OSCR

A pre-trained foundation model framework for multiplanar MRI classification of extramural vascular invasion and mesorectal fascia invasion in rectal cancer.

Code ↔ Paper

12 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 12 matches
  1. [1] § Methods › Evaluation › Evaluation metrics ↔ notebooks/compute_AUC_CI.ipynb, lines 513–584 · score 0.83 · confidence intervals, binary predictions, predicted probabilities, balanced accuracy, F1 score, CI
  2. [2] § Methods › Model development › Training strategy ↔ othercode/classification_model/FMmodel.py, lines 231–269 · score 0.76 · Cosine annealing, AdamW, decay, UMedPt, scheduling, optimized
  3. [3] § Methods › Data curation › Dataset ↔ notebooks/export_patient_information.ipynb, lines 87–153 · score 0.76 · La Fe, CHU Angers, Centre, Siemens, ULS, manufacturers
  4. [4] § Methods › Model development › Training strategy ↔ othercode/classification_model/segresnet192.py, lines 185–229 · score 0.74 · focal loss, Cosine annealing, SGD, decay, scheduling, batch
  5. [5] § Results › Classification results ↔ notebooks/draw_ROC.ipynb, lines 145–274 · score 0.74 · ROC curves, SeResnet, axial original, UMedPT_LR, MFI classification, AUC
  6. [6] § Results › Classification results ↔ notebooks/draw_ROC.ipynb, lines 145–274 · score 0.64 · axial_harmo, ROC curves, UMedPT_LR, AUC, sagittal, EVI
  7. [7] § Results › Patient characteristics ↔ notebooks/export_patient_information.ipynb, lines 87–153 · score 0.61 · CHU Angers, train_val, Siemens, ULS, manufacturers, patient
  8. [8] § Methods › Data curation › Image preprocessing and rectum localization ↔ notebooks/resample_totalmask3.ipynb, lines 83–172 · score 0.61 · NIfTI, colon mask, resampled, spacing, TotalSegmentator, rectum
  9. [9] § Results › Classification results ↔ notebooks/draw_ROC.ipynb, lines 1–139 · score 0.55 · UMedPT_LR, EVI classification, ROC, curves, AUC, score
  10. [10] § Methods › Data curation › Image preprocessing and rectum localization ↔ othercode/classification_model/centrecrop_correct2.ipynb, lines 26–111 · score 0.54 · center crop, voxel, intensity, spacing, TotalSegmentator, mask
  11. [11] § Methods › Model development › Training strategy ↔ othercode/classification_model/FMmodel.py, lines 289–345 · score 0.54 · Gaussian noise, affine, intensity, transforms, patch, Training
  12. [12] § Methods › Model development › Training strategy ↔ othercode/classification_model/embedding_LR.py, lines 277–333 · score 0.54 · Gaussian noise, affine, intensity, transforms, patch, Training

