OncoPath

Oncology and pathology analyses for jamovi and R

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Overview

OncoPath is a jamovi module and R package for oncology and pathology research. It is one of the modules of the ClinicoPath project and adds four analyses under the OncoPath menu in jamovi: patient timelines, tumour burden response, diagnostic test accuracy meta-analysis, and regional heterogeneity of an immunohistochemistry (IHC) marker.

The results are for research and exploratory description. They are not a substitute for a protocol-specified trial analysis or for clinical judgement.

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Analyses (4)

Analysis (jamovi menu)	R function	Menu subgroup	What it does
Swimmer Plot	swimmerplot	Patient Follow-Up Plots	One lane per patient from start and end times (numbers or dates), coloured by response; optional censoring status, up to five milestones, event markers and a grouping variable. Reports ORR and DCR with exact CIs, median follow-up (reverse Kaplan-Meier when a censoring status is given) and person-time by best response.
Treatment Response: Patient-Level Burden	waterfall	Patient Follow-Up Plots	Waterfall and spider plots from one tumour burden value per patient (a percent change) or per visit (a raw measurement). Assigns CR/PR/SD/PD with thresholds adapted from RECIST v1.1, measures progression against the nadir, and reports ORR and DCR with exact CIs, group comparisons, time to response and duration of response.
Diagnostic Test Meta-Analysis for Pathology	diagnosticmeta	Meta-Analysis	Pools 2x2 tables (TP, FP, FN, TN) from independent studies: bivariate random-effects model (Reitsma), proportional-hazards SROC model (Holling), heterogeneity, meta-regression on one study-level covariate, Deeks’ funnel-plot test, and forest, SROC and funnel plots.
IHC Heterogeneity Analysis	ihcheterogeneity	IHC Analysis	For one continuous IHC measurement (for example percent positive cells or an H-score) taken in several regions of the same case: how well regions (biopsies, cores, fields) agree with a reference measurement or with one another. Spearman correlation, ICC(2,1) and ICC(3,1), per-case CV, Bland-Altman limits of agreement, equivalence-margin judgements, and optional variance components, sample-size planning and spatial-compartment comparisons.

What the analyses do not do

• Treatment Response is not a RECIST v1.1 implementation. It never sees individual lesions, so it cannot sum target lesions, detect a new lesion, judge non-target progression or confirm a response. Its categories and ORR/DCR are unconfirmed and exploratory.
• Swimmer Plot reads the response categories you supply. It shows ORR and DCR only when every response value is a RECIST category (CR, PR, SD, PD or NE).
• IHC Heterogeneity analyses one marker at a time and measures regional (sampling) variability within each case. It does not measure agreement between observers.
• Diagnostic Test Meta-Analysis expects one 2x2 table per independent study. Several readers, cut-offs or algorithms applied to the same cohort are not independent studies.

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Installation

In jamovi

OncoPath needs jamovi 2.7.27 or later.

• When OncoPath is listed in the jamovi library: Modules (top right) → jamovi library → search for OncoPath → Install.
• Until then, a module file (.jmo) can be side-loaded through Modules → jamovi library → Sideload, when one is attached to a GitHub release.

As an R package

# install.packages("remotes")
remotes::install_github("sbalci/OncoPath")

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Quick Start (R)

Each function takes a data frame and column names as strings.

library(OncoPath)

# 1. Swimmer plot: patient timelines with response
data("swimmerplot_sample", package = "OncoPath")
swim <- swimmerplot(
  data        = swimmerplot_sample,
  patientID   = "PatientID",
  startTime   = "StartTime",
  endTime     = "EndTime",
  responseVar = "Response"
)

# 2. Treatment response from percent change (one row per patient)
data("waterfall_percentage_basic", package = "OncoPath")
wf <- waterfall(
  data        = waterfall_percentage_basic,
  patientID   = "PatientID",
  responseVar = "Response",
  inputType   = "percentage",
  groupVar    = "Treatment"
)

# 3. Treatment response from raw measurements (one row per patient per visit)
data("waterfall_raw_longitudinal", package = "OncoPath")
wf_raw <- waterfall(
  data            = waterfall_raw_longitudinal,
  patientID       = "PatientID",
  responseVar     = "Measurement",
  timeVar         = "Time",
  inputType       = "raw",
  measurementUnit = "mm",
  showSpiderPlot  = TRUE
)

# 4. Diagnostic test accuracy meta-analysis
data("diagnosticmeta_test_categorical", package = "OncoPath")
dta <- diagnosticmeta(
  data            = diagnosticmeta_test_categorical,
  study           = "study",
  true_positives  = "true_positives",
  false_positives = "false_positives",
  false_negatives = "false_negatives",
  true_negatives  = "true_negatives",
  covariate       = "imaging_modality",
  heterogeneity_analysis = TRUE,
  meta_regression = TRUE
)

# 5. IHC heterogeneity: four biopsies against a whole-section reference
data("ihcheterogeneity_test", package = "OncoPath")
ihc <- ihcheterogeneity(
  data         = ihcheterogeneity_test,
  wholesection = "wholesection",
  biopsy1      = "biopsy1",
  biopsy2      = "biopsy2",
  biopsy3      = "biopsy3",
  biopsy4      = "biopsy4",
  spatial_id   = "spatial_id"
)

# Results are jmvcore objects: print one, or read a table as a data frame
wf$summaryTable$asDF

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Example datasets

All example datasets are synthetic. They were generated to demonstrate the analyses and do not come from patients.

In jamovi, open them from the main menu (☰) → Open → Data Library → OncoPath. Each file opens with the analysis already set up:

jamovi example	Analysis	Contents
swimmerplot_sample.omv	Swimmer Plot	20 patients: start and end times in months, best response, four milestone times and an event column
waterfall_percentage_basic.omv	Treatment Response	20 patients: best percent change from baseline and treatment arm (Drug A / Drug B)
waterfall_raw_longitudinal.omv	Treatment Response	15 patients: tumour measurements at months 0, 2, 4 and 6
diagnosticmeta_test_categorical.omv	Diagnostic Test Meta-Analysis	20 studies: 2x2 counts, year, a quality score and imaging modality (CT 5, MRI 9, ultrasound 6)
ihcheterogeneity_test.omv	IHC Heterogeneity	50 cases: whole-section reference, four biopsies and a spatial compartment

In R, the same data are available with data(), together with diagnostic_studies (five 2x2 tables) and histopathology (a general-purpose clinicopathological data frame from the ClinicoPath project, not used by an OncoPath analysis).

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Documentation and support

• Website: https://www.serdarbalci.com/OncoPath/
• ClinicoPath project: https://www.serdarbalci.com/ClinicoPathJamoviModule/
• Source code: https://github.com/sbalci/OncoPath/
• Bug reports and feature requests: https://github.com/sbalci/OncoPath/issues

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Citation

If you use OncoPath in your research or publications, please cite:

@manual{balci2026oncopath,
  title  = {OncoPath: Oncology and Pathology Analyses for jamovi},
  author = {Serdar Balci},
  year   = {2026},
  note   = {R package and jamovi module version 1.1.0},
  url    = {https://www.serdarbalci.com/OncoPath/}
}

OncoPath has no DOI of its own. The ClinicoPath project, from which it is distributed, is archived on Zenodo as 10.5281/zenodo.3997188.

License

OncoPath is licensed under GPL (>= 2); see the GNU licenses page. It imports packages under other licences, including mada (GPL-2 only), ggrepel (GPL-3), RColorBrewer (Apache-2.0) and ggswim (MIT). Users who redistribute OncoPath together with its dependencies should check that the combination meets their licensing needs.