10:30
Cancer
Chair: Omer Can Akgun
10:30
15 mins
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Assessment of Fresh Breast Tumor Tissue Using HHG Microscopy
Laura van Huizen, Nikolay Kuzmin, Ellis Barbé, Susanne van der Velde, Elisabeth te Velde, Marie Louise Groot
Abstract: Background For a patient with breast cancer, fast diagnosis and precise excision of breast tumor tissue is important. The gold standard to assess excised breast tissue is histopathology, which takes at least 16 hours. Alternative techniques are therefore required that can assess breast tissue with a speed that enables ‘live’ feedback to the surgeon while she/he operates.
HHG Microscopy A technique that meets these requirements is higher harmonic generation microscopy (HHGM), a novel imaging technique, which is non-invasive, label-free and provides 3D images with a high, sub-cellular resolution, within seconds [1]. HHGM reveals tissue contrast provided by interfaces and noncentrosymmetric molecular structures via generation of second and third optical harmonics (SHG/THG).
Results 3D images of freshly excised and unprepared human healthy and tumor breast tissue showed that the combination of THG and SHG microscopy revealed the key breast components – lobules, ducts, fat tissue, connective tissue, blood vessels, and peripheral nerves. All THG/SHG images were in good agreement with H&E histology. Furthermore, the experiments showed that HHGM is able to reveal most of the pathological breast features. Thus, HHGM has a high added value for fast label-free assessment of fresh breast tumor tissue. Possible applications are intra-operative assessment and in-situ analysis, which makes HHGM a promising technique.
[1] N.V. Kuzmin, et al. Biomed. Opt. Exp., 7, 1889–1904 (2016)
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10:45
15 mins
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3D Strain Estimation for Improved Breast Cancer Detection in Automated Volumetric Breast Ultrasound Scanners
Gijs Hendriks, Chuan Chen, Hendrik Hansen, Chris de Korte
Abstract: Background:
Mammography is the gold standard in breast cancer detection but has reduced sensitivity in women with dense breast (i.e. an increased glandular-to-fat tissue ratio). The automated breast volume scanner (ABVS) was introduced as ultrasound alternative for mammography. In ABVS scanning, a linear ultrasound transducer (153 mm, 14L5BV) translates over the breast while collecting ultrasound data to reconstruct a 3-D volume of the breast. Studies report high sensitivity of ABVS in breast screening, however, the specificity is limited. Quasi-static elastography might be a solution to increase the specificity since malignant lesion are often stiffer compared to benign lesions.
Aim:
The aim of this study was to implement 3-D quasi-static elastography in ABVS and to verify feasibility in a patient study.
Methods:
Female patients who were scheduled for a breast exam in our hospital (Radboudumc Nijmegen) were recruited and signed an informed consent. Next to their regular breast exam, two additional breast ultrasound RF-data volumes were acquired using the ABVS with lifting the transducer (1 mm) in between scans. Displacements were calculated using coarse-to-fine cross-correlation of the acquired volumes. The 3-D strain tensor was derived from the displacements using a least-squares strain estimator. The principal strains were calculated by the eigenvalues of the tensor, because breathing was allowed between the two volumetric scans (2x15 seconds) and consequently the main strain component was not necessarily in the axial direction. Next, the principal strain component with largest absolute value (ε(principal1)) was normalized by the median of ε(principal1) in the subcutaneous fat divided by ε(prinicipal1) values. Finally, the median normalized strain (ε(norm)) was calculated in annotated lesions or in random areas if no lesions were detected.
Results:
29 patients participated in which 1 invasive ductal carcinoma (IDC), 2 ductal carcinoma in situ (DCIS), 1 other malignant lesion (OM), 3 fibro-adenoma (FA), 7 cysts and 4 other benign lesions (OB) were detected. The ε(norm) values were 3.5 (IDC); 0.9 and 2.1 (DCIS), 1.3 (OM), 0.8±0.5 (FA) and 1.3±0.6 (OB) in median ± iqr. In 11 subjects without lesion, ε(norm) (1.3±0.8) were found close to 1 as expected for healthy tissue. IDC and one DCIS showed increased ε(norm) compared to benign lesions (FA, Cysts, OB). DCIS lesions (especially low-grade) are often considered as pre-cancerous, can be less stiff compared to IDC which may explain the strain ratio within the range for benign tissues and lesions for the other DCIS lesion. Strains in cysts were not calculated given their hypoechoic properties.
Conclusion:
3-D quasi-static elastography was successfully developed for ABVS. Initial in-vivo results showed that IDC can be discriminated from benign lesions.
