title: Time-dependent estimates of recurrence and survival in colon cancer: clinical decision support system tool development for adjuvant therapy and oncological outcome assessment.
abstract: Unanswered questions remain in determining which high-risk node-negative colon cancer (CC) cohorts benefit from adjuvant therapy and how it may differ in an equal access population. Machine-learned Bayesian Belief Networks (ml-BBNs) accurately estimate outcomes in CC, providing clinicians with Clinical Decision Support System (CDSS) tools to facilitate treatment planning. We evaluated ml-BBNs ability to estimate survival and recurrence in CC. We performed a retrospective analysis of registry data of patients with CC to train-test-crossvalidate ml-BBNs using the Department of Defense Automated Central Tumor Registry (January 1993 to December 2004). Cases with events or follow-up that passed quality control were stratified into 1-, 2-, 3-, and 5-year survival cohorts. ml-BBNs were trained using machine-learning algorithms and k-fold crossvalidation and receiver operating characteristic curve analysis used for validation. BBNs were comprised of 5301 patients and areas under the curve ranged from 0.85 to 0.90. Positive predictive values for recurrence and mortality ranged from 78 to 84 per cent and negative predictive values from 74 to 90 per cent by survival cohort. In the 12-month model alone, 1,132,462,080 unique rule sets allow physicians to predict individual recurrence/mortality estimates. Patients with Stage II (N0M0) CC benefit from chemotherapy at different rates. At one year, all patients older than 73 years of age with T2-4 tumors and abnormal carcinoembryonic antigen levels benefited, whereas at five years, all had relative reduction in mortality with the largest benefit amongst elderly, highest T-stage patients. ml-BBN can readily predict which high-risk patients benefit from adjuvant therapy. CDSS tools yield individualized, clinically relevant estimates of outcomes to assist clinicians in treatment planning.
authors.affiliation: Department of Surgery, Uniformed Services University of the Health Sciences, Bethesda, Maryland, USA.
authors.forename: Scott R
authors.initials: SR
authors.lastname: Steele
authors.forename: Anton
authors.initials: A
authors.lastname: Bilchik
authors.forename: Eric K
authors.initials: EK
authors.lastname: Johnson
authors.forename: Aviram
authors.initials: A
authors.lastname: Nissan
authors.forename: George E
authors.initials: GE
authors.lastname: Peoples
authors.forename: John S
authors.initials: JS
authors.lastname: Eberhardt
authors.forename: Philip
authors.initials: P
authors.lastname: Kalina
authors.forename: Benjamin
authors.initials: B
authors.lastname: Petersen
authors.forename: Björn
authors.initials: B
authors.lastname: Brücher
authors.forename: Mladjan
authors.initials: M
authors.lastname: Protic
authors.forename: Itzhak
authors.initials: I
authors.lastname: Avital
authors.forename: Alexander
authors.initials: A
authors.lastname: Stojadinovic
mesh_terms: D000230:Adenocarcinoma / Q000401:mortality / Q000473:pathology / Q000628:therapy*; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000970:Antineoplastic Agents / Q000627:therapeutic use*; D001499:Bayes Theorem; D017024:Chemotherapy, Adjuvant; D003082:Colectomy; D003110:Colonic Neoplasms / Q000401:mortality / Q000473:pathology / Q000628:therapy*; D020000:Decision Support Systems, Clinical; D005260:Female; D005500:Follow-Up Studies; D006801:Humans; D008297:Male; D008875:Middle Aged; D015233:Models, Statistical; D009364:Neoplasm Recurrence, Local / Q000175:diagnosis*; D011237:Predictive Value of Tests; D012372:ROC Curve; D018714:Radiotherapy, Adjuvant; D012042:Registries; D012189:Retrospective Studies; D016019:Survival Analysis; D013997:Time Factors; D016896:Treatment Outcome
chemical_list: D000970:Antineoplastic Agents