@article{fichtinger2024c,

title = {Abstract PO2-23-07: Three-dimensional navigated mass spectrometry for intraoperative margin assessment during breast cancer surgery},

author = {Martin Kaufmann and Amoon Jamzad and Tamas Ungi and Jessica R Rodgers and Teaghan Koster and Chris Yeung and Josh Ehrlich and Alice Santilli and Mark Asselin and Natasja Janssen and Julie McMullen and Kathryn Solberg and Joanna Cheesman and Alessia Di Carlo and Kevin Yi Mi Ren and Sonal Varma and Shaila Merchant and Cecil Jay Engel and G Ross Walker and Andrea Gallo and Doris Jabs and Parvin Mousavi and Gabor Fichtinger and John F Rudan},

url = {https://aacrjournals.org/cancerres/article/84/9_Supplement/PO2-23-07/743683},

year  = {2024},

date = {2024-01-01},

journal = {Cancer Research},

volume = {84},

issue = {9_Supplement},

pages = {PO2-23-07-PO2-23-07},

publisher = {The American Association for Cancer Research},

abstract = {Positive resection margins occur in approximately 25% of breast cancer (BCa) surgeries, requiring re-operation. Margin status is not routinely available during surgery; thus, technologies that identify residual cancer on the specimen or cavity are needed to provide intraoperative decision support that may reduce positive margin rates. Rapid evaporative ionization mass spectrometry (REIMS) is an emerging technique that chemically profiles the plume generated by tissue cauterization to classify the ablated tissue as either cancerous or non-cancerous, on the basis of detected lipid species. Although REIMS can distinguish cancer and non-cancerous breast tissue by the signals generated, it does not indicate the location of the classified tissue in real-time. Our objective was to combine REIMS with spatio-temporal navigation (navigated REIMS), and to compare performance of navigated REIMS with conventional …},

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pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2024i,

title = {Three-dimensional navigated mass spectrometry for intraoperative margin assessment during breast cancer surgery},

author = {Martin Kaufmann and Amoon Jamzad and Tamas Ungi and Jessica Rodgers and Teaghan Koster and Yeung Chris and Natasja Janssen and Julie McMullen and Kathryn Solberg and Joanna Cheesman and Kevin Ti Mi Ren and Sonal Varma and Shaila Merchant and Cecil Jay Engel and G Ross Walker and Andrea Gallo and Doris Jabs and Parvin Mousavi and Gabor Fichtinger and John Rudan},

url = {https://scholar.google.com/scholar?cluster=16985799098796735653&hl=en&oi=scholarr},

year  = {2024},

date = {2024-01-01},

volume = {31},

issue = {1},

pages = {S10-S10},

publisher = {SPRINGER},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{kaufmann2024,

title = {Testing of rapid evaporative mass spectrometry for histological tissue classification and molecular diagnostics in a multi-site study},

author = {Martin Kaufmann and Pierre-Maxence Vaysse and Adele Savage and Loes FS Kooreman and Natasja Janssen and Sonal Varma and Kevin Yi Mi Ren and Shaila Merchant and Cecil Jay Engel and Steven WM Olde Damink and Marjolein L Smidt and Sami Shousha and Hemali Chauhan and Evdoxia Karali and Emine Kazanc and George Poulogiannis and Gabor Fichtinger and Boglárka Tauber and Daniel R Leff and Steven D Pringle and John F Rudan and Ron MA Heeren and Tiffany Porta Siegel and Zoltán Takáts and Júlia Balog},

year  = {2024},

date = {2024-01-01},

journal = {British Journal of Cancer},

pages = {1-11},

publisher = {Nature Publishing Group UK},

abstract = {Background 

While REIMS technology has successfully been demonstrated for the histological identification of ex-vivo breast tumor tissues, questions regarding the robustness of the approach and the possibility of tumor molecular diagnostics still remain unanswered. In the current study, we set out to determine whether it is possible to acquire cross-comparable REIMS datasets at multiple sites for the identification of breast tumors and subtypes. 