Paper

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The authors' code

Jupyter notebook · 278 lines · 9.9 KB · Apache-2.0 · 3 matches

  1. # %%
  2. import matplotlib.pyplot as plt
  3. from sklearn.metrics import roc_curve, auc, roc_auc_score
  4. from sklearn.utils import resample
  5. import numpy as np
  6. import matplotlib as mpl
  7. import pandas as pd
  9. #----for evi -----
  11. df1 = pd.read_csv(r"/path/to/workspace/Complete_set/Experiments/Results/UMedPT_LR/combine_origin.csv")
  12. df2 = pd.read_csv(r"/path/to/workspace/Complete_set/Experiments/Results/UMedPT_LR/axial_original.csv")
  13. df3 = pd.read_csv(r"/path/to/workspace/Complete_set/Experiments/Results/UMedPT_LR/sagittal_original.csv")
  14. df4 = pd.read_csv(r"/path/to/workspace/Complete_set/Experiments/Results/FMmodel192/test/best_FM_192.csv")
  16. # Extract ground truth and predicted probabilities for EVI and MFI
  17. y_true = df1['y_test_evi'].values
  19. y_pred_model1 = df1['y_pred_prob_evi'].values
  20. y_pred_model2 = df2['y_pred_prob_evi'].values
  21. y_pred_model3 = df3['y_pred_prob_evi'].values
  22. y_pred_model4 = df4['predicted_probability_evi'].values
  24. # Model names and placeholder for AUC formatting
  25. models = {
  26. 'UMedPT_LR axial+sagittal': y_pred_model1,
  27. 'UMedPT_LR axial': y_pred_model2,
  28. 'UMedPT_LR sagittal': y_pred_model3,
  29. 'UMedPT axial': y_pred_model4,
  30. }
  32. # Soft, elegant colors: light blue, light yellow, light green, light red
  33. # colors = ['#d62728','#1f77b4', '#ffbb00', '#2ca02c'] # soft red blue, yellow, green
  34. # colors = ['#e41a1c', '#377eb8', '#4daf4a', '#984ea3']
  35. # colors = ['#FBDD85', '#80A6E2', '#F46F43', '#403990']
  36. # colors = ['#403990', '#F46F43','#80A6E2','#FBDD85']
  37. colors = ['#403990', '#F46F43','#80A6E2','#FBDD85']
  39. # Set plot style to match high-quality publication standards (e.g., Nature)
  40. mpl.rcParams['font.family'] = 'sans-serif'
  41. mpl.rcParams['font.size'] = 10
  43. #bootstrap compute CI
  44. def bootstrap_roc_ci(y_true, y_pred, n_bootstraps=1000, alpha=0.95):
  45. rng = np.random.RandomState(42)
  46. tprs = []
  47. base_fpr = np.linspace(0, 1, 101)
  49. for _ in range(n_bootstraps):
  50. indices = rng.randint(0, len(y_pred), len(y_pred))
  51. if len(np.unique(y_true[indices])) < 2:
  52. continue
  53. fpr, tpr, _ = roc_curve(y_true[indices], y_pred[indices])
  54. tpr_interp = np.interp(base_fpr, fpr, tpr)
  55. tpr_interp[0] = 0.0
  56. tprs.append(tpr_interp)
  58. tprs = np.array(tprs)
  59. mean_tpr = tprs.mean(axis=0)
  60. std_tpr = tprs.std(axis=0)
  61. tpr_upper = np.minimum(mean_tpr + 1.96 * std_tpr, 1)
  62. tpr_lower = np.maximum(mean_tpr - 1.96 * std_tpr, 0)
  63. return base_fpr, mean_tpr, tpr_lower, tpr_upper
  65. def bootstrap_auc_ci(y_true, y_pred, n_bootstraps=1000, seed=42, alpha=0.95):
  66. rng = np.random.RandomState(seed)
  67. stats = []
  68. n = len(y_pred)
  69. y_true = np.asarray(y_true)
  70. y_pred = np.asarray(y_pred)
  71. for _ in range(n_bootstraps):
  72. idx = rng.randint(0, n, n)
  73. if len(np.unique(y_true[idx])) < 2:
  74. continue
  75. stats.append(roc_auc_score(y_true[idx], y_pred[idx]))
  76. lower = np.percentile(stats, (1 - alpha) / 2 * 100)
  77. upper = np.percentile(stats, (1 + alpha) / 2 * 100)
  78. return lower, upper
  81. # Create the figure
  82. plt.figure(figsize=(6, 6))
  84. # Plot ROC curves for each model
  85. # for (label, y_pred), color in zip(models.items(), colors):
  86. # fpr, mean_tpr, lower_tpr, upper_tpr = bootstrap_roc_ci(y_true, y_pred)
  87. # auc_score = auc(fpr, mean_tpr)
  88. # plt.plot(fpr, mean_tpr, lw=2, label=f"{label} (AUC: {auc_score:.2f})", color=color)
