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Affiliation to IPPT PAN

1.Piotrzkowska-Wróblewska H., Dobruch-Sobczak K., Klimonda Z., Karwat P., Roszkowska-Purska K., Gumowska M., Litniewski J., Monitoring breast cancer response to neoadjuvant chemotherapy with ultrasound signal statistics and integrated backscatter , PLOS ONE, ISSN: 1932-6203, DOI: 10.1371/journal.pone.0213749, Vol.14, No.3, pp.1-15, 2019
Piotrzkowska-Wróblewska H., Dobruch-Sobczak K., Klimonda Z., Karwat P., Roszkowska-Purska K., Gumowska M., Litniewski J., Monitoring breast cancer response to neoadjuvant chemotherapy with ultrasound signal statistics and integrated backscatter , PLOS ONE, ISSN: 1932-6203, DOI: 10.1371/journal.pone.0213749, Vol.14, No.3, pp.1-15, 2019

Abstract:
Background Neoadjuvant chemotherapy (NAC) is used in patients with breast cancer to reduce tumor focus, metastatic risk, and patient mortality. Monitoring NAC effects is necessary to capture resistant patients and stop or change treatment. The existing methods for evaluating NAC results have some limitations. The aim of this study was to assess the tumor response at an early stage, after the first doses of the NAC, based on the variability of the backscattered ultrasound energy, and backscatter statistics. The backscatter statistics has not previously been used to monitor NAC effects. Methods The B-mode ultrasound images and raw radio frequency data from breast tumors were obtained using an ultrasound scanner before chemotherapy and 1 week after each NAC cycle. The study included twenty-four malignant breast cancers diagnosed in sixteen patients and qualified for neoadjuvant treatment before surgery. The shape parameter of the homodyned K distribution and integrated backscatter, along with the tumor size in the longest dimension, were determined based on ultrasound data and used as markers for NAC response. Cancer tumors were assigned to responding and non-responding groups, according to histopathological evaluation, which was a reference in assessing the utility of markers. Statistical analysis was performed to rate the ability of markers to predict the final NAC response based on data obtained after subsequent therapeutic doses. Results Statistically significant differences (p<0.05) between groups were obtained after 2, 3, 4, and 5 doses of NAC for quantitative ultrasound markers and after 5 doses for the assessment based on maximum tumor dimension. Statistical analysis showed that, after the second and third NAC courses the classification based on integrated backscatter marker was characterized by an AUC of 0.69 and 0.82, respectively. The introduction of the second quantitative marker describing the statistical properties of scattering increased the corresponding AUC values to 0.82 and 0.91. Conclusions Quantitative ultrasound information can characterize the tumor's pathological response better and at an earlier stage of therapy than the assessment of the reduction of its dimensions. The introduction of statistical parameters of ultrasonic backscatter to monitor the effects of chemotherapy can increase the effectiveness of monitoring and contribute to a better personalization of NAC therapy.

2.Byra M., Galperin M., Ojeda-Fournier H., Olson L., O Boyle M., Comstock C., Andre M., Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion, Medical Physics, ISSN: 0094-2405, DOI: 10.1002/mp.13361, Vol.46, No.2, pp.746-755, 2019
Byra M., Galperin M., Ojeda-Fournier H., Olson L., O Boyle M., Comstock C., Andre M., Breast mass classification in sonography with transfer learning using a deep convolutional neural network and color conversion, Medical Physics, ISSN: 0094-2405, DOI: 10.1002/mp.13361, Vol.46, No.2, pp.746-755, 2019

Abstract:
Purpose: We propose a deep learning-based approach to breast mass classification in sonography and compare it with the assessment of four experienced radiologists employing breast imaging reporting and data system 4th edition lexicon and assessment protocol. Methods: Several transfer learning techniques are employed to develop classifiers based on a set of 882 ultrasound images of breast masses. Additionally, we introduce the concept of a matching layer. The aim of this layer is to rescale pixel intensities of the grayscale ultrasound images and convert those images to red, green, blue (RGB) to more efficiently utilize the discriminative power of the convolutional neural network pretrained on the ImageNet dataset. We present how this conversion can be determined during fine-tuning using back-propagation. Next, we compare the performance of the transfer learning techniques with and without the color conversion. To show the usefulness of our approach, we additionallyevaluate it using two publiclyavailable datasets. Results: Color conversion increased the areas under the receiver operating curve for each transfer learning method. For the better-performing approach utilizing the fine-tuning and the matching layer, the area under the curve was equal to 0.936 on a test set of 150 cases. The areas under the curves for the radiologists reading the same set of cases ranged from 0.806 to 0.882. In the case of the two separate datasets, utilizing the proposed approach we achieved areas under the curve of around 0.890. Conclusions: The concept of the matching layer is generalizable and can be used to improve the overall performance of the transfer learning techniques using deep convolutional neural networks. When fully developed as a clinical tool, the methods proposed in this paper have the potential to help radiologists with breast mass classification in ultrasound. © 2018 American Association of Physicists in Medicine [https://doi.org/10.1002/mp.13361]

