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Prediction of local failure after stereotactic radiotherapy in melanoma brain metastases using ensemble learning on clinical, dosimetric, and radiomic data.

Code ↔ Paper

2 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 2 matches
  1. [1] § Materials & methods › Feature elimination and model-building ↔ RunModelTrainingWithCV.R, lines 51 to 97 · score 0.75 · nested cross validation, inner loop, selected models, best models, outer, split · Report a mismatch
  2. [2] § Materials & methods › Statistical analysis ↔ RunModelTrainingWithCV.R, lines 51 to 97 · score 0.50 · nested cross validation, inner loop, outer, trained, model · Report a mismatch

Paper

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The authors' code

R · 174 lines · 6.5 KB · MIT · 2 matches

  1. #! /usr/bin/Rscript --vanilla
  3. library(MuMIn)
  4. library(survival)
  5. library("survminer")
  7. set.seed(7)
  9. source("DataSplitter.R")
  10. source("Outcome.R")
  11. source("MelanomeCSVParser.R")
  12. source("FeatureReduction.R")
  13. source("Model.R")
  14. source("PredefinedFeatureReductionRuleSequences.R")
  15. source("MelanomeSettings.R")
  16. source("ModelTrainer.R")
  17. source("ModelSelectionInnerLoop.R")
  18. source("TrackVariables.R")
  20. args <- commandArgs(trailingOnly = TRUE)
  22. if (length(args) > 2) {
  23. path <- args[1]
  26. featuresToConsider <- NULL
  27. featuresToConsiderArg <- args[2]
  28. availableFeatures = c("radiomics", "clinic", "dose", "combined")
  29. if (featuresToConsiderArg %in% availableFeatures) {
  30. featuresToConsider <- featuresToConsiderArg
  31. }
  32. if (is.null(featuresToConsider)) {
  33. stop("Argment for features to use is not correct", call. = FALSE)
  34. }
  36. doNestedCV <- NULL
  37. doNestedCVArg = args[3]
  38. if (doNestedCVArg == "true") {
  39. doNestedCV <- TRUE
  40. } else if (doNestedCVArg == "false") {
  41. doNestedCV <- FALSE
  42. }
  43. if (is.null(doNestedCV)) {
  44. stop("Argment for nested CV is not correct", call. = FALSE)
  45. }
  47. } else {
  48. stop("Wrong number of arguments.n", call. = FALSE)
  49. }
  51. melanomaNestedCrossValidation = function(settings) {
  53. dataList <- settings$csvParser$readCSVFiles()
  55. featureSets <- list()
  56. for (featureSetName in settings$featureSets) {
  57. parser <- settings$featureSetParserMapping[[featureSetName]]
  58. featureSets[[featureSetName]] <- parser(dataList)
  59. }
  61. outcome <- settings$csvParser$getOutcome(dataList[[1]])
  64. dataSplitter <- settings$getDataSplitter()
  66. variableTracker = TrackVariables$new(path)
  67. for (outerFoldNumber in seq_len(settings$nuOfOuterFolds)) {
  69. trainTestSetOutcome <- dataSplitter$sampleDataset(outcome)
  71. trainTestSetsSplits <- list()
  72. for (featureSetName in settings$featureSets) {
  73. trainTestSetsSplits[[featureSetName]] <- dataSplitter$getSubsetWithCurrentIdx(featureSets[[featureSetName]])
  74. }
  76. trainSets <- lapply(trainTestSetsSplits, `[[`, "trainSet")
  77. testSets <- lapply(trainTestSetsSplits, `[[`, "testSet")
  79. modelsPreprocessorsDatasets <- modelSelectionInnerLoop(trainTestSetOutcome$trainSet, settings, trainSets)
  80. selectedModel <- evaluateAllModelsOnAllValidationSets(modelsPreprocessorsDatasets$models, modelsPreprocessorsDatasets$featureReductioContainers, modelsPreprocessorsDatasets$testTrainSets)
  81. if (!is.null(selectedModel$model)) {
  82. trainModelOnNewData(selectedModel$model, selectedModel$preprocessor, trainTestSetOutcome$trainSet, trainSets)
  83. testAccuracy <- evaluateModelOnNewData(selectedModel$model, selectedModel$preprocessor, trainTestSetOutcome$testSet, testSets)
  86. variableTracker$trackVariable(testAccuracy)
  87. variableTracker$trackVariableWithName(selectedModel$model, "bestModels")
  88. variableTracker$trackVariableWithName(selectedModel$preprocessor, "preprocessors")