  89. # # plt.plot(fpr, lower_tpr, color=color, linestyle='--', alpha=0.6, linewidth=1)
  90. # # plt.plot(fpr, upper_tpr, color=color, linestyle='--', alpha=0.6, linewidth=1)
  91. # plt.fill_between(fpr, lower_tpr, upper_tpr, color=color, alpha=0.12)
  93. shade_first_n = 2
  94. for i, ((model_name, y_pred), color) in enumerate(zip(models.items(), colors)):
  95. fpr_raw, tpr_raw, _ = roc_curve(y_true, y_pred)
  96. auc_raw = roc_auc_score(y_true, y_pred)
  98. # --- AUC bootstrap CI ?? ---
  99. auc_lo, auc_hi = bootstrap_auc_ci(y_true, y_pred)
  101. # Legend ??????? or en-dash?
  102. label_text = f"{model_name} (AUC: {auc_raw:.2f} [{auc_lo:.2f}-{auc_hi:.2f}])"
  104. # ?????? or ?? plt.plot ???????
  105. plt.step(fpr_raw, tpr_raw, where='post', lw=2.2, color=color, label=label_text)
  107. # --- bootstrap CI for shading / dashed lines ---
  108. # fpr_ci, mean_tpr, lower_tpr, upper_tpr = bootstrap_roc_ci(y_true, y_pred)
  110. # if i < shade_first_n:
  111. # plt.fill_between(fpr, lower_tpr, upper_tpr, color=color, alpha=0.1, linewidth=0)
  112. # else:
  113. # plt.plot(fpr, lower_tpr, color=color, linestyle='--', linewidth=1.1, alpha=0.7)
  114. # plt.plot(fpr, upper_tpr, color=color, linestyle='--', linewidth=1.1, alpha=0.7)
  116. # Plot diagonal line representing random guess
  117. plt.plot([0, 1], [0, 1], 'k--', lw=1, label='Random Guess')
  119. # Set titles and labels
  120. plt.title('Best 4 Models for EVI Classification', fontsize=12, weight='normal')
  121. plt.xlabel('False Positive Rate', fontsize=12)
  122. plt.ylabel('True Positive Rate', fontsize=12)
  124. plt.legend(loc='lower right', frameon=False, fontsize=9)
  125. plt.grid(True, linestyle='--', alpha=0.3)
  126. plt.xticks(np.linspace(0, 1, 6))
  127. plt.yticks(np.linspace(0, 1, 6))
  129. # Thicker border lines
  130. for spine in plt.gca().spines.values():
  131. spine.set_linewidth(1.2)
  133. output_dir = "/path/to/workspace/Complete_set/Experiments/Results/ROC PHOTO/"
  134. filename = "roc_curve_evi_new3.png"
  136. # Optimize layout
  137. plt.tight_layout()
  138. plt.savefig(output_dir + filename, dpi=600, bbox_inches='tight')
  139. plt.show()
  142. # %%
  145. # %%
  146. import matplotlib.pyplot as plt
  147. from sklearn.metrics import roc_curve, auc
  148. import numpy as np
  149. import matplotlib as mpl
  150. import pandas as pd
  152. #----for MFI-----
  154. df1 = pd.read_csv(r"/path/to/workspace/Complete_set/Experiments/Results/axial_harmo_FM192/best_FM_axial_harmo.csv")
  155. df2 = pd.read_csv(r"/path/to/workspace/Complete_set/Experiments/Results/UMedPT_LR/axial_original.csv")
  156. df3 = pd.read_csv(r"/path/to/workspace/Complete_set/Experiments/Results/sagittal_harmo/best_scr_segresnet_sagittalharmo.csv")
  157. df4 = pd.read_csv(r"/path/to/workspace/Complete_set/Experiments/Results/Segresnet/best_segresnet_axial_original.csv")
  159. # Extract ground truth and predicted probabilities for EVI and MFI
  160. y_true = df1['ground_truth_mfi'].values
  162. y_pred_model1 = df1['predicted_probability_mfi'].values
  163. y_pred_model2 = df2['y_pred_prob_mfi'].values
  164. y_pred_model3 = df3['predicted_probability_mfi'].values
  165. y_pred_model4 = df4['predicted_probability_mfi'].values
  168. # Model names and placeholder for AUC formatting
  169. models = {
  170. 'UMedPT axial_harmo': y_pred_model1,
  171. 'UMedPT_LR axial': y_pred_model2,
  172. 'SeResnet axial': y_pred_model4,
  173. 'SeResnet sagittal_harmo': y_pred_model3
  174. }
  176. # Soft, elegant colors: light blue, light yellow, light green, light red
  177. # colors = ['#d62728','#1f77b4', '#ffbb00', '#2ca02c'] # soft red blue, yellow, green
