Precision Surgical Therapy for Adenocarcinoma of the Esophagus and Esophagogastric Junction
Copyright © 2019 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved..
INTRODUCTION: To facilitate the initial clinical decision regarding whether to use esophagectomy alone or neoadjuvant therapy in surgical care for individual patients with adenocarcinoma of the esophagus and esophagogastric junction-information not available from randomized trials-a machine-learning analysis was performed using worldwide real-world data on patients undergoing different therapies for this rare adenocarcinoma.
METHODS: Using random forest technology in a sequential analysis, we (1) identified eligibility for each of four therapies among 13,365 patients: esophagectomy alone (n = 6649), neoadjuvant therapy (n = 4706), esophagectomy and adjuvant therapy (n = 998), and neoadjuvant and adjuvant therapy (n = 1022); (2) performed survival analyses incorporating interactions of patient and cancer characteristics with therapy; (3) determined optimal therapy as that predicted to maximize lifetime within 10 years (restricted mean survival time; RMST) for each patient; and (4) compared lifetime gained from optimal versus actual therapies.
RESULTS: Actual therapy was optimal in 61% of those receiving esophagectomy alone; neoadjuvant therapy was optimal for 36% receiving neoadjuvant therapy. Many patients were predicted to benefit from postoperative adjuvant therapy. Total RMST for actual therapy received was 58,825 years. Had patients received optimal therapy, total RMST was predicted to be 62,982 years, a 7% gain.
CONCLUSIONS: Average treatment effect for adenocarcinoma of the esophagus yields only crude evidence-based therapy guidelines. However, patient response to therapy is widely variable, and survival after data-driven predicted optimal therapy often differs from actual therapy received. Therapy must address an individual patient's cancer and clinical characteristics to provide precision surgical therapy for adenocarcinoma of the esophagus and esophagogastric junction.
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2019 |
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| Erschienen: |
2019 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:14 |
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| Enthalten in: |
Journal of thoracic oncology : official publication of the International Association for the Study of Lung Cancer - 14(2019), 12 vom: 27. Dez., Seite 2164-2175 |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
Rice, Thomas W [VerfasserIn] |
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| Links: |
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| Themen: |
Artificial intelligence |
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| Anmerkungen: |
Date Completed 28.08.2020 Date Revised 14.02.2024 published: Print-Electronic Citation Status MEDLINE |
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| doi: |
10.1016/j.jtho.2019.08.004 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
NLM300522207 |
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| 500 | |a Date Revised 14.02.2024 | ||
| 500 | |a published: Print-Electronic | ||
| 500 | |a Citation Status MEDLINE | ||
| 520 | |a Copyright © 2019 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved. | ||
| 520 | |a INTRODUCTION: To facilitate the initial clinical decision regarding whether to use esophagectomy alone or neoadjuvant therapy in surgical care for individual patients with adenocarcinoma of the esophagus and esophagogastric junction-information not available from randomized trials-a machine-learning analysis was performed using worldwide real-world data on patients undergoing different therapies for this rare adenocarcinoma | ||
| 520 | |a METHODS: Using random forest technology in a sequential analysis, we (1) identified eligibility for each of four therapies among 13,365 patients: esophagectomy alone (n = 6649), neoadjuvant therapy (n = 4706), esophagectomy and adjuvant therapy (n = 998), and neoadjuvant and adjuvant therapy (n = 1022); (2) performed survival analyses incorporating interactions of patient and cancer characteristics with therapy; (3) determined optimal therapy as that predicted to maximize lifetime within 10 years (restricted mean survival time; RMST) for each patient; and (4) compared lifetime gained from optimal versus actual therapies | ||
| 520 | |a RESULTS: Actual therapy was optimal in 61% of those receiving esophagectomy alone; neoadjuvant therapy was optimal for 36% receiving neoadjuvant therapy. Many patients were predicted to benefit from postoperative adjuvant therapy. Total RMST for actual therapy received was 58,825 years. Had patients received optimal therapy, total RMST was predicted to be 62,982 years, a 7% gain | ||
| 520 | |a CONCLUSIONS: Average treatment effect for adenocarcinoma of the esophagus yields only crude evidence-based therapy guidelines. However, patient response to therapy is widely variable, and survival after data-driven predicted optimal therapy often differs from actual therapy received. Therapy must address an individual patient's cancer and clinical characteristics to provide precision surgical therapy for adenocarcinoma of the esophagus and esophagogastric junction | ||
| 650 | 4 | |a Journal Article | |
| 650 | 4 | |a Research Support, N.I.H., Extramural | |
| 650 | 4 | |a Research Support, Non-U.S. Gov't | |
| 650 | 4 | |a Artificial intelligence | |
| 650 | 4 | |a Machine learning | |
| 650 | 4 | |a Real-world data | |
| 650 | 4 | |a Survival analysis | |
| 700 | 1 | |a Lu, Min |e verfasserin |4 aut | |
| 700 | 1 | |a Ishwaran, Hemant |e verfasserin |4 aut | |
| 700 | 1 | |a Blackstone, Eugene H |e verfasserin |4 aut | |
| 700 | 0 | |a Worldwide Esophageal Cancer Collaboration Investigators |e verfasserin |4 aut | |
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