Methods 

A consortium of four sites with three of them having access to fresh surgical tissue samples performed tissue analysis using identical REIMS setups and protocols. Overall, 21 breast cancer specimens containing pathology-validated tumor and adipose tissues were analyzed and results were compared using uni- and multivariate statistics on normal, WT and PIK3CA mutant ductal carcinomas. 

Results 

Statistical analysis of data from standards showed significant …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2021b,

title = {Domain adaptation and self-supervised learning for surgical margin detection},

author = {Alice ML Santilli and Amoon Jamzad and Alireza Sedghi and Martin Kaufmann and Kathryn Logan and Julie Wallis and Kevin Y M Ren and Natasja Janssen and Shaila Merchant and Jay Engel and Doug McKay and Sonal Varma and Ami Wang and Gabor Fichtinger and John F Rudan and Parvin Mousavi},

url = {https://link.springer.com/article/10.1007/s11548-021-02381-6},

year  = {2021},

date = {2021-01-01},

journal = {International Journal of Computer Assisted Radiology and Surgery},

volume = {16},

issue = {5},

pages = {861-869},

publisher = {Springer International Publishing},

abstract = {Purpose 

One in five women who undergo breast conserving surgery will need a second revision surgery due to remaining tumor. The iKnife is a mass spectrometry modality that produces real-time margin information based on the metabolite signatures in surgical smoke. Using this modality and real-time tissue classification, surgeons could remove all cancerous tissue during the initial surgery, improving many facets of patient outcomes. An obstacle in developing a iKnife breast cancer recognition model is the destructive, time-consuming and sensitive nature of the data collection that limits the size of the datasets. 

Methods 

We address these challenges by first, building a self-supervised learning model from limited, weakly labeled data. By doing so, the model can learn to contextualize the general features of iKnife data from a more accessible cancer type. Second, the trained model can then be applied to a cancer …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2021c,

title = {Self-supervised learning for detection of breast cancer in surgical margins with limited data},

author = {Alice ML Santilli and Amoon Jamzad and Alireza Sedghi and Martin Kaufmann and Shaila Merchant and Jay Engel and Kathryn Logan and Julie Wallis and Natasja Janssen and Sonal Varmak and Gabor Fichtinger and John F Rudan and Parvin Mousavi},

url = {https://ieeexplore.ieee.org/abstract/document/9433829/},

year  = {2021},

date = {2021-01-01},

pages = {980-984},

publisher = {IEEE},

abstract = {Breast conserving surgery is a standard cancer treatment to resect breast tumors while preserving healthy tissue. The reoperation rate can be as high as 35% due to the difficulties associated with detection of remaining cancer in surgical margins. REIMS is a mass spectrometry method that can address this challenge through real-time measurement of molecular signature of tissue. However, the collection of breast spectra to train a cancer detection model is time consuming and large samples sizes are not practical. We propose an application of self-supervised learning to improve the performance of cancer detection at surgical margins using a limited number of labelled data samples. A deep model is trained for the intermediate task of capturing latent features of REIMS data without the use of cancer labels. The model compensates for the small data size by dividing the spectra into smaller patches and shuffling their …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2021e,

title = {Graph-based analysis of mass spectrometry data for tissue characterization with application in basal cell carcinoma surgery},

author = {Faranak Akbarifar and Amoon Jamzad and Alice Santilli and Martin Kauffman and Natasja Janssen and Laura Connolly and K Ren and Kaitlin Vanderbeck and Ami Wang and Doug Mckay and John Rudan and Gabor Fichtinger and Parvin Mousavi},

url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11598/1159812/Graph-based-analysis-of-mass-spectrometry-data-for-tissue-characterization/10.1117/12.2582045.short},

year  = {2021},

date = {2021-01-01},

volume = {11598},

pages = {279-285},

publisher = {SPIE},

abstract = {PURPOSE 

Basal Cell Carcinoma (BCC) is the most common cancer in the world. Surgery is the standard treatment and margin assessment is used to evaluate the outcome. The presence of cancerous cells at the edge of resected tissue i.e., positive margin, can negatively impact patient outcomes and increase the probability of cancer recurrence. Novel mass spectrometry technologies paired with machine learning can provide surgeons with real-time feedback about margins to eliminate the need for resurgery. To our knowledge, this is the first study to report the performance of cancer detection using Graph Convolutional Networks (GCN) on mass spectrometry data from resected BCC samples. 