  178. colors = ['#403990', '#F46F43','#80A6E2','#FBDD85']
  180. # Set plot style to match high-quality publication standards (e.g., Nature)
  181. mpl.rcParams['font.family'] = 'sans-serif'
  182. mpl.rcParams['font.size'] = 10
  184. def bootstrap_roc_ci(y_true, y_pred, n_bootstraps=1000, alpha=0.95):
  185. rng = np.random.RandomState(42)
  186. tprs = []
  187. base_fpr = np.linspace(0, 1, 101)
  189. for _ in range(n_bootstraps):
  190. indices = rng.randint(0, len(y_pred), len(y_pred))
  191. if len(np.unique(y_true[indices])) < 2:
  192. continue
  193. fpr, tpr, _ = roc_curve(y_true[indices], y_pred[indices])
  194. tpr_interp = np.interp(base_fpr, fpr, tpr)
  195. tpr_interp[0] = 0.0
  196. tprs.append(tpr_interp)
  198. tprs = np.array(tprs)
  199. mean_tpr = tprs.mean(axis=0)
  200. std_tpr = tprs.std(axis=0)
  201. tpr_upper = np.minimum(mean_tpr + 1.96 * std_tpr, 1)
  202. tpr_lower = np.maximum(mean_tpr - 1.96 * std_tpr, 0)
  203. return base_fpr, mean_tpr, tpr_lower, tpr_upper
  205. def bootstrap_auc_ci(y_true, y_pred, n_bootstraps=1000, seed=42, alpha=0.95):
  206. rng = np.random.RandomState(seed)
  207. stats = []
  208. n = len(y_pred)
  209. y_true = np.asarray(y_true)
  210. y_pred = np.asarray(y_pred)
  211. for _ in range(n_bootstraps):
  212. idx = rng.randint(0, n, n)
  213. if len(np.unique(y_true[idx])) < 2:
  214. continue
  215. stats.append(roc_auc_score(y_true[idx], y_pred[idx]))
  216. lower = np.percentile(stats, (1 - alpha) / 2 * 100)
  217. upper = np.percentile(stats, (1 + alpha) / 2 * 100)
  218. return lower, upper
  220. # Create the figure
  221. plt.figure(figsize=(6, 6))
  223. shade_first_n = 2
  224. for i, ((model_name, y_pred), color) in enumerate(zip(models.items(), colors)):
  225. fpr_raw, tpr_raw, _ = roc_curve(y_true, y_pred)
  226. auc_raw = roc_auc_score(y_true, y_pred)
  228. # --- AUC bootstrap CI ?? ---
  229. auc_lo, auc_hi = bootstrap_auc_ci(y_true, y_pred)
  231. # Legend ??????? or en-dash?
  232. label_text = f"{model_name} (AUC: {auc_raw:.2f} [{auc_lo:.2f}-{auc_hi:.2f}])"
  234. # ?????? or ?? plt.plot ???????
  235. plt.step(fpr_raw, tpr_raw, where='post', lw=2.2, color=color, label=label_text)
  237. # # --- bootstrap CI for shading / dashed lines ---
  238. # fpr_ci, mean_tpr, lower_tpr, upper_tpr = bootstrap_roc_ci(y_true, y_pred)
  240. # if i < shade_first_n:
  241. # plt.fill_between(fpr, lower_tpr, upper_tpr, color=color, alpha=0.1, linewidth=0)
  242. # else:
  243. # plt.plot(fpr, lower_tpr, color=color, linestyle='--', linewidth=1.1, alpha=0.7)
  244. # plt.plot(fpr, upper_tpr, color=color, linestyle='--', linewidth=1.1, alpha=0.7)
  246. # Plot diagonal line representing random guess
  247. plt.plot([0, 1], [0, 1], 'k--', lw=1, label='Random Guess')
  249. # Set titles and labels
  250. plt.title('Best 4 Models for MFI Classification', fontsize=12, weight='normal')
  251. plt.xlabel('False Positive Rate', fontsize=12)
  252. plt.ylabel('True Positive Rate', fontsize=12)
  254. # Add legend in bottom right, no frame
  255. plt.legend(loc='lower right', frameon=False, fontsize=9)
  257. # Add light grid lines
  258. plt.grid(True, linestyle='--', alpha=0.3)
  260. # Customize ticks
  261. plt.xticks(np.linspace(0, 1, 6))
  262. plt.yticks(np.linspace(0, 1, 6))
  264. # Thicker border lines
  265. for spine in plt.gca().spines.values():
  266. spine.set_linewidth(1.2)
  268. output_dir = "/path/to/workspace/Complete_set/Experiments/Results/ROC PHOTO/"
  269. filename = "roc_curve_mfi_new3.png"
  271. # Optimize layout
  272. plt.tight_layout()
  273. plt.savefig(output_dir + filename, dpi=300, bbox_inches='tight')
  274. plt.show()
  277. # %%

