LIVIVO - The Search Portal for Life Sciences

zur deutschen Oberfläche wechseln
Advanced search

Search results

Result 1 - 3 of total 3

Search options

  1. Article ; Online: From quantitative metrics to clinical success: assessing the utility of deep learning for tumor segmentation in breast surgery.

    Yeung, Chris / Ungi, Tamas / Hu, Zoe / Jamzad, Amoon / Kaufmann, Martin / Walker, Ross / Merchant, Shaila / Engel, Cecil Jay / Jabs, Doris / Rudan, John / Mousavi, Parvin / Fichtinger, Gabor

    International journal of computer assisted radiology and surgery

    2024  

    Abstract: Purpose: Preventing positive margins is essential for ensuring favorable patient outcomes following breast-conserving surgery (BCS). Deep learning has the potential to enable this by automatically contouring the tumor and guiding resection in real time. ...

    Abstract Purpose: Preventing positive margins is essential for ensuring favorable patient outcomes following breast-conserving surgery (BCS). Deep learning has the potential to enable this by automatically contouring the tumor and guiding resection in real time. However, evaluation of such models with respect to pathology outcomes is necessary for their successful translation into clinical practice.
    Methods: Sixteen deep learning models based on established architectures in the literature are trained on 7318 ultrasound images from 33 patients. Models are ranked by an expert based on their contours generated from images in our test set. Generated contours from each model are also analyzed using recorded cautery trajectories of five navigated BCS cases to predict margin status. Predicted margins are compared with pathology reports.
    Results: The best-performing model using both quantitative evaluation and our visual ranking framework achieved a mean Dice score of 0.959. Quantitative metrics are positively associated with expert visual rankings. However, the predictive value of generated contours was limited with a sensitivity of 0.750 and a specificity of 0.433 when tested against pathology reports.
    Conclusion: We present a clinical evaluation of deep learning models trained for intraoperative tumor segmentation in breast-conserving surgery. We demonstrate that automatic contouring is limited in predicting pathology margins despite achieving high performance on quantitative metrics.
    Language English
    Publishing date 2024-04-20
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 2365628-1
    ISSN 1861-6429 ; 1861-6410
    ISSN (online) 1861-6429
    ISSN 1861-6410
    DOI 10.1007/s11548-024-03133-y
    Database MEDical Literature Analysis and Retrieval System OnLINE

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 6615: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  2. Article ; Online: Real-time automatic tumor segmentation for ultrasound-guided breast-conserving surgery navigation.

    Hu, Zoe / Nasute Fauerbach, Paola V / Yeung, Chris / Ungi, Tamas / Rudan, John / Engel, Cecil Jay / Mousavi, Parvin / Fichtinger, Gabor / Jabs, Doris

    International journal of computer assisted radiology and surgery

    2022  Volume 17, Issue 9, Page(s) 1663–1672

    Abstract: Purpose: Ultrasound-based navigation is a promising method in breast-conserving surgery, but tumor contouring often requires a radiologist at the time of surgery. Our goal is to develop a real-time automatic neural network-based tumor contouring process ...

