@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 …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{fichtinger2023i,

title = {Attention-based multi-instance learning for improved glioblastoma detection using mass spectrometry},

author = {Dilakshan Srikanthan and Martin Kaufmann and Amoon Jamzad and Ayesha Syeda and Alice Santilli and Alireza Sedghi and Gabor Fichtinger and Jamie Purzner and John Rudan and Teresa Purzner and Parvin Mousavi},

url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/12466/1246611/Attention-based-multi-instance-learning-for-improved-glioblastoma-detection-using/10.1117/12.2654436.short},

year  = {2023},

date = {2023-01-01},

volume = {12466},

pages = {248-253},

publisher = {SPIE},

abstract = {Glioblastoma Multiforme (GBM) is the most common and most lethal primary brain tumor in adults with a five-year survival rate of 5%. The current standard of care and survival rate have remained largely unchanged due to the degree of difficulty in surgically removing these tumors, which plays a crucial role in survival, as better surgical resection leads to longer survival times. Thus, novel technologies need to be identified to improve resection accuracy. Our study features a curated database of GBM and normal brain tissue specimens, which we used to train and validate a multi-instance learning model for GBM detection via rapid evaporative ionization mass spectrometry. This method enables real-time tissue typing. The specimens were collected by a surgeon, reviewed by a pathologist, and sampled with an electrocautery device. The dataset comprised 276 normal tissue burns and 321 GBM tissue burns. Our multi …},

keywords = {},

pubstate = {published},

tppubtype = {article}

}

@article{Fooladgar2022,

title = {Uncertainty estimation for margin detection in cancer surgery using mass spectrometry},

author = {Fahimeh Fooladgar and Amoon Jamzad and Laura Connolly and Alice Santilli and Martin Kaufmann and Kevin Ren and Purang Abolmaesumi and John Rudan and Doug McKay and Gabor Fichtinger and Parvin Mousavi},

doi = {https://doi.org/10.1007/s11548-022-02764-3},

year  = {2022},

date = {2022-09-01},

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

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{fichtinger2021h,

title = {Graph transformers for characterization and interpretation of surgical margins},

author = {Amoon Jamzad and Alice Santilli and Faranak Akbarifar and Martin Kaufmann and Kathryn Logan and Julie Wallis and Kevin Ren and Shaila Merchant and Jay Engel and Sonal Varma and Gabor Fichtinger and John Rudan and Parvin Mousavi},

url = {https://link.springer.com/chapter/10.1007/978-3-030-87234-2_9},

year  = {2021},

date = {2021-01-01},

pages = {88-97},

publisher = {Springer International Publishing},

abstract = {PURPOSE: Deployment of deep models for clinical decision making should not only provide predicted outcomes, but also insights on how decisions are made. Considering the interpretability of Transformer models, and the power of graph networks in analyzing the inherent hierarchy of biological signals, a combined approach would be the next generation solution in computer aided interventions. In this study, we propose a framework for classification and visualization of surgical mass spectrometry data using Graph Transformer model to empower the interpretability of breast surgical margin assessment. METHODS: Using the iKnife, 144 burns (103 normal, 41 cancer) were collected and converted to multi-level graph structures. A Graph Transformer model was modified to output the intermediate attention parameters of the network. Beside ablation and prospective study, we propose multiple attention …},

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{fichtinger2020n,

title = {Open source software platform for interstitial ablation treatment planning},

author = {Alice Santilli and Csaba Pinter and Bote Jiang and Gernot Kronreif and Gabor Fichtinger},

url = {https://www.spiedigitallibrary.org/conference-proceedings-of-spie/11315/1131526/Open-source-software-platform-for-interstitial-ablation-treatment-planning/10.1117/12.2549577.short},

year  = {2020},

date = {2020-01-01},

volume = {11315},

pages = {566-571},

publisher = {SPIE},

abstract = {PURPOSE 

There are several interstitial (needle based) image-guided ablation planning systems available, but most of them are closed or unsupported. We propose an open source software platform for the planning of image-guided interstitial ablation procedures, providing generic functionality and support for specialized plug-ins. 

METHODS 

The patient’s image data is loaded or streamed into the system and the relevant structures are segmented. The user places fiducial points as ablation needle entries and tips, sets the ablation times, and the thermal dose is calculated by a dose engine. The thermal dose is then visualized on the 2D image slices and 3D rendering using a combination of isodose lines and surfaces. Quantitative feedback is provided by dose volume histograms. The treatment plan can be iteratively edited until satisfactory dose distribution is achieved. We performed a usability study with eight …},

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}

}