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DTSTAMP:20260421T090513Z
LOCATION:Plenary Room (Bldg. 6 - 001)
DTSTART;TZID=Europe/Stockholm:20260629T193000
DTEND;TZID=Europe/Stockholm:20260629T193100
UID:submissions.pasc-conference.org_PASC26_sess124_pos146@linklings.com
SUMMARY:An Ensemble Machine Learning Model to Predict 2- and 10-Year Breas
 t Cancer Recurrence Using Routine Hematological and Clinical Data
DESCRIPTION:Patricia Moreira (Inteli - Institute of Technology and Leaders
 hip)\n\nAccurate prediction of breast cancer recurrence remains difficult 
 because prognosis varies across molecular subtypes, and genomic tests are 
 often expensive or unavailable, leading to broad risk categories that may 
 cause overtreatment or undertreatment. We developed machine learning model
 s integrating routine hematological indices with clinicopathologic data to
  predict 2- and 10-year recurrence or death. We retrospectively analyzed 4
 ,277 women with primary breast cancer (2008–2022) from a single institutio
 n. The cohort included hormone receptor-positive (HR+; 60%), HER2-positive
  (21%), and triple-negative (TNBC; 18%) subtypes. We trained multiple clas
 sifiers and integrated them into a stacked ensemble using logistic regress
 ion as the final learner. Class imbalance was addressed with SMOTE applied
  only to training sets. The ensemble achieved strong discrimination: gener
 al cohort AUC 0.859 (2-year) and 0.814 (10-year), with specificity 88–86% 
 and sensitivity 67–59%. Subtype-specific performance remained robust: HR+ 
 AUC 0.862/0.804, HER2+ AUC 0.892/0.831, and TNBC AUC 0.834/0.829 (2-year/1
 0-year). SHAP analysis identified advanced tumor stage, elevated inflammat
 ory ratios (NLR, PLR, MLR), elevated red cell distribution width, and olde
 r age as key adverse predictors with stronger effects on early recurrence.
  This interpretable tool uses only routine blood tests, requiring no addit
 ional infrastructure, enabling scalable risk stratification where genomic 
 testing is unavailable.\n\n
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