OSCR

Modality-level attribution and redundancy-aware radiomics for MRI-based differentiation of melanoma and NSCLC brain metastases.

Code ↔ Paper

11 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 11 matches
  1. [1] § Methodology › Radiomic features importance ranking ↔ rank_and_split__4.py, lines 99–190 · score 0.88 · class_weight, logistic regression, train CV, correlation filter, missingness, saga
  2. [2] § Methodology › Radiomic features importance ranking ↔ logreg__5a.py, lines 173–183 · score 0.76 · class_weight, logistic regression, correlation filter, saga, L1, solver
  3. [3] § Methodology › Explainability ↔ svc__5d.py, lines 554–634 · score 0.72 · KernelSHAP, model agnostic, bounded, Platt, explanations, SVC
  4. [4] § Methodology › Model development and hyperparameter optimization ↔ rank_and_split__4.py, lines 99–190 · score 0.70 · logistic regression, train CV, correlation threshold, penalty, stratified, fold
  5. [5] § Methodology › Model development and hyperparameter optimization ↔ randfor__5c.py, lines 221–310 · score 0.70 · train CV, correlation threshold, Optuna, Hyperparameter, depth, optimization
  6. [6] § Methodology › Model development and hyperparameter optimization ↔ collect_val_test_reports.py, lines 225–333 · score 0.68 · imbalance aware, PR AUC, precision, Youden, sensitivity, validation
  7. [7] § Results › Classification ↔ collect_val_test_reports.py, lines 225–333 · score 0.63 · imbalance aware metrics, PR AUC, sensitivity, probability, classification, threshold
  8. [8] § Methodology › Explainability ↔ lightGBM__5b.py, lines 656–701 · score 0.57 · Pareto style, cumulative importance
  9. [9] § Methodology › Explainability ↔ randfor__5c.py, lines 670–723 · score 0.57 · Pareto style, cumulative importance
  10. [10] § Methodology › Radiomics extraction ↔ all_lesions__1/pipeline_extract_catalogue__1.py, lines 63–94 · score 0.57 · bin width, Radiomic feature extraction, spline, resampled, mask
  11. [11] § Methodology › Radiomics extraction ↔ pipeline_extract_catalogue__1.py, lines 153–273 · score 0.56 · PyRadiomics, Bias, isotropic, N4, resampled, bin