    Abstract Purpose: Ultrasound-based navigation is a promising method in breast-conserving surgery, but tumor contouring often requires a radiologist at the time of surgery. Our goal is to develop a real-time automatic neural network-based tumor contouring process for intraoperative guidance. Segmentation accuracy is evaluated by both pixel-based metrics and expert visual rating.
    Methods: This retrospective study includes 7318 intraoperative ultrasound images acquired from 33 breast cancer patients, randomly split between 80:20 for training and testing. We implement a u-net architecture to label each pixel on ultrasound images as either tumor or healthy breast tissue. Quantitative metrics are calculated to evaluate the model's accuracy. Contour quality and usability are also assessed by fellowship-trained breast radiologists and surgical oncologists. Additionally, the viability of using our u-net model in an existing surgical navigation system is evaluated by measuring the segmentation frame rate.
    Results: The mean dice similarity coefficient of our u-net model is 0.78, with an area under the receiver-operating characteristics curve of 0.94, sensitivity of 0.95, and specificity of 0.67. Expert visual ratings are positive, with 93% of responses rating tumor contour quality at or above 7/10, and 75% of responses rating contour quality at or above 8/10. Real-time tumor segmentation achieved a frame rate of 16 frames-per-second, sufficient for clinical use.
    Conclusion: Neural networks trained with intraoperative ultrasound images provide consistent tumor segmentations that are well received by clinicians. These findings suggest that neural networks are a promising adjunct to alleviate radiologist workload as well as improving efficiency in breast-conserving surgery navigation systems.
    MeSH term(s) Breast/diagnostic imaging ; Breast Neoplasms/diagnostic imaging ; Breast Neoplasms/surgery ; Female ; Humans ; Image Processing, Computer-Assisted/methods ; Mastectomy, Segmental ; Retrospective Studies ; Ultrasonography, Interventional
    Language English
    Publishing date 2022-05-19
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 2365628-1
    ISSN 1861-6429 ; 1861-6410
    ISSN (online) 1861-6429
    ISSN 1861-6410
    DOI 10.1007/s11548-022-02658-4
    Database MEDical Literature Analysis and Retrieval System OnLINE

    Full text online

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 6615: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 2021: Bestellungen von Artikeln über das Online-Bestellformular
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

  3. Article ; Online: Real-time electromagnetic navigation for breast-conserving surgery using NaviKnife technology: A matched case-control study.

    Gauvin, Gabrielle / Yeo, Caitlin T / Ungi, Tamas / Merchant, Shaila / Lasso, Andras / Jabs, Doris / Vaughan, Thomas / Rudan, John F / Walker, Ross / Fichtinger, Gabor / Engel, Cecil Jay

    The breast journal

    2019  Volume 26, Issue 3, Page(s) 399–405

    Abstract: Breast-conserving surgery (BCS) is a mainstay in breast cancer treatment. For nonpalpable breast cancers, current strategies have limited accuracy, contributing to high positive margin rates. We developed NaviKnife, a surgical navigation system based on ... ...

    Abstract Breast-conserving surgery (BCS) is a mainstay in breast cancer treatment. For nonpalpable breast cancers, current strategies have limited accuracy, contributing to high positive margin rates. We developed NaviKnife, a surgical navigation system based on real-time electromagnetic (EM) tracking. The goal of this study was to confirm the feasibility of intraoperative EM navigation in patients with nonpalpable breast cancer and to assess the potential value of surgical navigation. We recruited 40 patients with ultrasound visible, single, nonpalpable lesions, undergoing BCS. Feasibility was assessed by equipment functionality and sterility, acceptable duration of the operation, and surgeon feedback. Secondary outcomes included specimen volume, positive margin rate, and reoperation outcomes. Study patients were compared to a control group by a matched case-control analysis. There was no equipment failure or breach of sterility. The median operative time was 66 (44-119) minutes with NaviKnife vs 65 (34-158) minutes for the control (P = .64). NaviKnife contouring time was 3.2 (1.6-9) minutes. Surgeons rated navigation as easy to setup, easy to use, and useful in guiding nonpalpable tumor excision. The mean specimen volume was 95.4 ± 73.5 cm
    MeSH term(s) Breast Neoplasms/diagnostic imaging ; Breast Neoplasms/surgery ; Case-Control Studies ; Electromagnetic Phenomena ; Female ; Humans ; Mastectomy, Segmental ; Reoperation ; Retrospective Studies
    Language English
    Publishing date 2019-09-17
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 1289960-4
    ISSN 1524-4741 ; 1075-122X
    ISSN (online) 1524-4741
    ISSN 1075-122X
    DOI 10.1111/tbj.13480
    Database MEDical Literature Analysis and Retrieval System OnLINE

    Full text online

    More links

    Kategorien

    In stock of ZB MED Cologne/Königswinter

    Zs.A 4483: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (2.OG)
    ab Jg. 2022: Lesesaal (EG)

    Order via subito

    This service is chargeable due to the Delivery terms set by subito. Orders including an article and supplementary material will be classified as separate orders. In these cases, fees will be demanded for each order.

To top