Re-Evaluation of the Survival Paradox Between Stage IIB/IIC and Stage IIIA Colon Cancer

Li, Hongbo; Fu, Guangshun; Wei, Wei; Huang, Yong; Wang, Zhenguang; Tao, Liang; Tian, Shuyun; Chen, Honggang; Zhang, Wei

doi:10.3389/fonc.2020.595107

Cited by 17 publications

(16 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The Western medicine predictors were determined according to the predictor in the reported prognostic model (25) and the actual record of the case data in this study. The details are as follows: (1) Age at diagnosis; (2) primary tumor (T stage); (3) number of positive lymph nodes; (4) whether the number of detected lymph nodes was less than 12; (5) whether there was lymphatic, vascular and nerve invasion; (6) tumor site;…”

Section: Model Predictorsmentioning

confidence: 99%

Machine learning based prognostic model of Chinese medicine affecting the recurrence and metastasis of I-III stage colorectal cancer: A retrospective study in China

Tang

Gao

et al. 2022

Front. Oncol.

View full text Add to dashboard Cite

BackgroundTo construct prognostic model of colorectal cancer (CRC) recurrence and metastasis (R&M) with traditional Chinese medicine (TCM) factors based on different machine learning (ML) methods. Aiming to offset the defects in the existing model lacking TCM factors.MethodsPatients with stage I-III CRC after radical resection were included as the model data set. The training set and the internal verification set were randomly divided at a ratio of 7: 3 by the “set aside method”. The average performance index and 95% confidence interval of the model were calculated by repeating 100 tests. Eight factors were used as predictors of Western medicine. Two types of models were constructed by taking “whether to accept TCM intervention” and “different TCM syndrome types” as TCM predictors. The model was constructed by four ML methods: logistic regression, random forest, Extreme Gradient Boosting (XGBoost) and support vector machine (SVM). The predicted target was whether R&M would occur within 3 years and 5 years after radical surgery. The area under curve (AUC) value and decision curve analysis (DCA) curve were used to evaluate accuracy and utility of the model.ResultsThe model data set consisted of 558 patients, of which 317 received TCM intervention after radical resection. The model based on the four ML methods with the TCM factor of “whether to accept TCM intervention” showed good ability in predicting R&M within 3 years and 5 years (AUC value > 0.75), and XGBoost was the best method. The DCA indicated that when the R&M probability in patients was at a certain threshold, the models provided additional clinical benefits. When predicting the R&M probability within 3 years and 5 years in the model with TCM factors of “different TCM syndrome types”, the four methods all showed certain predictive ability (AUC value > 0.70). With the exception of the model constructed by SVM, the other methods provided additional clinical benefits within a certain probability threshold.ConclusionThe prognostic model based on ML methods shows good accuracy and clinical utility. It can quantify the influence degree of TCM factors on R&M, and provide certain values for clinical decision-making.

show abstract

Section: Model Predictorsmentioning

confidence: 99%

Machine learning based prognostic model of Chinese medicine affecting the recurrence and metastasis of I-III stage colorectal cancer: A retrospective study in China

Tang

Gao

et al. 2022

Front. Oncol.

View full text Add to dashboard Cite

show abstract

“…These linear approaches may cause a phenomenon known as the "survival paradox": T4N0 CRC had a significantly worse outcome than T1-2N1 cancer regardless of adjuvant chemotherapy, 12 which may mislead clinicians into over-or underestimating the disease prognosis. 13 To deal with these challenges, methods based on machine learning (ML), a subfield of artificial intelligence with the goal of developing algorithms capable of learning from data automatically, 14 were used in the present study. ML is able to use complex algorithms for large datasets with multidimensional variables to capture high-dimensional, nonlinear relationships among clinical features to make data-driven outcome predictions.…”

Section: Introductionmentioning

confidence: 99%

“…These linear approaches may cause a phenomenon known as the “survival paradox”: T4N0 CRC had a significantly worse outcome than T1-2N1 cancer regardless of adjuvant chemotherapy, 12 which may mislead clinicians into over- or underestimating the disease prognosis. 13 …”

Section: Introductionmentioning

confidence: 99%

Machine Learning-Based Prognostic Prediction Models of Non-Metastatic Colon Cancer: Analyses Based on Surveillance, Epidemiology and End Results Database and a Chinese Cohort