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

Python · 248 lines · 10 KB · BSD-3-Clause · 2 matches

  1. #!/usr/bin/env python3
  2. import os
  3. os.environ["OMP_NUM_THREADS"] = "1"
  4. os.environ["MKL_NUM_THREADS"] = "1"
  5. os.environ["OPENBLAS_NUM_THREADS"] = "1"
  7. import numpy as np
  8. import pandas as pd
  9. from pathlib import Path
  10. import argparse
  11. import json
  13. from sklearn.base import BaseEstimator, TransformerMixin
  14. from sklearn.model_selection import StratifiedKFold
  15. from sklearn.pipeline import Pipeline
  16. from sklearn.preprocessing import StandardScaler, MinMaxScaler
  17. from sklearn.impute import SimpleImputer
  18. from sklearn.linear_model import LogisticRegression
  19. from sklearn.feature_selection import VarianceThreshold, SelectPercentile, f_classif, chi2, mutual_info_classif
  20. from sklearn.exceptions import NotFittedError
  21. from pandas.api.types import is_numeric_dtype
  23. # ============================== CONFIG ==============================
  25. parser = argparse.ArgumentParser()
  26. parser.add_argument("--topk", type=int, required=True, help="Top-k features to use")
  27. parser.add_argument("--config", type=str, required=True, help="Path to config JSON")
  28. args = parser.parse_args()
  30. TOPK_FEATURES = args.topk
  32. with open(args.config, "r") as f:
  33. cfg = json.load(f)["code4"]
  35. INPUT_XLSX = cfg["input_xlsx"]
  36. OUTPUT_DIR = f"./top_{TOPK_FEATURES}_features"
  37. os.makedirs(OUTPUT_DIR, exist_ok=True)
  39. OUTPUT_TRAIN_RAW = OUTPUT_DIR + "/traincv_set__4.xlsx"
  40. OUTPUT_TEST_RAW = OUTPUT_DIR + "/test_set__4.xlsx"
  41. OUTPUT_IMPORTANCE = OUTPUT_DIR + "/importance_traincv__4.csv"
  42. OUTPUT_FEATURELIST = OUTPUT_DIR + "/feature_list__4.csv"
  43. OUTPUT_TRAIN_TOPK = OUTPUT_DIR + f"/traincv_set__4_top{TOPK_FEATURES}.xlsx"
  44. OUTPUT_TEST_TOPK = OUTPUT_DIR + f"/test_set__4_top{TOPK_FEATURES}.xlsx"
  45. OUTPUT_FAMILY_SUM = OUTPUT_DIR + f"/radiomics_family_summary_top{TOPK_FEATURES}__4.csv"
  47. RANDOM_SEED = cfg.get("random_seed", 42)
  48. N_TEST_MINORITY = cfg["test_patients"]["minority"]
  49. N_TEST_MAJORITY = cfg["test_patients"]["majority"]
  50. N_SPLITS = cfg["n_splits"]
  52. TARGET_COL = cfg["grouping"]["target_col"]
  53. CLASSES = list(cfg["grouping"]["classes"].values())
  55. PRIMARY_KEY_1 = "subject_id"
  56. PRIMARY_KEY_2 = "roi"
  58. FS_CFG = cfg.get("feature_selection", {})
  60. # ===================== helpers: split =====================
  61. def choose_patients_by_class(df: pd.DataFrame, target_col: str, n_min: int, n_maj: int, rng: np.random.Generator):
  62. subjects = df[[PRIMARY_KEY_1, target_col]].drop_duplicates()
  63. grouped = subjects.groupby(target_col)[PRIMARY_KEY_1].unique().to_dict()
  64. min_class, maj_class = CLASSES[0], CLASSES[1]
  65. min_subj = grouped.get(min_class, [])
  66. maj_subj = grouped.get(maj_class, [])
  67. min_sample = rng.choice(min_subj, size=min(n_min, len(min_subj)), replace=False)
  68. maj_sample = rng.choice(maj_subj, size=min(n_maj, len(maj_subj)), replace=False)
  69. return np.concatenate([min_sample, maj_sample])
  71. # ===================== Corr filter =====================
  72. class CorrelationFilter(BaseEstimator, TransformerMixin):
  73. def __init__(self, threshold=0.95, method="spearman"):
  74. self.threshold = threshold
  75. self.method = method
  76. self.keep_features_ = None
  78. def fit(self, X, y=None):
  79. Xdf = pd.DataFrame(X).copy()
  80. med = pd.Series(np.nanmedian(Xdf.values, axis=0), index=Xdf.columns)
  81. Xdf = Xdf.fillna(med)
  82. valid = Xdf.notna().sum(axis=0) >= 2
  83. Xdf = Xdf.loc[:, valid]
  84. corr = Xdf.corr(method=self.method).abs()
  85. upper = corr.where(np.triu(np.ones(corr.shape), k=1).astype(bool))
  86. drop = set()
  87. for col in upper.columns:
  88. if col in drop:
  89. continue
  90. high = upper.index[upper[col] >= self.threshold].tolist()
  91. drop.update(high)
  92. self.keep_features_ = [c for c in Xdf.columns if c not in drop]
  93. return self
  95. def transform(self, X):
  96. Xdf = pd.DataFrame(X)
  97. return Xdf[self.keep_features_].values
  99. # ===================== importance on train-cv =====================
  100. def compute_importance_traincv(df_traincv: pd.DataFrame):
  101. df = df_traincv.copy()
  102. df.columns = df.columns.map(lambda c: str(c).strip())
  103. y = df[TARGET_COL].map({CLASSES[0]: 0, CLASSES[1]: 1}).values
  105. feat_cols = [c for c in df.columns if c not in {PRIMARY_KEY_1, PRIMARY_KEY_2, TARGET_COL} and is_numeric_dtype(df[c])]
  106. print(f"Numeric features considered: {len(feat_cols)}")
  107. original_count = len(feat_cols)
  109. X = df[feat_cols].copy()
  110. X.columns = X.columns.astype(str)
  112. # Variance Threshold
  113. if FS_CFG.get("variance_threshold", {}).get("enabled", True):
  114. thresh = FS_CFG["variance_threshold"].get("threshold", 1e-8)
  115. vt = VarianceThreshold(threshold=thresh)
  116. X = X.loc[:, vt.fit(X).get_support()]
  117. print(f"[Variance Threshold] Features remaining: {X.shape[1]} ({100*X.shape[1]/original_count:.1f}%)")
  119. # Missingness Filter
  120. if FS_CFG.get("missingness_filter", {}).get("enabled", True):
  121. max_nan_frac = FS_CFG["missingness_filter"].get("max_nan_fraction", 0.2)
  122. keep_mask = X.isna().mean() <= max_nan_frac
  123. X = X.loc[:, keep_mask]