METHODS 

The dataset used in this study is a subset of an ongoing clinical data acquired by our group and annotated with the help of a trained pathologist. There is a total number of 190 spectra in this dataset, including 127 …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Janssen2020a,

title = {Navigated tissue characterization during skin cancer surgery},

author = {Natasja Janssen and Martin Kaufmann and Alice Santilli and Amoon Jamzad and Vanderbeck Kaitlin and Kevin Ren and Tamas Ungi and Parvin Mousavi and John Rudan and Doug McKay and Amy Wang and Gabor Fichtinger},

url = {https://doi.org/10.1007/s11548-020-02200-4},

doi = {10.1007/s11548-020-02200-4},

year  = {2020},

date = {2020-01-01},

journal = {Int J Comput Assist Radiol Surg},

abstract = {<p><strong>Purpose: </strong>Basal cell carcinoma (BCC) is the most commonly diagnosed skin cancer and is treated by surgical resection. Incomplete tumor removal requires surgical revision, leading to significant healthcare costs and impaired cosmesis. We investigated the clinical feasibility of a surgical navigation system for BCC surgery, based on molecular tissue characterization using rapid evaporative ionization mass spectrometry (REIMS).</p> 

<p><strong>Methods: </strong>REIMS enables direct tissue characterization by analysis of cell-specific molecules present within surgical smoke, produced during electrocautery tissue resection. A tissue characterization model was built by acquiring REIMS spectra of BCC, healthy skin and fat from ex vivo skin cancer specimens. This model was used for tissue characterization during navigated skin cancer surgery. Navigation was enabled by optical tracking and real-time visualization of the cautery relative to a contoured resection volume. The surgical smoke was aspirated into a mass spectrometer and directly analyzed with REIMS. Classified BCC was annotated at the real-time position of the cautery. Feasibility of the navigation system, and tissue classification accuracy for ex vivo and intraoperative surgery were evaluated.</p> 

<p><strong>Results: </strong>Fifty-four fresh excision specimens were used to build the ex vivo model of BCC, normal skin and fat, with 92% accuracy. While 3 surgeries were successfully navigated without breach of sterility, the intraoperative performance of the ex vivo model was low (< 50%). Hypotheses are: (1) the model was trained on heterogeneous mass spectra that did not originate from a single tissue type, (2) during surgery mixed tissue types were resected and thus presented to the model, and (3) the mass spectra were not validated by pathology.</p> 

<p><strong>Conclusion: </strong>REIMS-navigated skin cancer surgery has the potential to detect and localize remaining tumor intraoperatively. Future work will be focused on improving our model by using a precise pencil cautery tip for burning localized tissue types, and having pathology-validated mass spectra.</p>},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2020m,

title = {Assessment of skill translation of intrathecal needle insertion using real-time needle tracking with an augmented reality display},

author = {Saleh Choueib and Ciara McGarry and Melanie Jaeger and Tamas Ungi and Natasja Janssen and Gabor Fichtinger and Lindsey Patterson},

url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11315/113152A/Assessment-of-skill-translation-of-intrathecal-needle-insertion-using-real/10.1117/12.2549663.short},

year  = {2020},

date = {2020-01-01},

volume = {11315},

pages = {592-598},

publisher = {SPIE},

abstract = {PURPOSE 

Current lumbar puncture simulators lack visual feedback of the needle path. We propose a lumbar puncture simulator that introduces a visual virtual reality feedback to enhance the learning experience. This method incorporates virtual reality and a position tracking system. We aim to assess the advantages of the stereoscopy of virtual reality (VR) on needle insertion skills learning. 