draw_ROC.ipynb at commit fa683ec, under Apache-2.0 · at the source

Overview

Authors: Yumeng Zhang1, Shruti Atul Mali1, Danial Khan1, Sina Amirrajab1, Eduardo Ibor-Crespo2, Ana Jimenez-Pastor2, Gloria Ribas3, Silvia Flor-Arnal3, Marta Zerunian4, Christophe Aubé5,6, Luis Martí-Bonmatí3,7, Zohaib Salahuddin1, Philippe Lambin1,8
ORCID iDs: Philippe Lambin
  1. The D-Lab, Department of Precision Medicine, GROW - Research Institute for Oncology and Reproduction, Maastricht University,Maastricht, The Netherlands
  2. Research & Frontiers in AI Department, Quantitative Imaging Biomarkers in Medicine, Quibim SL, Valencia, Spain
  3. Biomedical Imaging Research Group, La Fe Health Research Institute, Valencia, Spain
  4. Radiology Unit, Department of Surgical and Medical Sciences and Translational Medicine, Sapienza University of Rome, Sant’Andrea Hospital,Rome, Italy
  5. Laboratoire HIFIH, Université d’Angers, SFR ICAT 4208,Angers, France
  6. Department of Radiology, CHU Angers,Angers, France
  7. Medical Imaging Department, La Fe University and Polytechnic Hospital,Valencia, Spain
  8. Department of Radiology and Nuclear Medicine, GROW - Research Institute for Oncology and Reproduction, Maastricht University Medical Center+,Maastricht, The Netherlands
Institutions: Maastricht University (Netherlands); Instituto de Investigación Sanitaria La Fe (Spain); Sapienza University of Rome (Italy); Université d'Angers (France); Centre Hospitalier Universitaire d'Angers (France); Hospital Universitari i Politècnic La Fe (Spain)
Journal: Insights into imaging, volume 17, issue 1, article 141
Dates: received 2 December 2025; accepted 19 April 2026; published online 22 May 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.1186/s13244-026-02296-3 · PMID 42171860 · PMCID PMC13197564 · OpenAlex W7162068795
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: structural MRI / diffusion (modality), other condition (population)
Methods: Connectivity, Smoothing, state filtering, decompositions, Machine learning, Preprocessing, Statistics
Keywords: Rectal neoplasms, Magnetic resonance imaging, Extramural vascular invasion, Mesorectal fascia, Deep learning
Topic: Colorectal Cancer Surgical Treatments (Oncology, Medicine), according to OpenAlex
Funding: AIDAVA (101057062); EUCAIM (101100633); EuCanImage (952103); GLIOMATCH (101136670); IMI-OPTIMA (101034347); ImmunoSABR (733008); REALM (101095435); RADIOVAL (101057699); CHAIMELEON (952172); China Scholarship Council (202208110055); HYPOXIMMUNO (694812); AUTO.DISTINCT (957565); JTC2016 CLEARLY (UM 2017-8295)
Citations: not cited yet (Europe PMC); 44 references in the paper