  89. variableTracker$trackVariableWithName(list(dataSplitter$getTestIdxs()), "testIdxs")
  90. variableTracker$trackVariableWithName(list(dataSplitter$getTrainIdxs()), "trainIdxs")
  91. variableTracker$trackVariableWithName(modelsPreprocessorsDatasets$testTrainSets, "trainTestSetsInnerLoop")
  93. variableTracker$saveTrackedVariablesToRds()
  94. }
  96. }
  97. }
  99. melanomaCrossValidation = function(settings) {
  101. dataList <- settings$csvParser$readCSVFiles()
  103. featureSets <- list()
  104. for (featureSetName in settings$featureSets) {
  105. parser <- settings$featureSetParserMapping[[featureSetName]]
  106. featureSets[[featureSetName]] <- parser(dataList)
  107. }
  109. outcome <- settings$csvParser$getOutcome(dataList[[1]])
  111. modelsPreprocessorsDatasets <- modelSelectionInnerLoop(outcome, settings, featureSets)
  113. selectedModel <- evaluateAllModelsOnAllValidationSets(modelsPreprocessorsDatasets$models, modelsPreprocessorsDatasets$featureReductioContainers, modelsPreprocessorsDatasets$testTrainSets)
  114. trainModelOnNewData(selectedModel$model, selectedModel$preprocessor, outcome, featureSets)
  115. testAccuracy <- evaluateModelOnNewData(selectedModel$model, selectedModel$preprocessor, outcome, featureSets)
  116. variableTracker = TrackVariables$new(path)
  117. variableTracker$trackVariable(testAccuracy)
  118. variableTracker$trackVariableWithName(selectedModel$model, "bestModels")
  119. variableTracker$trackVariableWithName(selectedModel$preprocessor, "preprocessors")
  120. variableTracker$trackVariableWithName(modelsPreprocessorsDatasets$testTrainSets, "trainTestSetsInnerLoop")
  122. variableTracker$saveTrackedVariablesToRds()
  124. }
  126. melanomaTestOnExternalData = function(settings) {
  128. dataList <- settings$csvParser$readCSVFiles()
  130. featureSets <- list()
  131. for (featureSetName in settings$featureSets) {
  132. parser <- settings$featureSetParserMapping[[featureSetName]]
  133. featureSets[[featureSetName]] <- parser(dataList)
  134. }
  136. outcome <- settings$csvParser$getOutcome(dataList[[1]])
  139. dataSplitter <- settings$getDataSplitter()
  141. modelsPreprocessorsDatasets <- modelSelectionInnerLoop(outcome, settings, featureSets)
  142. selectedModel <- evaluateAllModelsOnAllValidationSets(modelsPreprocessorsDatasets$models, modelsPreprocessorsDatasets$featureReductioContainers, modelsPreprocessorsDatasets$testTrainSets)
  143. trainModelOnNewData(selectedModel$model, selectedModel$preprocessor, modelsPreprocessorsDatasets$testTrainSets[[1]]$outcomeTrain, modelsPreprocessorsDatasets$testTrainSets[[1]]$featuresTrain)
  144. testAccuracy <- evaluateModelOnNewData(selectedModel$model, selectedModel$preprocessor, modelsPreprocessorsDatasets$testTrainSets[[1]]$outcomeTest, modelsPreprocessorsDatasets$testTrainSets[[1]]$featuresTest)
  146. dataSplitter$sampleDataset(outcome)
  147. variableTracker = TrackVariables$new(path)
  148. variableTracker$trackVariable(testAccuracy)
  149. variableTracker$trackVariableWithName(selectedModel$model, "bestModels")
  150. variableTracker$trackVariableWithName(selectedModel$preprocessor, "preprocessors")
  151. variableTracker$trackVariableWithName(list(dataSplitter$getTestIdxs()), "testIdxs")
  152. variableTracker$trackVariableWithName(list(dataSplitter$getTrainIdxs()), "trainIdxs")
  154. variableTracker$saveTrackedVariablesToRds()
  156. }
  158. csvParser <- MelanomeCSVParser$new()
  159. if (featuresToConsider == "radiomics") {
  160. settings <- MelanomeSettingsRadiomics$new(csvParser)
  161. } else if (featuresToConsider == "clinic") {
  162. settings <- MelanomeSettingsClinical$new(csvParser)
  163. } else if (featuresToConsider == "dose") {
  164. settings <- MelanomeSettingsDose$new(csvParser)
  165. } else {
  166. settings <- MelanomeSettings$new(csvParser)
  167. }
  169. if (doNestedCV) {
  170. melanomaNestedCrossValidation(settings)
  171. } else {
  172. melanomaCrossValidation(settings)
  173. }