  124. print(f"[Missingness Filter] Features remaining: {X.shape[1]} ({100*X.shape[1]/original_count:.1f}%)")
  126. # Correlation Filter
  127. if FS_CFG.get("correlation_filter", {}).get("enabled", True):
  128. corr_thresh = FS_CFG["correlation_filter"].get("threshold", 0.95)
  129. corr = CorrelationFilter(threshold=corr_thresh)
  130. corr.fit(X)
  131. X = pd.DataFrame(X[corr.keep_features_], columns=corr.keep_features_)
  132. print(f"[Correlation Filter] Features remaining: {X.shape[1]} ({100*X.shape[1]/original_count:.1f}%)")
  134. # ANOVA (f_classif)
  135. if FS_CFG.get("anova_f_classif", {}).get("enabled", False):
  136. sel = SelectPercentile(f_classif, percentile=FS_CFG["anova_f_classif"].get("percentile", 50))
  137. sel.fit(X.fillna(0), y)
  138. X = X.loc[:, sel.get_support()]
  139. print(f"[ANOVA F-test] Features remaining: {X.shape[1]} ({100*X.shape[1]/original_count:.1f}%)")
  141. # Chi-squared
  142. if FS_CFG.get("chi2", {}).get("enabled", False):
  143. X_safe = X.fillna(0)
  144. if (X_safe >= 0).all().all():
  145. chi = SelectPercentile(chi2, percentile=FS_CFG["chi2"].get("percentile", 50))
  146. chi.fit(X_safe, y)
  147. X = X.loc[:, chi.get_support()]
  148. print(f"[Chi2 Test] Features remaining: {X.shape[1]} ({100*X.shape[1]/original_count:.1f}%)")
  149. else:
  150. print("[Chi2 Test] Skipped: negative values detected")
  152. # Mutual Information
  153. if FS_CFG.get("mutual_info", {}).get("enabled", False):
  154. mi = SelectPercentile(mutual_info_classif, percentile=FS_CFG["mutual_info"].get("percentile", 50))
  155. mi.fit(X.fillna(0), y)
  156. X = X.loc[:, mi.get_support()]
  157. print(f"[Mutual Info] Features remaining: {X.shape[1]} ({100*X.shape[1]/original_count:.1f}%)")
  159. feat_cols_final = X.columns.tolist()
  161. pipe = Pipeline([
  162. ("imp", SimpleImputer(strategy="median")),
  163. ("sc", StandardScaler()),
  164. ("clf", LogisticRegression(
  165. penalty="l1", solver="saga", C=1.0, max_iter=10000,
  166. class_weight="balanced", random_state=RANDOM_SEED, n_jobs=-1
  167. )),
  168. ])
  170. skf = StratifiedKFold(n_splits=N_SPLITS, shuffle=True, random_state=RANDOM_SEED)
  171. coef_list = []
  173. for fold_i, (tr, te) in enumerate(skf.split(X, y), start=1):
  174. Xtr, ytr = X.iloc[tr], y[tr]
  175. if len(np.unique(ytr)) < 2:
  176. print(f"[Fold {fold_i}] Skipped due to single-class train split")
  177. continue
  178. pipe.fit(Xtr, ytr)
  179. coefs = pd.Series(pipe.named_steps["clf"].coef_[0], index=X.columns)
  180. coef_list.append(coefs)
  182. if not coef_list:
  183. raise RuntimeError("All CV folds failed due to class imbalance.")
  185. coef_df = pd.concat(coef_list, axis=1)
  186. coef_df.columns = [f"fold{i+1}" for i in range(len(coef_list))]
  188. median_abs_coef = coef_df.abs().median(axis=1, skipna=True)
  189. imp = pd.DataFrame({"median_abs_coef": median_abs_coef}).sort_values("median_abs_coef", ascending=False)
  190. return imp, feat_cols
  192. # ===================== main =====================
  193. def main():
  194. rng = np.random.default_rng(RANDOM_SEED)
  195. df_all = pd.read_excel(INPUT_XLSX)
  196. df_all[PRIMARY_KEY_1] = df_all[PRIMARY_KEY_1].astype(str)
  197. df_all = df_all[df_all[TARGET_COL].isin(CLASSES)].copy()
  198. print(f"Total samples after filtering target classes: {df_all.shape[0]}")
  200. test_subjects = choose_patients_by_class(df_all, TARGET_COL, N_TEST_MINORITY, N_TEST_MAJORITY, rng)
  201. mask_test = df_all[PRIMARY_KEY_1].isin(test_subjects)
  202. df_test_raw = df_all.loc[mask_test].copy()
  203. df_train_raw = df_all.loc[~mask_test].copy()
  204. print(f"Train patients: {df_train_raw[PRIMARY_KEY_1].nunique()} | Test patients: {df_test_raw[PRIMARY_KEY_1].nunique()}")
  206. df_train_raw.to_excel(OUTPUT_TRAIN_RAW, index=False)
  207. df_test_raw.to_excel(OUTPUT_TEST_RAW, index=False)
  209. imp, feat_cols_all = compute_importance_traincv(df_train_raw)
  210. imp.to_csv(OUTPUT_IMPORTANCE, index=True)
  213. remaining_feats = [f for f in imp.index.tolist() if f in df_all.columns]
  215. if len(remaining_feats) < TOPK_FEATURES:
  216. print(f"Warning: Less than {TOPK_FEATURES} features remaining: Keeping only {len(remaining_feats)}.")
  217. TOPK_FINAL = len(remaining_feats)
  218. else:
  219. TOPK_FINAL = TOPK_FEATURES
  221. top_feats = remaining_feats[:TOPK_FINAL]
  222. pd.Series(top_feats, name="feature").to_csv(OUTPUT_FEATURELIST, index=False)
  224. cols_final = [PRIMARY_KEY_1, PRIMARY_KEY_2, TARGET_COL] + top_feats
  225. for c in cols_final:
  226. if c not in df_train_raw.columns:
  227. df_train_raw[c] = np.nan
  228. if c not in df_test_raw.columns:
  229. df_test_raw[c] = np.nan
  231. df_train_topk = df_train_raw.loc[:, cols_final]
  232. df_test_topk = df_test_raw.loc[:, cols_final]
  233. df_train_topk.to_excel(OUTPUT_TRAIN_TOPK, index=False)
  234. df_test_topk.to_excel(OUTPUT_TEST_TOPK, index=False)
  236. def family_of(name: str) -> str:
  237. return name.split("__", 1)[0] if isinstance(name, str) and "__" in name else "misc"
  239. print(f"Top-{TOPK_FINAL} features kept.")
  241. topk_for_family = imp.head(TOPK_FINAL).copy()
  242. families = topk_for_family.index.to_series().apply(family_of)
  243. family_summary = families.value_counts().rename_axis("family").reset_index(name=f"count_in_top{TOPK_FINAL}")
  244. family_summary.to_csv(OUTPUT_FAMILY_SUM, index=False)
  246. if __name__ == "__main__":
  247. main()

