predict_pathofm: Apply supported models for one pathology foundation-model...

In PathoFMPred: Tumour Feature Models from Pathology Representations

Apply supported models for one pathology foundation-model representation

Description

Multiple rows with the same 'patient_id' are interpreted as multiple slides and mean-pooled before prediction, matching model development.

Usage

predict_pathofm(
  cancer,
  features,
  foundation_model = c("TITAN", "GigaSSL", "ProvGigaPath"),
  patient_id = NULL,
  outcome_type = c("continuous", "binary"),
  family = NULL,
  warn_ood = TRUE,
  include_limited_evidence = FALSE
)

Arguments

cancer	One TCGA cancer code per row, or one code recycled to all rows.
features	A matrix or data frame containing the correctly named features for 'foundation_model'.
foundation_model	One of 'TITAN', 'GigaSSL', or 'ProvGigaPath'.
patient_id	Optional identifier per row. Repeated identifiers are pooled.
outcome_type	One or more of 'continuous' and 'binary'.
family	Optional endpoint-family filter.
warn_ood	Warn when over 5 percent of dimensions fall outside training ranges.
include_limited_evidence	Explicitly opt in to limited-sample models that still crossed the representation-specific matched effect threshold. Models tested below the threshold never enter prediction or reporting.

Value

A long data frame with one row per patient and applicable model. For atlas collections, fields prefixed 'matched_tss_' describe the representation-specific sensitivity analysis that held complete TCGA tissue-source-site codes apart. 'repeated_auc' and 'repeated_pr_auc' are missing when five-repeat validation was not performed for that model; they are never filled with a primary-screen estimate. These internal performance fields do not validate an individual prediction.

PathoFMPred documentation built on Nov. 12, 2026, 5:10 p.m.