Collage, Huazhong University of Science and TechnologyWuhan, China
| | | | - Wei Vivian Li
- Department of Biostatistics and Epidemiology, Rutgers School of Public HealthPiscataway, NJ, USA
| | - Lanjing Zhang
- Department of Pathology, Princeton Medical CenterPlainsboro, NJ, USA
- Department of Biological Sciences, Rutgers UniversityNewark, NJ, USA
- Rutgers Cancer Institute of New JerseyNew Brunswick, NJ, USA
- Department of Chemical Biology, Ernest Mario School of Pharmacy, Rutgers UniversityPiscataway, NJ, USA
| |
Collapse
|
22
|
Tian H, Cao S, Hu M, Wang Y, Fu Q, Pan Y, Qin T. Identification of predictive factors in hepatocellular carcinoma outcome: A longitudinal study. Oncol Lett 2020; 20:765-773. [PMID: 32566003 PMCID: PMC7285798 DOI: 10.3892/ol.2020.11581] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Accepted: 02/19/2020] [Indexed: 12/24/2022] Open
Abstract
Various surgical methods impact the prognosis of patients with hepatocellular carcinoma (HCC) differently. However, clinical guidelines remain inconsistent and the relative importance of predictors of survival outcomes requires further evaluation. The present study aimed to rank the importance of predictive factors that impact the survival outcomes of patients with HCC and to compare the prognosis associated with different surgical methods based on data obtained from the Surveillance, Epidemiology and End Results database. To achieve these aims, the present study used a random forest (RF) model to detect important predictive factors associated with survival outcomes in patients with HCC. Cox regression analysis was used to compare different surgery methods. The variables included in the Cox regression model were selected based on the Gini index calculated by the RF model. Using the RF model, the present study demonstrated that surgery method, tumor size and age were the first, second and third most important factors associated with HCC prognosis, respectively. Overall, patients who underwent local tumor destruction [(hazard ratio (HR)=0.48; 95% confidence interval (CI), 0.45–0.51; P<0.001)], wedge or segmental resection (HR, 0.31; 95% CI, 0.29–0.33; P<0.001), lobectomy (HR, 0.29, 95% CI, 0.27–0.31; P<0.001) or liver transplantation (HR, 0.16; 95% CI, 0.14–0.17; P<0.001) demonstrated improved overall survival time compared with those treated with surgery, with a gradual decreasing trend observed in HRs. The present study demonstrated that the surgical method used is the most important predictor of the survival outcomes of patients with HCC. Liver transplantation resulted in the best prognosis for patients with HCC, except for those with undifferentiated tumors or distant metastasis.
Collapse
Affiliation(s)
- Huiyuan Tian
- Department of Research and Discipline Development, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Henan University People's Hospital, Zhengzhou, Henan 450003, P.R. China
| | - Shaofeng Cao
- Department of Gastroenterology, The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, P.R. China
| | - Mingxing Hu
- Department of Hepatobiliary and Pancreatic Surgery, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Henan University People's Hospital, Zhengzhou, Henan 450003, P.R. China
| | - Yuzhu Wang
- Department of Hepatobiliary and Pancreatic Surgery, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Henan University People's Hospital, Zhengzhou, Henan 450003, P.R. China
| | - Qiang Fu
- Department of Hepatobiliary and Pancreatic Surgery, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Henan University People's Hospital, Zhengzhou, Henan 450003, P.R. China
| | - Yanfeng Pan
- Department of Infectious Diseases, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan 450000, P.R. China
| | - Tao Qin
- Department of Hepatobiliary and Pancreatic Surgery, Henan Provincial People's Hospital, Zhengzhou University People's Hospital, Henan University People's Hospital, Zhengzhou, Henan 450003, P.R. China
| |
Collapse
|
23
|
Cheng CY, Tseng WL, Chang CF, Chang CH, Gau SSF. A Deep Learning Approach for Missing Data Imputation of Rating Scales Assessing Attention-Deficit Hyperactivity Disorder. Front Psychiatry 2020; 11:673. [PMID: 32765316 PMCID: PMC7379397 DOI: 10.3389/fpsyt.2020.00673] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/18/2020] [Accepted: 06/29/2020] [Indexed: 02/03/2023] Open
Abstract
A variety of tools and methods have been used to measure behavioral symptoms of attention-deficit/hyperactivity disorder (ADHD). Missing data is a major concern in ADHD behavioral studies. This study used a deep learning method to impute missing data in ADHD rating scales and evaluated the ability of the imputed dataset (i.e., the imputed data replacing the original missing values) to distinguish youths with ADHD from youths without ADHD. The data were collected from 1220 youths, 799 of whom had an ADHD diagnosis, and 421 were typically developing (TD) youths without ADHD, recruited in Northern Taiwan. Participants were assessed using the Conners' Continuous Performance Test, the Chinese versions of the Conners' rating scale-revised: short form for parent and teacher reports, and the Swanson, Nolan, and Pelham, version IV scale for parent and teacher reports. We used deep learning, with information from the original complete dataset (referred to as the reference dataset), to perform missing data imputation and generate an imputation order according to the imputed accuracy of each question. We evaluated the effectiveness of imputation using support vector machine to classify the ADHD and TD groups in the imputed dataset. The imputed dataset can classify ADHD vs. TD up to 89% accuracy, which did not differ from the classification accuracy (89%) using the reference dataset. Most of the behaviors related to oppositional behaviors rated by teachers and hyperactivity/impulsivity rated by both parents and teachers showed high discriminatory accuracy to distinguish ADHD from non-ADHD. Our findings support a deep learning solution for missing data imputation without introducing bias to the data.