rank_and_split__4.py at commit 0e87668, under BSD-3-Clause · at the source

Overview

Authors: Rafail C Christodoulou1, Georgios Vamvouras2, Amal El Masri3, Platon S Papageorgiou4, Evros Vassiliou5, Elena E Solomou6, Sokratis G Papageorgiou7, Michalis F Georgiou8
  1. Division of Neuroimaging and Neurointervention, Department of Radiology, Stanford University, Stanford, CA, United States
  2. Department of Electrical and Computer Engineering, National Technical University of Athens (NTUA), Athens, Greece
  3. Faculty of Medicine, American University of Beirut, Beirut, Lebanon
  4. Department of Medicine, National and Kapodistrian University of Athens, Athens, Greece
  5. Department of Biological Sciences, Kean University, Union, NJ, United States
  6. Internal Medicine-Hematology, University of Patras Medical School, Rion, Greece
  7. 1st Department of Neurology, Medical School, National and Kapodistrian University of Athens, Eginition Hospital, Athens, Greece
  8. Department of Radiology, University of Miami, Miami, FL, United States
Institutions: Stanford Medicine (United States); Stanford University (United States); National Technical University of Athens (Greece); American University of Beirut (Lebanon); National and Kapodistrian University of Athens (Greece); Kean University (United States); University of Patras (Greece); Eginition Hospital (Greece); University of Miami (United States)
Journal: Frontiers in oncology, volume 16, article 1816015
Dates: received 23 February 2026; accepted 8 June 2026; published online 29 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3389/fonc.2026.1816015 · PMID 42444810 · PMCID PMC13357173 · OpenAlex W7166529421
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), other condition (population)
Methods: Connectivity, Statistics, Machine learning, Preprocessing, fMRI & imaging, Physiology & signal measures
Keywords: arteficial intelligence, explainable AI (SHAP), melanoma, NSCLC, personalized & precision medicine, radiomics
Topic: Radiomics and Machine Learning in Medical Imaging (Radiology, Nuclear Medicine and Imaging, Medicine), according to OpenAlex
Citations: not cited yet (Europe PMC); 23 references in the paper