Abstract

Objectives: Accurate MRI-based identification of extramural vascular invasion (EVI) and mesorectal fascia invasion (MFI) is crucial for risk-stratified rectal cancer treatment. However, subjective visual assessment and inter-institutional variability limit diagnostic consistency. This study developed and evaluated a multi-center, foundation model-driven framework that automatically classifies EVI and MFI on axial and sagittal MRI.

Materials and methods: A total of 331 pre-treatment rectal cancer T2-weighted MRI scans from three European hospitals were retrospectively recruited. A self-supervised frequency domain harmonization strategy was applied to reduce scanner variability. Three classifiers, SeResNet, the universal biomedical pretrained model (UMedPT) with a multilayer perceptron head, and a logistic-regression variant using frozen UMedPT features (UMedPT_LR), were trained (n = 265) and tested (n = 66). Gradient-weighted class activation mapping (Grad-CAM) visualized model predictions.

Results: UMedPT_LR achieved the best EVI performance with multiplanar fusion (AUC = 0.82, test set). For MFI, UMedPT trained on axial harmonized images yielded the highest performance (AUC = 0.77). Both tasks outperformed the CHAIMELEON 2024 benchmark (EVI: 0.82 vs 0.74; MFI: 0.77 vs 0.75). Harmonization enhanced MFI classification, and multiplanar fusion further boosted EVI performance. Grad-CAM confirmed biologically plausible attention on peritumoral regions (EVI) and mesorectal fascia margins (MFI).

Conclusion: The proposed foundation model-driven framework, leveraging frequency domain harmonization and multiplanar fusion, achieves state-of-the-art performance for automated EVI and MFI classification on MRI, demonstrating strong generalizability across multiple centers.

Critical relevance statement: Addressing inter-center inconsistencies in rectal cancer MRI, a multiplanar foundation model with cross-scanner harmonization significantly improves the detection of EVI and MFI, potentially standardizing staging and guiding therapy.

Key Points: Among the first studies to investigate automated classification of both EVI and MFI using axial and sagittal T2-weighted MRI. Foundation model-derived features outperform conventional convolutional neural networks (CNNs) for EVI and MFI classification. Frequency domain harmonization and multiplanar fusion selectively enhance diagnostic performance. Automated prediction of EVI and MFI may support more consistent staging and clinical decision-making across institutions.

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 12 matches between paragraphs and lines of code.

yumengzhang97/foundation-model-rectal-mri

License: Apache-2.0
State: the link answers, verified on 28 September 2026
Evidence: files inventoried
Commit: fa683ec2c4c201aca83cb7070eda9863bbf589d8, 6 July 2026
Languages: Jupyter (46), Python (11)
Size: 61 files, 57 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: README, license file, environment (requirements.txt), 46 notebooks
Not found: CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (36 files), SimpleITK (31 files), pandas (24 files), scikit-learn (12 files), PyTorch (8 files), MONAI (7 files), Matplotlib (6 files), NiBabel (4 files), nnU-Net (2 files), pydicom (2 files), SciPy (2 files), OpenCV (1 file)
Availability: 1 check, the latest on 28 September 2026: the link answers
  • 28 September 2026: the link answers
59 files
At the source: github.com/yumengzhang97/foundation-model-rectal-mri

Code availability

The code will be made publicly available at https://github.com/yumengzhang97/foundation-model-rectal-mri upon publication.

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

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  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
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  • 12 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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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 multicentre MRI datasets used in this study are subject to institutional and EU data protection regulations and therefore cannot be publicly shared. An earlier version of this manuscript was made available as a preprint on arXiv (10.48550/arXiv.2505.18058). The preprint was subsequently updated during the review process.

