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Machine Learning Predicts Oxaliplatin Benefit in Early Colon Cancer
DC Field | Value | Language |
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dc.contributor.author | 백순명 | - |
dc.date.accessioned | 2025-02-03T08:28:09Z | - |
dc.date.available | 2025-02-03T08:28:09Z | - |
dc.date.issued | 2024-05 | - |
dc.identifier.issn | 0732-183X | - |
dc.identifier.uri | https://ir.ymlib.yonsei.ac.kr/handle/22282913/201695 | - |
dc.description.abstract | Purpose: A combination of fluorouracil, leucovorin, and oxaliplatin (FOLFOX) is the standard for adjuvant therapy of resected early-stage colon cancer (CC). Oxaliplatin leads to lasting and disabling neurotoxicity. Reserving the regimen for patients who benefit from oxaliplatin would maximize efficacy and minimize unnecessary adverse side effects. Methods: We trained a new machine learning model, referred to as the colon oxaliplatin signature (COLOXIS) model, for predicting response to oxaliplatin-containing regimens. We examined whether COLOXIS was predictive of oxaliplatin benefits in the CC adjuvant setting among 1,065 patients treated with 5-fluorouracil plus leucovorin (FULV; n = 421) or FULV + oxaliplatin (FOLFOX; n = 644) from NSABP C-07 and C-08 phase III trials. The COLOXIS model dichotomizes patients into COLOXIS+ (oxaliplatin responder) and COLOXIS- (nonresponder) groups. Eight-year recurrence-free survival was used to evaluate oxaliplatin benefits within each of the groups, and the predictive value of the COLOXIS model was assessed using the P value associated with the interaction term (int P) between the model prediction and the treatment effect. Results: Among 1,065 patients, 526 were predicted as COLOXIS+ and 539 as COLOXIS-. The COLOXIS+ prediction was associated with prognosis for FULV-treated patients (hazard ratio [HR], 1.52 [95% CI, 1.07 to 2.15]; P = .017). The model was predictive of oxaliplatin benefits: COLOXIS+ patients benefited from oxaliplatin (HR, 0.65 [95% CI, 0.48 to 0.89]; P = .0065; int P = .03), but COLOXIS- patients did not (COLOXIS- HR, 1.08 [95% CI, 0.77 to 1.52]; P = .65). Conclusion: The COLOXIS model is predictive of oxaliplatin benefits in the CC adjuvant setting. The results provide evidence supporting a change in CC adjuvant therapy: reserve oxaliplatin only for COLOXIS+ patients, but further investigation is warranted. Trial registration: ClinicalTrials.gov NCT00096278 NCT00004931. | - |
dc.description.statementOfResponsibility | restriction | - |
dc.language | English | - |
dc.publisher | American Society of Clinical Oncology | - |
dc.relation.isPartOf | JOURNAL OF CLINICAL ONCOLOGY | - |
dc.rights | CC BY-NC-ND 2.0 KR | - |
dc.subject.MESH | Adult | - |
dc.subject.MESH | Aged | - |
dc.subject.MESH | Antineoplastic Combined Chemotherapy Protocols* / therapeutic use | - |
dc.subject.MESH | Chemotherapy, Adjuvant | - |
dc.subject.MESH | Clinical Trials, Phase III as Topic | - |
dc.subject.MESH | Colonic Neoplasms* / drug therapy | - |
dc.subject.MESH | Colonic Neoplasms* / mortality | - |
dc.subject.MESH | Colonic Neoplasms* / pathology | - |
dc.subject.MESH | Female | - |
dc.subject.MESH | Fluorouracil* / administration & dosage | - |
dc.subject.MESH | Fluorouracil* / therapeutic use | - |
dc.subject.MESH | Humans | - |
dc.subject.MESH | Leucovorin* / administration & dosage | - |
dc.subject.MESH | Leucovorin* / therapeutic use | - |
dc.subject.MESH | Machine Learning* | - |
dc.subject.MESH | Male | - |
dc.subject.MESH | Middle Aged | - |
dc.subject.MESH | Neoplasm Staging | - |
dc.subject.MESH | Organoplatinum Compounds / administration & dosage | - |
dc.subject.MESH | Organoplatinum Compounds / therapeutic use | - |
dc.subject.MESH | Oxaliplatin* / administration & dosage | - |
dc.subject.MESH | Oxaliplatin* / therapeutic use | - |
dc.title | Machine Learning Predicts Oxaliplatin Benefit in Early Colon Cancer | - |
dc.type | Article | - |
dc.contributor.college | College of Medicine (의과대학) | - |
dc.contributor.department | BioMedical Science Institute (의생명과학부) | - |
dc.contributor.googleauthor | Lujia Chen | - |
dc.contributor.googleauthor | Ying Wang | - |
dc.contributor.googleauthor | Chunhui Cai | - |
dc.contributor.googleauthor | Ying Ding | - |
dc.contributor.googleauthor | Rim S Kim | - |
dc.contributor.googleauthor | Corey Lipchik | - |
dc.contributor.googleauthor | Patrick G Gavin | - |
dc.contributor.googleauthor | Greg Yothers | - |
dc.contributor.googleauthor | Carmen J Allegra | - |
dc.contributor.googleauthor | Nicholas J Petrelli | - |
dc.contributor.googleauthor | Jennifer Marie Suga | - |
dc.contributor.googleauthor | Judith O Hopkins | - |
dc.contributor.googleauthor | Naoyuki G Saito | - |
dc.contributor.googleauthor | Terry Evans | - |
dc.contributor.googleauthor | Srinivas Jujjavarapu | - |
dc.contributor.googleauthor | Norman Wolmark | - |
dc.contributor.googleauthor | Peter C Lucas | - |
dc.contributor.googleauthor | Soonmyung Paik | - |
dc.contributor.googleauthor | Min Sun | - |
dc.contributor.googleauthor | Katherine L Pogue-Geile | - |
dc.contributor.googleauthor | Xinghua Lu | - |
dc.identifier.doi | 10.1200/JCO.23.01080 | - |
dc.contributor.localId | A01823 | - |
dc.relation.journalcode | J01331 | - |
dc.identifier.eissn | 1527-7755 | - |
dc.identifier.pmid | 38315963 | - |
dc.identifier.url | https://ascopubs.org/doi/10.1200/JCO.23.01080 | - |
dc.contributor.alternativeName | Paik, Soon Myung | - |
dc.contributor.affiliatedAuthor | 백순명 | - |
dc.citation.volume | 42 | - |
dc.citation.number | 13 | - |
dc.citation.startPage | 1520 | - |
dc.citation.endPage | 1530 | - |
dc.identifier.bibliographicCitation | JOURNAL OF CLINICAL ONCOLOGY, Vol.42(13) : 1520-1530, 2024-05 | - |
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