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Purpose <p>Intraoperative margin assessment is crucial to ensure complete tumor removal and minimize the risk of cancer recurrence during breast-conserving surgery. The Intelligent Knife (iKnife), a mass spectrometry device that analyzes surgical smoke, shows promise in near-real-time margin evaluation. However, current AI models depend on labeled ex-vivo datasets, which are costly and time-consuming to produce. This research explores the potential of machine learning anomaly detection models to reduce reliance on labeled ex-vivo datasets by utilizing unlabeled intraoperative spectra.</p> Methods <p>iKnife spectra were collected intraoperatively from 15 breast cancer surgeries. Ex-vivo samples were recorded from the resected specimen by a pathologist. Healthy samples were from the margin, and tumor samples were from the cross-section. We trained four anomaly detection methods, Isolation Forest (iForest), One Class Principal Component Analysis (OCPCA), Generalized One Class Discriminative Subspaces (GODS), and its Kernelized extension (KGODS), under two strategies: (i) intraoperative data only and (ii) intraoperative data plus healthy ex-vivo data. Performance was evaluated via four-fold cross-validation on labeled ex-vivo samples, with an additional ensemble approach on a held-out set. We compared the models to benchmark supervised classifiers and explored intraoperative feasibility with a retrospective case.</p> Results <p>Using intraoperative data alone, the average balanced accuracies were 70% (iForest), 81% (OC-PCA), 77% (GODS), and 81% (KGODS) during four-fold cross-validation. Adding healthy ex-vivo data improved performance across all models; however, OC-PCA remained competitive without ex-vivo labels. On the held-out set, OC-PCA trained only on intraoperative data achieved 81% balanced accuracy, 90% sensitivity, and 72% specificity. OC-PCA was selected for intraoperative feasibility and correctly detected the tumor breach with one false positive.</p> Conclusion <p>Anomaly detection models, particularly OC-PCA, can identify positive breast cancer margins with no labeled ex-vivo data. Though slightly lower in performance than supervised classifiers, they offer a promising low-resource alternative for intraoperative label generation and semi-supervised training, which can enhance clinical deployment.</p>

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Anomaly detection using intraoperative iKnife data: a comparative analysis in breast cancer surgery

  • 期刊论文

  • 发表日期：

    2025年7月29日

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  • Olivia Radcliffe,
  • Laura Connolly,
  • Amoon Jamzad,
  • Martin Kaufmann,
  • Shaila Merchant,
  • Jay Engel,
  • Ross Walker,
  • Sonal Varma,
  • Gabor Fichtinger,
  • John Rudan,
  • Parvin Mousavi

摘要

Purpose

Intraoperative margin assessment is crucial to ensure complete tumor removal and minimize the risk of cancer recurrence during breast-conserving surgery. The Intelligent Knife (iKnife), a mass spectrometry device that analyzes surgical smoke, shows promise in near-real-time margin evaluation. However, current AI models depend on labeled ex-vivo datasets, which are costly and time-consuming to produce. This research explores the potential of machine learning anomaly detection models to reduce reliance on labeled ex-vivo datasets by utilizing unlabeled intraoperative spectra.

Methods

iKnife spectra were collected intraoperatively from 15 breast cancer surgeries. Ex-vivo samples were recorded from the resected specimen by a pathologist. Healthy samples were from the margin, and tumor samples were from the cross-section. We trained four anomaly detection methods, Isolation Forest (iForest), One Class Principal Component Analysis (OCPCA), Generalized One Class Discriminative Subspaces (GODS), and its Kernelized extension (KGODS), under two strategies: (i) intraoperative data only and (ii) intraoperative data plus healthy ex-vivo data. Performance was evaluated via four-fold cross-validation on labeled ex-vivo samples, with an additional ensemble approach on a held-out set. We compared the models to benchmark supervised classifiers and explored intraoperative feasibility with a retrospective case.

Results

Using intraoperative data alone, the average balanced accuracies were 70% (iForest), 81% (OC-PCA), 77% (GODS), and 81% (KGODS) during four-fold cross-validation. Adding healthy ex-vivo data improved performance across all models; however, OC-PCA remained competitive without ex-vivo labels. On the held-out set, OC-PCA trained only on intraoperative data achieved 81% balanced accuracy, 90% sensitivity, and 72% specificity. OC-PCA was selected for intraoperative feasibility and correctly detected the tumor breach with one false positive.

Conclusion

Anomaly detection models, particularly OC-PCA, can identify positive breast cancer margins with no labeled ex-vivo data. Though slightly lower in performance than supervised classifiers, they offer a promising low-resource alternative for intraoperative label generation and semi-supervised training, which can enhance clinical deployment.