RunModelTrainingWithCV.R at commit ff63ec1, under MIT · at the source

Overview

Authors: Nanna E. Wielenberg1, Ilias Sachpazidis2, Oliver Blanck3, Lucas Zander4, Jan C. Peeken4, Stephanie E. Combs4, Horst Urbach5, Maxim Zaitsev6, Dimos Baltas2, Ilinca Popp1, Anca-Ligia Grosu1, Tobias Fechter2
ORCID iDs: Oliver Blanck, Jan C. Peeken, Ilinca Popp, Tobias Fechter
  1. Department of Radiation Oncology, Medical Center University of Freiburg, Medical Faculty University of Freiburg,Freiburg im Breisgau, Germany
  2. Division of Medical Physics, Department of Radiation Oncology, Medical Center University of Freiburg, Medical Faculty University of Freiburg,Freiburg, Germany
  3. Department of Radiation Oncology, University Medical Center Schleswig-Holstein,Kiel, Germany
  4. Department of Radiation Oncology, TUM School of Medicine and Health, TUM University Hospital Klinikum Rechts der Isar, Technical University Munich,Munich, Germany
  5. Department of Neuroradiology, Medical Center University of Freiburg, Medical Faculty University of Freiburg,Freiburg, Germany
  6. Division of Medical Physics, Department of Diagnostic and Interventional Radiology, Medical Center University of Freiburg, Medical Faculty University of Freiburg,Freiburg, Germany
Institutions: University of Freiburg (Germany); University Hospital Schleswig-Holstein (Germany); Technical University of Munich (Germany)
Journal: Radiation oncology (London, England), volume 21, issue 1, article 50
Dates: received 23 August 2025; accepted 7 March 2026; published online 21 March 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1186/s13014-026-02825-w · PMID 41864968 · PMCID PMC13063852 · OpenAlex W7139981597
Open access: gold, a free copy (OpenAlex)
Status: code verified (how the code is found and verified)
Categories: structural MRI / diffusion (modality), human (organism), other condition (population), clinical / translational (subfield)
Methods: Connectivity, Statistics, Machine learning, Preprocessing
Keywords: Malignant melanoma, Brain metastasis, Stereotactic radiotherapy, Radiomics, Ensemble learning
MeSH: Brain Neoplasms*, Melanoma*, Radiosurgery*, Adult, Aged, Aged, 80 and over, Ensemble Learning, Female, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Prognosis, Radiomics, Radiotherapy Dosage, Radiotherapy Planning, Computer-Assisted, Retrospective Studies, Treatment Failure (* major topic)
Topic: Brain Metastases and Treatment (Oncology, Medicine), according to OpenAlex
Funding: Universitätsklinikum Freiburg (8975)
Citations: not cited yet (Europe PMC); 43 references in the paper

Abstract

Background: This study aimed to predict lesion-specific outcomes after stereotactic radiotherapy (SRT) in patients with brain metastases from malignant melanoma (MBM), using clinical, dosimetric, and pretherapeutic MRI data.

Methods: In this multicenter retrospective study, 517 MBM from 130 patients treated with single-fraction or hypofractionated SRT at three centers were analyzed. From contrast-enhanced T1-weighted MRI, 1576 radiomic features (RF) were extracted per lesion − 788 from the gross tumor volume (GTV) and 788 from a 3 mm peritumoral margin. Clinical, dosimetric and RF data from one center were used for feature selection and model development via nested cross-validation employing an ensemble learning approach; external validation used data from the other two centers.

Results: Local failure occurred in 72/517 lesions (13.9%). Predictive models based on clinical data, RF, or a combination of both achieved c-indices of 0.60 ± 0.15, 0.65 ± 0.11, and 0.65 ± 0.12, respectively. RF-based models outperformed the clinical models; dosimetric data alone were not predictive. Most predictive RF originated from the peritumoral margin (92%) versus GTV (76%). On the first external dataset, all models performed similarly (c-index: 0.60–0.63), but generalization was poor on the second (c-index < 0.50), likely due to differences in patient characteristics and imaging protocols.

Conclusions: Pretherapeutic MRI features, particularly from the peritumoral region, show promise for predicting lesion-specific outcomes in MBM after SRT. Their consistent contribution suggests biologically relevant information that may support individualized treatment planning. Combined with clinical data, these markers offer prognostic insight, though generalizability remains limited by data heterogeneity.