Tang¹,

Gao²,

He³

et al. 2022

CMAR

View full text Add to dashboard Cite

Purpose: The present study aimed to develop prognostic prediction models based on machine learning (ML) for non-metastatic colon cancer (CRC), which can provide a precise quantitative risk assessment and serve as an assistive method for treatment strategy development. The possibility of improving prediction accuracy using nonlinear methods compared to linear methods was investigated. Patients and Methods: A cancer-specific survival (CSS) model constructed using logistic regression, extreme gradient boosting (XGBoost), and random forest algorithms was trained on the Surveillance, Epidemiology, and End Results datasets for 15,254 patients with nonmetastatic CRC (split into training [70%] and internal validation [30%] datasets) and externally validated with an outpatient cohort of 311 cases from Xiyuan Hospital in China. A Chinese cohort was also used to develop recurrence and metastasis (R&M) models for CRC patients. The experiments for each model were performed 100 times to obtain average scores and 95% confidence intervals. The model performance was evaluated using the area under the receiver operating characteristic curve (AUC) values. Results: The XGBoost approach showed the highest AUC values of 0.86 (0.84-0.88), 0.82 (0.81-0.83), and 0.81 (0.79-0.82) for one-, three-, and five-year CSS cohorts, respectively, along with a relatively high generalization ability. The XGBoost approach also performed best for the R&M model, with the AUC values of 0.71 (0.64-0.79), 0.79 (0.74-0.86), and 0.89 (0.82-0.95) for one-, three-, and five-year R&M cohorts, respectively. The rankings of predictor importance for the CSS and R&M models were different, and the higher model accuracy was associated with more prognostic predictors. Conclusion: Three different ML algorithms for developing prognostic prediction models for non-metastatic CRC were compared. The predictive performance results showed that the nonlinear XGBoost approach performed best, suggesting that it can be used for quantifying the prognostic risk. It was also demonstrated that the model performance can be improved when more prognostic predictors are considered.

show abstract

“…While for stage III CRC adjuvant systemic therapy is mandatory, stage II CRC must be firstly assessed for potential risk/benefits of therapy and prognosis. Currently, adjuvant chemotherapy for stage II CRC is recommended to patents with less than 12 lymph nodes analyzed after surgery and poor prognostic features (such as poorly differentiated histology, lymphatic/vascular invasion, positive margins, obstruction/perforation, comorbidities) [11]. Currently, the options of adjuvant therapy for high-risk stage II CRC are FOLFOX (5-fluorouracil, leucovorin, and oxaliplatin), CAPEOX (capecitabine and oxaliplatin), capecitabine, or 5-fluorouracil/leucovorin for 3-6 months.…”

Section: Introduction 1standard Of Care Of Colorectal Cancermentioning

confidence: 99%

“…Inevitably, distinct drug responses and inconsistent survival outcomes are not uncommon within a single stage, which is even more pronounced regarding stages II and III [17]. The existence of a survival paradox between stages IIB/IIC and IIIA (T1-2, N1, M0 or T1, N2, M0) is a widely known phenomenon in CRC patients, and numerous causes had been described to contribute to an inferior survival in IIB/IIC compared with that of stage IIIA [11]. One explanation might be the insufficient use of systemic adjuvant chemotherapy in stage II CRC, since it is part of the standard care of treatment used in stage III CRC [18].…”

Section: Introduction 1standard Of Care Of Colorectal Cancermentioning

confidence: 99%

Analyzing the Opportunities to Target DNA Double-Strand Breaks Repair and Replicative Stress Responses to Improve Therapeutic Index of Colorectal Cancer

Tomasini

Guecheva²,

Leguisamo³

et al. 2021

Cancers

View full text Add to dashboard Cite

Despite the ample improvements of CRC molecular landscape, the therapeutic options still rely on conventional chemotherapy-based regimens for early disease, and few targeted agents are recommended for clinical use in the metastatic setting. Moreover, the impact of cytotoxic, targeted agents, and immunotherapy combinations in the metastatic scenario is not fully satisfactory, especially the outcomes for patients who develop resistance to these treatments need to be improved. Here, we examine the opportunity to consider therapeutic agents targeting DNA repair and DNA replication stress response as strategies to exploit genetic or functional defects in the DNA damage response (DDR) pathways through synthetic lethal mechanisms, still not explored in CRC. These include the multiple actors involved in the repair of DNA double-strand breaks (DSBs) through homologous recombination (HR), classical non-homologous end joining (NHEJ), and microhomology-mediated end-joining (MMEJ), inhibitors of the base excision repair (BER) protein poly (ADP-ribose) polymerase (PARP), as well as inhibitors of the DNA damage kinases ataxia-telangiectasia and Rad3 related (ATR), CHK1, WEE1, and ataxia-telangiectasia mutated (ATM). We also review the biomarkers that guide the use of these agents, and current clinical trials with targeted DDR therapies.

show abstract

Re-Evaluation of the Survival Paradox Between Stage IIB/IIC and Stage IIIA Colon Cancer

Cited by 17 publications

References 25 publications

Machine learning based prognostic model of Chinese medicine affecting the recurrence and metastasis of I-III stage colorectal cancer: A retrospective study in China

Machine learning based prognostic model of Chinese medicine affecting the recurrence and metastasis of I-III stage colorectal cancer: A retrospective study in China

Machine Learning-Based Prognostic Prediction Models of Non-Metastatic Colon Cancer: Analyses Based on Surveillance, Epidemiology and End Results Database and a Chinese Cohort

Analyzing the Opportunities to Target DNA Double-Strand Breaks Repair and Replicative Stress Responses to Improve Therapeutic Index of Colorectal Cancer

Contact Info

Product

Resources

About