ArticleCancers2021
Artificial Intelligence-Based Segmentation of Residual Tumor in Histopathology of Pancreatic Cancer after Neoadjuvant Treatment.
Article in Cancers, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Clinical Application Progress of Artificial Intelligence in Pancreatic Cancer: From Diagnosis to Immunotherapy.Oncology research · 2026Review
- Detection and Classification of Pancreatic Cancer Nodules on CT Images using U-Net and Ensemble Models.Current medical imaging · 2026Article
- Machine learning-assisted mid-infrared spectrochemical fibrillar collagen imaging in clinical tissues.Journal of biomedical optics · 2024Article
- Artificial Intelligence in Pancreatic Image Analysis: A Review.Sensors (Basel, Switzerland) · 2024Review
- Pancreatic Ductal Adenocarcinoma (PDAC): A Review of Recent Advancements Enabled by Artificial Intelligence.Cancers · 2024Review
- Pathological Complete Response in Patients With Resected Pancreatic Adenocarcinoma After Preoperative Chemotherapy.JAMA network open · 2024Observational
- Machine learning assisted mid-infrared spectrochemical fibrillar collagen imaging in clinical tissues.bioRxiv : the preprint server for biology · 2024Article
- Preoperative chemotherapy, radiotherapy and surgical decision-making in patients with borderline resectable and locally advanced pancreatic cancer.Nature reviews. Gastroenterology & hepatology · 2024Review
- Exploring the application and future outlook of Artificial intelligence in pancreatic cancer.Frontiers in oncology · 2024Review
- Roadmap for providing and leveraging annotated data by cytologists in the PDAC domain as open data: support for AI-based pathology image analysis development and data utilization strategies.Frontiers in oncology · 2024Article
- Recent Advances and Future Challenges in Pancreatic Cancer Care: Early Detection, Liquid Biopsies, Precision Medicine and Artificial Intelligence.Journal of clinical medicine · 2023Article
- An Optical Sensory System for Assessment of Residual Cancer Burden in Breast Cancer Patients Undergoing Neoadjuvant Chemotherapy.Sensors (Basel, Switzerland) · 2023Article
- Application of Artificial Intelligence in the Diagnosis, Treatment, and Prognostic Evaluation of Mediastinal Malignant Tumors.Journal of clinical medicine · 2023Review
- [Multi-tissue segmentation model of whole slide image of pancreatic cancer based on multi task and attention mechanism].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- Histopathological tumour response scoring in resected pancreatic cancer following neoadjuvant therapy: international interobserver study (ISGPP-1).The British journal of surgery · 2022Article
- Setting the Research Agenda for Clinical Artificial Intelligence in Pancreatic Adenocarcinoma Imaging.Cancers · 2022Review
- Critical issues in pathologic evaluation of pancreatic ductal adenocarcinoma resected after neoadjuvant treatment: a narrative review.Chinese clinical oncology · 2022Review
- Mapping intellectual structures and research hotspots in the application of artificial intelligence in cancer: A bibliometric analysis.Frontiers in oncology · 2022Article
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHistologic examination of resected pancreatic cancer after neoadjuvant therapy (NAT) is used to assess the effect of NAT and may guide the choice for adjuvant treatment. However, evaluating residual tumor burden in pancreatic cancer is challenging given tumor response heterogeneity and challenging histomorphology. Artificial intelligence techniques may offer a more reproducible approach.
methodsFrom 64 patients, one H&E-stained slide of resected pancreatic cancer after NAT was digitized. Three separate classes were manually outlined in each slide (i.e., tumor, normal ducts, and remaining epithelium). Corresponding segmentation masks and patches were generated and distributed over training, validation, and test sets. Modified U-nets with varying encoders were trained, and F1 scores were obtained to express segmentation accuracy.
resultsThe highest mean segmentation accuracy was obtained using modified U-nets with a DenseNet161 encoder. Tumor tissue was segmented with a high mean F1 score of 0.86, while the overall multiclass average F1 score was 0.82.
conclusionsThis study shows that artificial intelligence-based assessment of residual tumor burden is feasible given the promising obtained F1 scores for tumor segmentation. This model could be developed into a tool for the objective evaluation of the response to NAT and may potentially guide the choice for adjuvant treatment.
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