Intraoperative spectroscopic evaluation of sentinel lymph nodes in breast cancer surgery

Background: Sentinel lymph node (SLN) biopsy is a standard procedure for patients with breast cancer. Positive SLNs on histological examination can lead to a second surgery for axillary lymph node clearance (ALNC). Here we report a non-destructive technique based on autofluorescence (AF) imaging and Raman spectroscopy for intra-operative assessment of SLNs excised in breast cancer surgery.Methods: A microscope integrating AF imaging and Raman spectroscopy modules was built to allow scanning of lymph node biopsy samples. AF imaging was utilised to determine optimal sampling locations for Raman spectroscopy measurements, such that scanning was completed within 20-30 minutes. After optimisation of the AF image analysis and training of classification models based on data from 85 samples, the AF-Raman technique was tested on an independent set of 81lymph nodes. Sensitivity and specificity were calculated using post-operative histology as a standard of reference.Results: The area under the receiver operating characteristic (ROC) curve for the AF-Raman analysis for bisected lymph nodes was 0.93. For a regime that maximised specificity (reduced risk of false positive detections), a 97% specificity and a 80% sensitivity was achieved. The main confounders for metastasis were areas rich in histiocytes, for which only few Raman spectra had been included in the training dataset.Conclusions: This preliminary study indicates that with further development and extension of the training dataset (inclusion of Raman spectra of histiocytes), the AF-Raman is a promising technique for intra-operative assessment of SLNs. Intra-operative detection of metastatic SLNs could greatly reduce additional surgery for axillary clearance

Enhanced Bayesian RFI Mitigation and Transient Flagging Using Likelihood Reweighting

Contamination by Radio Frequency Interference (RFI) is a ubiquitous challenge for radio astronomy. In particular, transient RFI is difficult to detect and avoid, especially in large data sets with many time bins. In this work, we present a Bayesian methodology for time-dependent, transient anomaly mitigation performed jointly with model fitting. The computation time for correcting for transient anomalies in this manner in time-separated data sets grows proportionally with the number of time bins. We demonstrate that utilising likelihood reweighting can allow our Bayesian anomaly mitigation method to be performed with a computation time close to independent of the number of time bins. In particular, we identify a factor of 44 improvement in computation time for a test case with 2000 time bins. We also demonstrate how this method enables the flagging threshold to be fit for as a free parameter, fully automating the mitigation process. We find that this threshold fitting also prevents overcorrecting of the data in the case of wide priors. Finally, we investigate the potential of the methodology as a transient detector. We demonstrate that the method is able to reliably flag an individual anomalous data point out of 302,000 provided the SNR ⩾ 10

Enhanced Bayesian RFI mitigation and transient flagging using likelihood reweighting

Acknowledgements: We would like to thank Will Handley for his work on the development of the original methodology. DA was supported by the Science and Technologies Facilities Council and SL was supported by the European Research Council and the UKRI.Funder: Science and Technology Facilities Council; doi: https://doi.org/10.13039/501100000271Funder: European Research Council; doi: https://doi.org/10.13039/100010663Funder: UKRI; doi: https://doi.org/10.13039/100014013Abstract Contamination by radio frequency interference (RFI) is a ubiquitous challenge for radio astronomy. In particular, transient RFI is difficult to detect and avoid, especially in large data sets with many time bins. In this work, we present a Bayesian methodology for time-dependent, transient anomaly mitigation performed jointly with model fitting. The computation time for correcting transient anomalies in this manner in time-separated data sets grows proportionally with the number of time bins. We demonstrate that utilizing likelihood reweighting can allow our Bayesian anomaly mitigation method to be performed with a computation time close to independent of the number of time bins. In particular, we identify a factor of 44 improvement in computation time for a test case with 2000 time bins. We also demonstrate how this method enables the flagging threshold to be fit as a free parameter, fully automating the mitigation process. We find that this threshold fitting also prevents overcorrecting of the data in the case of wide priors. Finally, we investigate the potential of the methodology as a transient detector. We demonstrate that the method is able to reliably flag an individual anomalous data point out of 302 000 provided the Signal to Noise Ratio is $\ge\!{10}$.</jats:p

Intraoperative spectroscopic evaluation of sentinel lymph nodes in breast cancer surgery

Background and objectives: Sentinel lymph node (SLN) biopsy is a standard procedure for patients with breast cancer and normal axilla on imaging. Positive SLNs on histological examination can lead to a subsequent surgery for axillary lymph node clearance (ALNC). Here we report a non-destructive technique based on autofluorescence (AF) imaging and Raman spectroscopy for intra-operative assessment of SLNs excised in breast cancer surgery.Methods: A microscope integrating AF imaging and Raman spectroscopy modules was built to allow scanning of lymph node biopsy samples. During AF-Raman measurements, AF imaging determined optimal sampling locations for Raman spectroscopy measurements. After optimisation of the AF image analysis and training of classification models based on data from 85 samples, the AF-Raman technique was tested on an independent set of 81 lymph nodes comprised of 58 fixed and 23 fresh specimens. The sensitivity and specificity of AF-Raman were calculated using post-operative histology as a standard of reference.Results: The independent set contained 66 negative lymph nodes and 15 positive lymph nodes according to the reference standard, collected from 78 patients recruited randomly. For this set of specimens, the area under the receiver operating characteristic (ROC) curve for the AF-Raman technique was 0.93 [0.83-0.98]. AF-Raman was then operated in a regime that maximised detection specificity, producing a 94% detection accuracy: 80% sensitivity and 97% specificity. The main confounders for SLN metastasis were areas rich in histiocytes clusters, for which only few Raman spectra had been included in the training dataset.Discussion: This preliminary study indicates that with further development and extension of the training dataset by inclusion of additional Raman spectra of histiocytes clusters and capsule, the AF-Raman may become a promising technique for intra-operative assessment of SLNs. Intra-operative detection of positive biopsies could avoid second surgery for axillary clearance