Abstract

Background: Differentiating the primary source of brain metastases using imaging alone is difficult, especially when intracranial disease is the initial sign of cancer. This study aimed to develop and validate a redundancy-aware, explainable radiomics framework for distinguishing melanoma from non–small cell lung cancer (NSCLC) brain metastases through multiparametric MRI.

Methods: Lesion-level radiomic features were extracted from T1, contrast-enhanced T1 (T1CE), T2, and FLAIR sequences. Correlation-based redundancy filtering and L1-regularized feature ranking were applied to minimize collinearity among features. A structured parametric analysis across progressively larger top-K feature subsets was performed to evaluate performance stability and modality-specific contribution patterns. Multiple machine learning classifiers were tested using cross-validation and an independent test set, with model interpretability assessed via TreeSHAP to quantify feature- and modality-level contributions.

Results: The best-performing configuration achieved a test AUC of 0.75, while RF-200 was selected for the primary tree-based explainability analysis due to its stable AUC-based discrimination. SHAP analysis showed that classification depended on a small set of predictors combining texture and intensity distribution features. FLAIR and T2 features contributed most to model attribution, suggesting that fluid-sensitive sequences capture discrimination-relevant heterogeneity and microenvironmental signals beyond contrast enhancement.

Discussion: A redundancy-aware, interpretable radiomics approach shows promising preliminary discrimination between melanoma and NSCLC brain metastases and provides structured insight into modality-specific sequence contributions. External validation and prospective reader studies are required before clinical translation.

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 11 matches between paragraphs and lines of code.

georgeDV2002/metastasis_radiomics

License: BSD-3-Clause
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: 0e87668ae65c74b0fc6f9185e9a872f5b97ff989, 1 May 2026
Languages: Python (23)
Size: 30 files, 23 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (requirements.txt)
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: pandas (21 files), NumPy (20 files), Matplotlib (12 files), scikit-learn (9 files), SciPy (3 files), SHAP (3 files), NiBabel (2 files), PyRadiomics (2 files), SimpleITK (2 files), LightGBM (1 file), seaborn (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
25 files
At the source: github.com/georgeDV2002/metastasis_radiomics

Code availability

The complete implementation of the preprocessing, radiomics extraction, feature selection, model training, calibration, and explainability pipeline is publicly available at: https://github.com/georgeDV2002/metastasis_radiomics.

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

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;
  • 23 scripts, each with its path and the digest of its content;
  • 11 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 statement

Publicly available datasets were analyzed in this study. This data can be found here: https://www.cancerimagingarchive.net/collection/pretreat-metstobrain-masks/.

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, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 8 authors, 6 keywords, 23 references.

Cite

This paper

Christodoulou, R. C., Vamvouras, G., El Masri, A., Papageorgiou, P. S., Vassiliou, E., Solomou, E. E., Papageorgiou, S. G., & Georgiou, M. F. (2026). Modality-level attribution and redundancy-aware radiomics for MRI-based differentiation of melanoma and NSCLC brain metastases. Frontiers in oncology, 16, 1816015. https://doi.org/10.3389/fonc.2026.1816015

BibTeX

@article{christodoulou2026modality,
author = {Christodoulou, Rafail C and Vamvouras, Georgios and El Masri, Amal and Papageorgiou, Platon S and Vassiliou, Evros and Solomou, Elena E and Papageorgiou, Sokratis G and Georgiou, Michalis F},
title = {{Modality-level attribution and redundancy-aware radiomics for MRI-based differentiation of melanoma and NSCLC brain metastases}},
journal = {Frontiers in oncology},
year = {2026},
month = jun,
volume = {16},
pages = {1816015},
publisher = {Frontiers Media SA},
issn = {2234-943X},
doi = {10.3389/fonc.2026.1816015},
url = {https://doi.org/10.3389/fonc.2026.1816015},
pmid = {42444810},
pmcid = {PMC13357173}
}