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

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Recorded: type, language, journal, volume, issue, pages, dates, 13 authors, 5 keywords, 13 funders, 40 references.

Cite

This paper

Zhang, Y., Mali, S. A., Khan, D., Amirrajab, S., Ibor-Crespo, E., Jimenez-Pastor, A., Ribas, G., Flor-Arnal, S., Zerunian, M., Aubé, C., Martí-Bonmatí, L., Salahuddin, Z., & Lambin, P. (2026). A pre-trained foundation model framework for multiplanar MRI classification of extramural vascular invasion and mesorectal fascia invasion in rectal cancer. Insights into imaging, 17(1), 141. https://doi.org/10.1186/s13244-026-02296-3

BibTeX

@article{zhang2026pre,
author = {Zhang, Yumeng and Mali, Shruti Atul and Khan, Danial and Amirrajab, Sina and Ibor-Crespo, Eduardo and Jimenez-Pastor, Ana and Ribas, Gloria and Flor-Arnal, Silvia and Zerunian, Marta and Aubé, Christophe and Martí-Bonmatí, Luis and Salahuddin, Zohaib and Lambin, Philippe},
title = {{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},
year = {2026},
month = may,
volume = {17},
number = {1},
pages = {141},
publisher = {Springer},
issn = {1869-4101},
doi = {10.1186/s13244-026-02296-3},
url = {https://doi.org/10.1186/s13244-026-02296-3},
pmid = {42171860},
pmcid = {PMC13197564}
}

RIS

TY - JOUR
AU - Zhang, Yumeng
AU - Mali, Shruti Atul
AU - Khan, Danial
AU - Amirrajab, Sina
AU - Ibor-Crespo, Eduardo
AU - Jimenez-Pastor, Ana
AU - Ribas, Gloria
AU - Flor-Arnal, Silvia
AU - Zerunian, Marta
AU - Aubé, Christophe
AU - Martí-Bonmatí, Luis
AU - Salahuddin, Zohaib
AU - Lambin, Philippe
TI - A pre-trained foundation model framework for multiplanar MRI classification of extramural vascular invasion and mesorectal fascia invasion in rectal cancer
T2 - Insights into imaging
J2 - Insights Imaging
PY - 2026
DA - 2026/05/22
VL - 17
IS - 1
SP - 141
SN - 1869-4101
PB - Springer
DO - 10.1186/s13244-026-02296-3
UR - https://doi.org/10.1186/s13244-026-02296-3
LA - en
ER -

CSL-JSON

{
"id": "10.1186/s13244-026-02296-3",
"type": "article-journal",
"title": "A pre-trained foundation model framework for multiplanar MRI classification of extramural vascular invasion and mesorectal fascia invasion in rectal cancer",
"container-title": "Insights into imaging",
"author": [
{
"family": "Zhang",
"given": "Yumeng"
},
{
"family": "Mali",
"given": "Shruti Atul"
},
{
"family": "Khan",
"given": "Danial"
},
{
"family": "Amirrajab",
"given": "Sina"
},
{
"family": "Ibor-Crespo",
"given": "Eduardo"
},
{
"family": "Jimenez-Pastor",
"given": "Ana"
},
{
"family": "Ribas",
"given": "Gloria"
},
{
"family": "Flor-Arnal",
"given": "Silvia"
},
{
"family": "Zerunian",
"given": "Marta"
},
{
"family": "Aubé",
"given": "Christophe"
},
{
"family": "Martí-Bonmatí",
"given": "Luis"
},
{
"family": "Salahuddin",
"given": "Zohaib"
},
{
"family": "Lambin",
"given": "Philippe"
}
],
"container-title-short": "Insights Imaging",
"volume": "17",
"issue": "1",
"page": "141",
"DOI": "10.1186/s13244-026-02296-3",
"PMID": "42171860",
"PMCID": "PMC13197564",
"ISSN": "1869-4101",
"publisher": "Springer",
"URL": "https://doi.org/10.1186/s13244-026-02296-3",
"language": "en",
"issued": {
"date-parts": [
[
2026,
5,
22
]
]
}
}

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