LASSO-Based Machine Learning Algorithm for Prediction of Lymph Node Metastasis in T1 Colorectal Cancer

Kang, Jeonghyun; Choi, Yoon Jung; Kim, Im-kyung; Lee, Hye Sun; Kim, Hogeun; Baik, Seung Hyuk; Kim, Nam Kyu; Lee, Kang Young

doi:10.4143/crt.2020.974

Cited by 119 publications

(85 citation statements)

References 30 publications

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“…Most studies report that rectal cancer patients have a statistically equal or higher rate of LNM compared with T1 CRC patients (Table 1) [6,[20][21][22][23][24][25]. Several studies presented no significant difference in the rate of LNM between colon and rectal T1 cancer patients (colon vs rectum: Oh et al, 13.1% vs 9.1%, p = 0.089; Barel et al, 6.8% vs 12.3%, p = 0.2616; Miyachi et al, 8.4% vs 11.1%, p = 0.299; and Marcias et al, 13.4% vs 16.7%, p = 0.675) [6,[23][24][25].…”

Section: Colon Vs Rectummentioning

confidence: 99%

Tumor Location as a Prognostic Factor in T1 Colorectal Cancer

Ichimasa

Kudo

Kouyama

et al. 2022

J Anus Rectum Colon

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The incidence of T1 colorectal cancer is expected to increase because of the prevalence of colorectal cancer screening and the progress of endoscopic treatment such as endoscopic submucosal dissection or endoscopic full-thickness resection. Currently, the requirement for additional surgery after endoscopic resection of T1 colorectal cancer is determined according to several treatment guidelines (in USA, Europe, and Japan) referring to the following pathological findings: lymphovascular invasion, tumor differentiation, depth of invasion, and tumor budding, all of which are reported to be risk factors for lymph node metastasis. In addition to these factors, in this review, we investigate whether tumor location, which is an objective factor, has an impact on the presence of lymph node metastasis and recurrence. From recent studies, left-sided location, especially the sigmoid colon in addition to rectum, could be a risk factor for lymph node metastasis and cancer recurrence. The treatment of T1 colorectal cancer should be managed considering these findings.

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Section: Colon Vs Rectummentioning

confidence: 99%

Tumor Location as a Prognostic Factor in T1 Colorectal Cancer

Ichimasa

Kudo

Kouyama

et al. 2022

J Anus Rectum Colon

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“…No correlations were observed analyzing the CD8 positive TILs ( 21 ). Differently, a recent study by Kang et al analyzed the distribution and mean numbers of CD3, CD8, and FOXP3 positive lymphocytes in pT1 CRC and identified a significant association between lymph node metastasis and a lower number of CD8 positive lymphocytes located within the tumor core ( 49 ). In the present study, we demonstrated a correlation between absent/mild TILs and a higher risk of nodal involvement.…”

Section: Discussionmentioning

confidence: 99%

The Importance of Being “That” Colorectal pT1: A Combined Clinico-Pathological Predictive Score to Improve Nodal Risk Stratification

et al. 2022

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The management of endoscopically resected pT1 colorectal cancer (CRC) relies on nodal metastasis risk estimation based on the assessment of specific histopathological features. Avoiding the overtreatment of metastasis-free patients represents a crucial unmet clinical need. By analyzing a consecutive series of 207 pT1 CRCs treated with colectomy and lymphadenectomy, this study aimed to develop a novel clinicopathological score to improve pT1 CRC metastasis prediction. First, we established the clinicopathological profile of metastatic cases: lymphovascular invasion (OR: 23.8; CI: 5.12–110.9) and high-grade tumor budding (OR: 5.21; CI: 1.60–16.8) correlated with an increased risk of nodal metastasis, while age at diagnosis >65 years (OR: 0.26; CI: 0.09–0.71) and high tumor-infiltrating lymphocytes (OR: 0.19; CI: 0.06–0.59) showed a protective effect. Combining these features, we built a five-tier risk score that, applied to our series, identified cases with a higher risk (score ≥ 2) of nodal metastasis (OR: 7.7; CI: 2.4–24.4). Notably, a score of 0 was only assigned to cases with no metastases (13/13 cases) and all the score 4 samples (2/2 cases) showed nodal metastases. In conclusion, we developed an effectively combined score to assess pT1 CRC nodal metastasis risk. We believe that its adoption within a multidisciplinary pT1 unit could improve patients' clinical management and limit surgical overtreatment.

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“…The LASSO regression can assist us in identifying the variables worth investigating from a huge quantity of data. 5 In this study, we explored different characteristics that may be excellent predictors for malnutrition, and we used LASSO regression to filter over 135 variables. This section of the data was then subjected to a correlation matrix analysis.…”

Section: Discussionmentioning

confidence: 99%

A Predictive Model for Qualitative Evaluation of PG-SGA in Tumor Patients Through Machine Learning

Liu¹,

Li²,

Ji³

et al. 2022

CMAR

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Objective Patient-Generated Subjective Global Assessment (PG-SGA) was a nutritional status assessment technique specifically tailored for patients with oncology. The goal of this study was to develop a machine learning (ML) prediction model for predicting PG-SGA categorization of patients with tumor. Methods From 2014 to 2020, patients at the First Hospital of Jilin University performed laboratory testing, bioelectrical impedance, physical measures, and the PG-SGA scale. A total of 8230 patients were involved in the study. Patients with missing or partial data were removed, leaving 7287 patients, of which 3743 were males and 3544 were females. ML was used to design a clinical prediction model for PG-SGA categories. Results Through the least absolute shrinkage and selection operator (LASSO) and the correlation matrix, 135 variables were screened and 6 variables were retained; ML was performed among the remaining variables. The accuracy of neural network prediction models was 70.3% and 70.4% for males and females in the training cohort, respectively, and 74.4% and 73.2% for males and females in the validation cohort, respectively. The area under curve (AUC) of males was 0.87 for PG-SGA scores “0–3”, 0.70 for PG-SGA scores “4–8” and 0.74 for PG-SGA scores “>8”. As for females, the AUC was 0.85 for PG-SGA scores “0–3”, 0.65 for PG-SGA scores “4–8” and 0.76 for PG-SGA scores “>8”. The results of confusion matrix showed that the models were of good predictive validity. The prediction model was nearly 90% accurate for predictions that do not require nutritional support. Conclusion We demonstrated that neural network learning is the best clinical prediction model using ML. The model can work as a prediction for the PG-SGA classification of patients with cancer and can be promoted further in the clinic.

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LASSO-Based Machine Learning Algorithm for Prediction of Lymph Node Metastasis in T1 Colorectal Cancer

Cited by 119 publications

References 30 publications

Tumor Location as a Prognostic Factor in T1 Colorectal Cancer

Tumor Location as a Prognostic Factor in T1 Colorectal Cancer

The Importance of Being “That” Colorectal pT1: A Combined Clinico-Pathological Predictive Score to Improve Nodal Risk Stratification

A Predictive Model for Qualitative Evaluation of PG-SGA in Tumor Patients Through Machine Learning

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