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11:00
15 mins
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Prediction of Prostate Cancer Biopsy Outcomes Based on Dynamic 3D Contrast-Enhanced Ultrasound Quantification
Rogier R Wildeboer, Ruud JG van Sloun, Pintong Huang, Hessel Wijkstra, Massimo Mischi
Abstract: Introduction ♦ Today, non-targeted ≥10-core systematic biopsy (SBx) using transrectal ultrasound (TRUS) is the guideline-recommended clinical pathway for prostate cancer (PCa) diagnosis. However, this technique is associated with a risk of underdiagnosis and overtreatment as well as the occurrence of complications. Three-dimensional (3D) dynamic contrast-enhanced ultrasound (DCE-US) recordings exploit specific imaging of contrast agents to visualize and characterize (micro)vascularity. Quantification algorithms for DCE-US of the prostate have shown good potential for PCa localization in 2D. With the introduction of 3D DCE-US, a few have recently been expanded to three dimensions.
Materials and Methods ♦ We present the 3D implementation of a large range of estimators comprising contrast ultrasound dispersion imaging (CUDI) and assess their performance to discriminate prostate regions with and without PCa. Furthermore, we utilize combinations of the extracted parameters to predict individual SBx-core biopsy pathology. To this end, we apply a discriminative and a generative machine-learning approach, that is, a support vector machine (SVM) and a Gaussian Mixture Model (GMM), to a dataset of 43 2-minute 3D DCE-US recordings acquired at the Second Affiliated Hospital of Zhejiang University in Hangzhou, China. The machine-learning algorithms were trained and tested in a leave-one-prostate-out fashion.
Results ♦ Individually, the estimators show good correlation with the presence of prostate cancer in SBx-regions. Best performing for PCa and significant PCa (sPCa) are convective velocity (ROC curve area PCa = 0.71; sPCa = 0.80), wash-in time (PCa = 0.71; sPCa = 0.78) and mean transit time (PCa = 0.69; sPCa = 0.79). The best machine-learning approach is the GMM, showing a ROC curve area of 0.76 and 0.81 for PCa and sPCa, respectively.
Discussion and Conclusions ♦ 3D quantification of DCE-US for the characterization of prostate tissue yields promising results. Moreover, we demonstrated that machine-learning approaches can improve the classification performance compared to individual DCE-US parameters. Although this study is limited by the relatively small number of patients, these results show potential for further development in ultrasound-based PCa localization.
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11:15
15 mins
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Fully Automatic Construction of Optimal Radiomics Workflows
Martijn Starmans, Sebastian van der Voort, Razvan Miclea, Melissa Vos, Fatih Incekara, Milea Timbergen, Maarten Wijnenga, Guillaume Padmos, Wouter Kessels, Arno van Leenders, Martin van den Bent, Arnaud Vincent, Dirk Grünhagen, Cornelis Verhoef, Stefan Sleijfer, Jacob Visser, Marion Smits, Maarten Thomeer, Wiro Niessen, Stefan Klein
Abstract: In radiomics, we aim to determine relations between a combination of imaging features and clinically relevant data or outcomes. Many radiomics applications and methods have been described in literature. However, there is no method that works for all applications. We present a Workflow for Optimal Radiomics Classification (WORC1), an open source solution to fully automatically construct an optimal workflow per application.
WORC states radiomics as a modular workflow. Various algorithms may be used in each module in order to complete the same predefined task. The complete radiomics workflow is treated as a Combined Algorithm Selection and Hyperparameter optimization problem (CASH). A collection of commonly used oversampling, feature selection and machine learning algorithms is combined in CASH. During training, WORC automatically adapts itself by testing thousands of pseudo-randomly defined radiomics workflows. The best workflows are combined into a single optimal signature. To evaluate WORC, three experiments on different clinical applications were performed: (1) classification of 119 patients with primary liver tumors in benign or malignant on T2-weighted Magnetic Resonance Images (MRIs) (2) prediction of 1p/19q co-deletion in 287 patients with presumed low grade gliomas on T1- and T2-weighted MRIs and (3) distinguishing liposarcomas from lipomas in 88 patients on T1-weighted MRIs. Ground truth was obtained through pathology. Evaluation is implemented in WORC through a 100x random-split cross-validation, with 80% of the data used for training and 20% for independent testing. Performance is given in 95% corrected2 confidence intervals (CIs).
The CIs of the area under the curve, sensitivity and specificity were [0.86, 0.99], [0.58, 0.89] and [0.85, 0.98] for liver tumors, [0.74, 0.85], [0.58, 0.76] and [0.72, 0.86] for brain tumors and [0.74, 0.93], [0.59, 0.86] and [0.67, 0.92] for lipomas/liposarcomas.
Hence, using the modular design and automatic adaptation of WORC, we obtained good results for three completely different applications while using the exact same configuration. Therefore, WORC is a promising approach for fully automatic construction of optimal radiomics workflows.