METHODS 

We scanned and rendered spine models into three-dimensional (3D) virtual models to be used in the lumbar puncture simulator. The motion of the needle was tracked relative to the spine model in real-time using electromagnetic tracking, which allows accurate replay of the needle insertion path. Using 3D Slicer and SlicerVR, we created a virtual environment with the tracked needle and spine. In this study, 23 medical students performed a traditional lumbar puncture procedure using the augmented simulator. The …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2020t,

title = {91: Use of Electromagnetic Tracking Technology to Reconstruct Catheter Paths in Breast Brachytherapy-A Pilot Study},

author = {Harry Brastianos and Natasja Janssen and Aquila Akingbade and Tim Olding and Thomas Vaughan and Tamas Ungi and Andras Lasso and Mary Westerland and Chandra Joshi and Martin Korzeniowski and Gabor Fichtinger and Conrad Falkson},

url = {https://scholar.google.com/scholar?cluster=9660613250415496944&hl=en&oi=scholarr},

year  = {2020},

date = {2020-01-01},

journal = {Radiotherapy and Oncology},

volume = {150},

pages = {S41},

publisher = {Elsevier},

abstract = {Conclusions: The combined modality treatment factors and outcomes are comparable to the results of ASCENDE-RT and remain an effective treatment option for IR and HR prostate cancer. Higher GGG, HRF, PPC are potentially associated with worse outcomes. People who had early relapse had worse OS as demonstrated by ASCENDE-RT.},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2020u,

title = {159: Using A Simulation Model for Training Residents in High-Dose Interstitial Breast Brachytherapy: A Pilot Study},

author = {Harry Brastianos and Evan Lusty and Aquila Akingbade and Natasja Janssen and Tamas Ungi and Martin Korzeniowski and Catherine Metz and Gabor Fichtinger and Conrad Falkson},

url = {https://scholar.google.com/scholar?cluster=6789634058285951388&hl=en&oi=scholarr},

year  = {2020},

date = {2020-01-01},

journal = {Radiotherapy and Oncology},

volume = {150},

pages = {S68-S69},

publisher = {Elsevier},

abstract = {Results: Twenty-six (90%) National Societies completed the survey. One respondent perceived that the values of the training system of their country would be incompatible with the proposed ESTROCore Curriculum. The most common contextual barriers to implementation was a lack of support from the government (57%), a lack of internal organizational support (35%) and a ‘poor fit’between the ESTROCore Curriculum and the broader political & economic context (35%). Perceived implementation process barriers included insufficient numbers of faculty (44%), poor coordination between the government and training institutions (48%), and a lack of an influential person leading the implementation (44%). Two barriers related to curriculum change were a lack of funding and lack of assessment tools. 

Conclusions: The content and values espoused in the ESTRO Core Curriculum are endorsed across diverse …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2020v,

title = {75: Use of Three-Dimensional (3d) Surface Scanner to Define Treatment Volumes in Non-Melanoma Skin Cancer: A Pilot Study},

author = {Harry C Brastianos and Aquila Akingbade and Tamas Ungi and Natasja Janssen and Chandra Joshi and Gabor Fichtinger and Timothy Hanna},

url = {https://scholar.google.com/scholar?cluster=4582970843516641989&hl=en&oi=scholarr},

year  = {2020},

date = {2020-01-01},

journal = {Radiotherapy and Oncology},

volume = {150},

pages = {S35},

publisher = {Elsevier},

abstract = {Results: Mean DVH planning objective performance were as follows: PTV45 V95%(IMRT= 96.7%},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2020y,

title = {Real-Time Molecular Detection of Basal Cell Carcinoma with Rapid Evaporative Ionization Mass Spectrometry},

author = {Kaitlin Vanderbeck and Natasja Janssen and Martin Kaufmann and Alice Santilli and Kevin Ren and David Berman and Gabor Fichtinger and Parvin Mousavi and John Rudan and Douglas McKay and Ami Wang},

url = {https://scholar.google.com/scholar?cluster=15840746728170265189&hl=en&oi=scholarr},

year  = {2020},

date = {2020-01-01},

volume = {100},

issue = {SUPPL 1},

pages = {505-506},

publisher = {NATURE PUBLISHING GROUP},

keywords = {},

pubstate = {published},

tppubtype = {article}

}