RIS

TY - JOUR
AU - Christodoulou, Rafail C
AU - Vamvouras, Georgios
AU - El Masri, Amal
AU - Papageorgiou, Platon S
AU - Vassiliou, Evros
AU - Solomou, Elena E
AU - Papageorgiou, Sokratis G
AU - Georgiou, Michalis F
TI - Modality-level attribution and redundancy-aware radiomics for MRI-based differentiation of melanoma and NSCLC brain metastases
T2 - Frontiers in oncology
J2 - Front Oncol
PY - 2026
DA - 2026/06/29
VL - 16
SP - 1816015
SN - 2234-943X
PB - Frontiers Media SA
DO - 10.3389/fonc.2026.1816015
UR - https://doi.org/10.3389/fonc.2026.1816015
LA - en
ER -

CSL-JSON

{
"id": "10.3389/fonc.2026.1816015",
"type": "article-journal",
"title": "Modality-level attribution and redundancy-aware radiomics for MRI-based differentiation of melanoma and NSCLC brain metastases",
"container-title": "Frontiers in oncology",
"author": [
{
"family": "Christodoulou",
"given": "Rafail C"
},
{
"family": "Vamvouras",
"given": "Georgios"
},
{
"family": "El Masri",
"given": "Amal"
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{
"family": "Papageorgiou",
"given": "Platon S"
},
{
"family": "Vassiliou",
"given": "Evros"
},
{
"family": "Solomou",
"given": "Elena E"
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{
"family": "Papageorgiou",
"given": "Sokratis G"
},
{
"family": "Georgiou",
"given": "Michalis F"
}
],
"container-title-short": "Front Oncol",
"volume": "16",
"page": "1816015",
"DOI": "10.3389/fonc.2026.1816015",
"PMID": "42444810",
"PMCID": "PMC13357173",
"ISSN": "2234-943X",
"publisher": "Frontiers Media SA",
"URL": "https://doi.org/10.3389/fonc.2026.1816015",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
29
]
]
}
}

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Journal: Journal of clinical medicine
In common: SimpleITK, NiBabel, seaborn, 5 other tools, structural MRI / diffusion, other condition, 2 references
[3] doi:10.1371/journal.pcbi.1014555 [code]
Body surface potential driven personalisation of electrophysiological digital twins in hypertrophic cardiomyopathy.
Journal: PLoS computational biology
In common: PyRadiomics, SimpleITK, NiBabel, 6 other tools, structural MRI / diffusion
[4] doi:10.1186/s13244-026-02296-3 [code]
A pre-trained foundation model framework for multiplanar MRI classification of extramural vascular invasion and mesorectal fascia invasion in rectal cancer.
Journal: Insights into imaging
In common: SimpleITK, NiBabel, scikit-learn, 4 other tools, structural MRI / diffusion, other condition, 2 references
[5] doi:10.1371/journal.pone.0337726 [code]
Classification of chronic pain and spinal cord stimulation response using machine learning in magnetoencephalography data
Journal: n/a
In common: PyRadiomics, LightGBM, SimpleITK, 5 other tools
[6] doi:10.1038/s41598-026-56688-y [code]
On the value of radiomics in addition to clinical measures in emotional conflict fMRI for predicting sertraline response in major depressive disorder.
Journal: Scientific reports
In common: PyRadiomics, SHAP, NiBabel, 6 other tools
[7] doi:10.1212/wnl.0000000000218472 [code]
Lesion-Level Subtypes of White Matter Hyperintensity Evolution Beyond Spatial Location.
Journal: Neurology
In common: LightGBM, SHAP, NiBabel, 5 other tools, structural MRI / diffusion
[8] doi:10.21203/rs.3.rs-9914920/v1 [code]
Prediction of cognitive performance by demographics, sleep, and brain morphometry: machine learning findings from ENIGMA-Sleep Working Group
Journal: Research Square (preprint)
In common: SHAP, NiBabel, seaborn, 5 other tools, structural MRI / diffusion, 1 reference
[9] doi:10.3390/cancers18162636 [code]
Exploring the Impact of T2-Weighted MRI Fat Saturation on Radiomics Stability for Brain Radionecrosis Prediction After Skull-Base Proton Therapy: A Pilot Study.
Journal: Cancers
In common: PyRadiomics, SimpleITK, seaborn, 5 other tools, structural MRI / diffusion
[10] doi:10.1073/pnas.2516601123 [code]
Unveiling the glymphatic system's role in brain aging: A comprehensive biomarker and modifiable intervention target.
Journal: Proceedings of the National Academy of Sciences of the United States of America
In common: LightGBM, SHAP, seaborn, 5 other tools, structural MRI / diffusion

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