1. Starmans, M. P. A., ``Workflow for Optimal Radiomics Classification (WORC)’’, https://github.com/MStarmans91/WORC, Aug. 2018
2. Nadeau, C. and Bengio, Y., ``Inference for the generalization error’’, Advances in neural information processing systems, 307-313, 2000
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11:30
15 mins
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Lab-on-Chip Device for High-Throughput Multi-Analysis Single-Cell Studies
Federico Bedini, Virgilio Valente
Abstract: Lab-on-chip (LoC) devices embody several key features, including the use of small sample volume, control over fluid dynamics, easy integration of cell manipulation techniques (cell sorting, cell isolation) and higher throughput than conventional analytical methods (e.g. patch clamp). Impedance flow cytometry (IFC) has been widely adopted for high-throughput cell detection and manipulation, sorting and counting. IFC works by measuring the impedance between a set of electrodes as single cells pass through a microchannel. IFC, however, allows only for short-term single-cell analysis. Microelectrode arrays (MEAs) are often used to perform long-term analysis of cell populations. Coupled with electrical impedance spectroscopy (EIS), adhesion, morphology, proliferation and temporal evolution of cells can be analysed.
In this work, a novel LoC device is proposed, which integrates in-channel IFC and in-chamber MEAs in the same microfluidic platform. The microfluidic chip is fabricated on standard Si wafers through a CMOS-compatible photolithographic process, allowing for future microelectronics integration. Several versions of the chip are realized on the same Si wafer, diced and singularly tested to evaluate the effect of the geometrical parameters and channel configuration on the detection sensitivity. The first design includes IFC electrodes of different sizes at the bottom of a 10x10 μm2 microchannel, and a culture chamber comprising of 9 microwells with interdigitated electrodes. Different IDE parameters (width from 10 μm to 100 μm, gap from 5 μm to 20 μm) are also realized to assess optimum geometries for high sensitivity and SNR.
The IFC and MEA electrodes are characterised using a multichannel impedance analyser. The performance of the microfluidic device is tested in a fluidic system with microbeads of different sizes (4-6-8 μm).
Integration of IFC and MEAs in the same platform enables automated, high-throughput single-cell sorting and manipulation and long-term network analysis, allowing for more comprehensive studies of cell cultures.
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11:45
15 mins
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Design and Custom Fabrication of a Smart Temperature Sensor for an Organ-on-a-Chip Platform
Ronaldo Ponte, Vasiliki Giagka, Wouter Serdijn
Abstract: Incubators in cell cultures are used to grow and maintain cells under optimal temperature alongside other key variables, such as pH, humidity, atmospheric conditions etc. As enzymatic activity and protein synthesis proceed optimally at 37.5 oC, a temperature rise can cause protein denaturation, whereas a drop in temperature can slow down catalysis and polypeptide initiation [1].
Inside the incubator, the measurements are gauged according to the temperature of the heating element, which is not exactly the same as that of the cells. Time spent outside the incubator can greatly impact cell health. In fact, out-of-incubator temperature and its change over time are unknown variables to clinicians and researchers, while a considerable number of cell culture losses are attributed to this reason.
To accurately monitor the temperature of the culture throughout cell growth, an in situ temperature sensor with at least ±0.5 oC of resolution is of paramount importance. This allows the growth of the cultured cells to be optimized.
This work reports on the design and fabrication of a time-mode signal-processing in situ temperature sensor customized for an organ-on-a-chip (OOC) application. The circuit was fabricated using an in-house integrated circuit technology that requires only 7 lithographic steps and is compatible with MEMS fabrication process. The proposed circuit is developed to provide the first out-of-incubator temperature monitoring of cell cultures on an OOC platform in a monolithic fabrication. Measurement results on wafer reveal a temperature measurement resolution of less than ±0.2 oC (3σ) and a maximum nonlinearity error of less than 0.3% across a temperature range from 25 oC to 100 oC.
To the authors’ best knowledge, no in situ temperature-sensing fully integrated on an OOC platform exists to date. This is the first time such integration is being performed using a custom-designed circuit fabricated on the same silicon substrate as that of the OOC. The simple, robust, and custom IC technology used for the sensor fabrication grants a very cost-effective integrated solution in virtue of the reduced cost per wafer along with the large silicon area available on the platform [2]. Moreover, no further complicated assembly and subsequent protection of the pre-fabricated components is required. This minimizes the extra processing steps, along with the related handling risks, leading to higher yields. Finally, the freedom enjoyed by the MEMS-electronics co-design offers a large degree of versatility to accomodate electronics in a range of different OOC shapes and structures.
REFERENCES
[1] T. Neutelings, C. A. Lambert, B. V. Nusgens, and A. C. Colige. Effects of mild cold shock (25 oC) followed by warming up at 37 oC on the cellular stress response. PLOS ONE, 2013.
[2] H. van Zeijl and L. Nanver. A Low-cost BiCMOS Process with Metal Gates. MRS Proceedings, 2000.
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