Collapse
Affiliation(s)
- Chung-Yuan Cheng
- Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan.,Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
| | - Wan-Ling Tseng
- Child Study Center, Yale University School of Medicine, New Haven, CT, United States
| | - Ching-Fen Chang
- Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan
| | - Chuan-Hsiung Chang
- Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan
| | - Susan Shur-Fen Gau
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan.,Graduate Institute of Brain and Mind Sciences, and Graduate Institute of Clinical Medicine, College of Medicine, National Taiwan University, Taipei, Taiwan
| |
Collapse
|
24
|
Huang S, Yang J, Fong S, Zhao Q. Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges. Cancer Lett 2019; 471:61-71. [PMID: 31830558 DOI: 10.1016/j.canlet.2019.12.007] [Citation(s) in RCA: 225] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 12/04/2019] [Accepted: 12/06/2019] [Indexed: 02/06/2023]
Abstract
Cancer is an aggressive disease with a low median survival rate. Ironically, the treatment process is long and very costly due to its high recurrence and mortality rates. Accurate early diagnosis and prognosis prediction of cancer are essential to enhance the patient's survival rate. Developments in statistics and computer engineering over the years have encouraged many scientists to apply computational methods such as multivariate statistical analysis to analyze the prognosis of the disease, and the accuracy of such analyses is significantly higher than that of empirical predictions. Furthermore, as artificial intelligence (AI), especially machine learning and deep learning, has found popular applications in clinical cancer research in recent years, cancer prediction performance has reached new heights. This article reviews the literature on the application of AI to cancer diagnosis and prognosis, and summarizes its advantages. We explore how AI assists cancer diagnosis and prognosis, specifically with regard to its unprecedented accuracy, which is even higher than that of general statistical applications in oncology. We also demonstrate ways in which these methods are advancing the field. Finally, opportunities and challenges in the clinical implementation of AI are discussed. Hence, this article provides a new perspective on how AI technology can help improve cancer diagnosis and prognosis, and continue improving human health in the future.
Collapse
Affiliation(s)
- Shigao Huang
- Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macao, China
| | - Jie Yang
- Department of Computer and Information Science, University of Macau, Taipa, Macau, China; Chongqing Industry&Trade Polytechnic, Chongqing, China
| | - Simon Fong
- Department of Computer and Information Science, University of Macau, Taipa, Macau, China; Zhuhai Institute of Advanced Technology Chinese Academy of Sciences, Zhuhai, China.
| | - Qi Zhao
- Cancer Center, Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Taipa, Macao, China.
| |
Collapse
|
25
|
Lee L, Ito T, Jensen RT. Prognostic and predictive factors on overall survival and surgical outcomes in pancreatic neuroendocrine tumors: recent advances and controversies. Expert Rev Anticancer Ther 2019; 19:1029-1050. [PMID: 31738624 PMCID: PMC6923565 DOI: 10.1080/14737140.2019.1693893] [Citation(s) in RCA: 38] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2019] [Accepted: 11/13/2019] [Indexed: 02/06/2023]
Abstract
Introduction: Recent advances in diagnostic modalities and therapeutic agents have raised the importance of prognostic factors in predicting overall survival, as well as predictive factors for surgical outcomes, in tailoring therapeutic strategies of patients with pancreatic neuroendocrine neoplasms (panNENs).Areas covered: Numerous recent studies of panNEN patients report the prognostic values of a number of clinically related factors (clinical, laboratory, imaging, treatment-related factors), pathological factors (histological, classification, grading) and molecular factors on long-term survival. In addition, an increasing number of studies showed the usefulness of various factors, specifically biomarkers and molecular makers, in predicting recurrence and mortality related to surgical treatment. Recent findings (from the last 3 years) in each of these areas, as well as recent controversies, are reviewed.Expert commentary: The clinical importance of prognostic and predictive factors for panNENs is markedly increased for both overall outcome and post resection, as a result of recent advances in all aspects of the diagnosis, management and treatment of panNENs. Despite the proven prognostic utility of routinely used tumor grading/classification and staging systems, further studies are required to establish these novel prognostic factors to support their routine clinical use.
Collapse
Affiliation(s)
- Lingaku Lee
- Digestive Diseases Branch, NIDDK, NIH, Bethesda, MD, 20892-1804, USA
- Department of Hepato-Biliary-Pancreatology, National Kyushu Cancer Center, Fukuoka, 811-1395, Japan
| | - Tetsuhide Ito
- Neuroendocrine Tumor Centre, Fukuoka Sanno Hospital, International University of Health and Welfare, Fukuoka, 814-0001, Japan
| | - Robert T. Jensen
- Digestive Diseases Branch, NIDDK, NIH, Bethesda, MD, 20892-1804, USA
| |
Collapse
|