Clinical trial number: Not applicable.

Supplementary Information: The online version contains supplementary material available at 10.1186/s13014-026-02825-w.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

ToFec/RadiomicsMM

License: MIT
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Commit: ff63ec10c08d8df457e264fb9774c771cf1b329d, 9 April 2026
Languages: R (13), Python (1)
Size: 69 files, 14 scripts
Software Heritage: not archived
Found in: “Data availability”
Holds: README, license file
Not found: CITATION.cff, environment file, tests, continuous integration, documentation
Tools: survival (4 files), caret (1 file), NumPy (1 file), reticulate (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers
  • 30 September 2026: the link answers
16 files
At the source: github.com/ToFec/RadiomicsMM

The paper's code and data availability statement is in the Data section.

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 14 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability

The developed code, RF, and models are publicly available: (https://github.com/ToFec/RadiomicsMM).

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 12 authors, 5 keywords, 18 MeSH terms, 1 funder, 43 references.

Cite

This paper

Wielenberg, N. E., Sachpazidis, I., Blanck, O., Zander, L., Peeken, J. C., Combs, S. E., Urbach, H., Zaitsev, M., Baltas, D., Popp, I., Grosu, A.-L., & Fechter, T. (2026). Prediction of local failure after stereotactic radiotherapy in melanoma brain metastases using ensemble learning on clinical, dosimetric, and radiomic data. Radiation oncology (London, England), 21(1), 50. https://doi.org/10.1186/s13014-026-02825-w

BibTeX

@article{wielenberg2026prediction,
author = {Wielenberg, Nanna E. and Sachpazidis, Ilias and Blanck, Oliver and Zander, Lucas and Peeken, Jan C. and Combs, Stephanie E. and Urbach, Horst and Zaitsev, Maxim and Baltas, Dimos and Popp, Ilinca and Grosu, Anca-Ligia and Fechter, Tobias},
title = {{Prediction of local failure after stereotactic radiotherapy in melanoma brain metastases using ensemble learning on clinical, dosimetric, and radiomic data}},
journal = {Radiation oncology (London, England)},
year = {2026},
month = mar,
volume = {21},
number = {1},
pages = {50},
publisher = {BMC},
issn = {1748-717X},
doi = {10.1186/s13014-026-02825-w},
url = {https://doi.org/10.1186/s13014-026-02825-w},
pmid = {41864968},
pmcid = {PMC13063852}
}

RIS

TY - JOUR
AU - Wielenberg, Nanna E.
AU - Sachpazidis, Ilias
AU - Blanck, Oliver
AU - Zander, Lucas
AU - Peeken, Jan C.
AU - Combs, Stephanie E.
AU - Urbach, Horst
AU - Zaitsev, Maxim
AU - Baltas, Dimos
AU - Popp, Ilinca
AU - Grosu, Anca-Ligia
AU - Fechter, Tobias
TI - Prediction of local failure after stereotactic radiotherapy in melanoma brain metastases using ensemble learning on clinical, dosimetric, and radiomic data
T2 - Radiation oncology (London, England)
J2 - Radiat Oncol
PY - 2026
DA - 2026/03/21
VL - 21
IS - 1
SP - 50
SN - 1748-717X
PB - BMC
DO - 10.1186/s13014-026-02825-w
UR - https://doi.org/10.1186/s13014-026-02825-w
LA - en
ER -

CSL-JSON

{
"id": "10.1186/s13014-026-02825-w",
"type": "article-journal",
"title": "Prediction of local failure after stereotactic radiotherapy in melanoma brain metastases using ensemble learning on clinical, dosimetric, and radiomic data",
"container-title": "Radiation oncology (London, England)",
"author": [
{
"family": "Wielenberg",
"given": "Nanna E."
},
{
"family": "Sachpazidis",
"given": "Ilias"
},
{
"family": "Blanck",
"given": "Oliver"
},
{
"family": "Zander",
"given": "Lucas"
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{
"family": "Peeken",
"given": "Jan C."
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{
"family": "Combs",
"given": "Stephanie E."
},
{
"family": "Urbach",
"given": "Horst"
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{
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"given": "Maxim"
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],
"container-title-short": "Radiat Oncol",
"volume": "21",
"issue": "1",
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"DOI": "10.1186/s13014-026-02825-w",
"PMID": "41864968",
"PMCID": "PMC13063852",
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"language": "en",
"issued": {
"date-parts": [
[
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21
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]
}
}

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