{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T05:08:05Z","timestamp":1789103285152,"version":"build-2803163510"},"reference-count":57,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T00:00:00Z","timestamp":1789084800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Med."],"abstract":"<jats:sec>\n                    <jats:title>Objective<\/jats:title>\n                    <jats:p>Improving the diagnostic precision of skin and subcutaneous lesion assessments is the main goal of this study. Although elastography is an effective method for determining tissue stiffness, the high-dimensional, non-linear data these scans generate frequently present challenges for traditional deep learning models. To capture fine-grained stiffness patterns that conventional models may overlook, this study proposes and evaluates a hybrid quantum-classical deep learning architecture.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A retrospective dataset of 520 elastographic images with histological confirmation is used in the investigation. The proposed methodology uses a multi-stage hybrid pipeline, in which initial feature vectors are extracted from raw images using a traditional Convolutional Neural Network (CNN). A parameterized quantum circuit layer receives these vectors. This layer recognizes complex textural patterns by performing extensive transformations within a high-dimensional Hilbert space. A final fully connected layer processes the output to categorize lesions as either benign or malignant. We present a proof-of-concept simulation of combining a parameterized quantum layer with a classical CNN backbone. Although the current classical software emulation introduces a latency trade-off (124.5 ms vs. 8.4 ms per image), our result can serve as a baseline for analyzing low-parameter feature representations before running on physical QPU hardware.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Compared to fully classical baselines, the hybrid model showed a notable performance improvement. Important conclusions include 94.3% diagnostic accuracy, 93.8% sensitivity, and 94.7% specificity. From 0.921 (classical) to 0.963 (hybrid), the Area Under the ROC Curve (AUC) rose by 4.2%. Testing revealed that, especially in situations with unclear stiffness patterns, the quantum layer was the main source of increased predictive power.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>Quantum circuit integration offers more \u201cexpressive power\u201d than conventional neural networks. The model efficiently captures non-linear correlations in tissue density that are typically flattened or ignored by classical architectures, as it operates in a high-dimensional Hilbert space. This implies that combining quantum and classical techniques is especially well-suited to medical imaging, where minute changes in data have a clinically significant impact.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The study concludes that when it comes to describing cutaneous and subcutaneous masses, the hybrid classical-quantum model performs noticeably better than conventional deep learning techniques. These results indicate a promising advancement in medical AI that could provide physicians with more accurate and reliable tools for the early identification of cancer using elastographic data.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fmed.2026.1925092","type":"journal-article","created":{"date-parts":[[2026,9,11]],"date-time":"2026-09-11T04:36:16Z","timestamp":1789101376000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Quantum-enhanced deep learning for elastographic image-based characterization of cutaneous and subcutaneous masses"],"prefix":"10.3389","volume":"13","author":[{"given":"Hongping","family":"Zhang","sequence":"first","affiliation":[{"name":"The People's Hospital of Jianyang City","place":["Jianyang, Chengdu, Sichuan, 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