Keywords:
BI-RADS, breast mass classification, convolutional neural networks, transfer learning, ultrasound imaging

3.Gambin B., Kruglenko E., Tymkiewicz R., Litniewski J., Ultrasound assessment of the conversion of sound energy into heat in tissue phantoms enriched with magnetic micro- and nanoparticles, Medical Physics, ISSN: 0094-2405, DOI: 10.1002/mp.13742, pp.1-10, 2019
Gambin B., Kruglenko E., Tymkiewicz R., Litniewski J., Ultrasound assessment of the conversion of sound energy into heat in tissue phantoms enriched with magnetic micro- and nanoparticles, Medical Physics, ISSN: 0094-2405, DOI: 10.1002/mp.13742, pp.1-10, 2019

Abstract:
Purpose: Nowadays, the improvement of ultrasonic hyperthermia therapy is often achieved by adding hard particles to the sonicated medium in order to increase the heating efficiency. The explanation of the phenomenon of ultrasonic heating still requires testing on tissue mimicking materials (TMMs), enriched with particles of different sizes and physical properties. Our goal was to determine, by comparing their quantitative acoustic properties, which TMMs, with magnetic micro- or nanoparticles, convert more ultrasonic energy into heat or which of the particles embedded in the agar gel act as more effective thermal sonosensitizers. Methods: We manufactured a pure agar gel and an agar gel with the addition of magnetic micro- or nanoparticles in two proportions of 8 and 16 mg/ml. Ultrasound quantitative techniques, the broadband reflection substitution technique and backscattered spectrum analysis were used to characterize the samples by speed of sound (SOS), frequency-dependent attenuation, and backscattering coefficients. The integrated backscattering coefficients were also calculated. The quantitative parameters, scattering, and attenuation coefficients of ultrasound in phantoms with micro- and nanoparticles were estimated. Based on the attenuation and scattering of ultrasound in the samples, the ultrasonic energy absorption, which determines the heating efficiency, was evaluated. Additionally, the temperature increase during sonication of the phantoms by an ultrasonic beam was directly measured using thermocouples. Results: The density of the materials with nanoparticles was higher than for the materials with microparticles with the same fractions of particles. The SOS for all materials ranged from 1489 to 1499 m/s. The attenuation in the whole frequency range (3–8 MHz) was higher for the materials with nanoparticles than for the materials with microparticles. For the materials with the lower content (8 mg/ml) of particles, the attenuation coefficient was 0.2 dB/(MHz cm). For the 16 mg/ml concentration of nanoparticles and microparticles, the attenuation coefficients were 0.66 and 0.45 dB/(MHz cm), resectively. The value of backscattering coefficient in the whole frequency range was greater for the materials with microparticles than for the materials with nanoparticles. The values of the integrated backscattering coefficient were 0.05 and 0.08 1/m for the materials with nanoparticles and 0.46 and 0.82 1/m for the materials with microparticles and concentrations of 8 and 16 mg/ml, respectively. The rates of temperature increase in the first 3 s due to ultrasonic heating were higher for the materials with nanoparticles than for the materials with microparticles. Conclusions: Based on acoustical measurements, we confirmed that all materials can be used as tissue phantoms in the study of ultrasonic hyperthermia, as their properties were in the range of soft tissue properties. We found that the nanoparticle-doped materials had greater attenuation and smaller scattering of ultrasound than the materials with microparticles, so absorption in these materials is greater. Thus, the TMMs with nanoparticles convert more acoustic energy into heat and we conclude that magnetic nanoparticles are more effective thermal sonosensitizers than microparticles. This conclusion is confirmed by direct measurement of the temperature increase in the samples subjected to sonification.

Keywords:
backscattering coefficient, frequency-dependent attenuation, hyperthermia TMM, magnetic particles, ultrasound absorption

4.Klimonda Z., Karwat P., Dobruch-Sobczak K., Piotrzkowska-Wróblewska H., Litniewski J., Breast-lesions characterization using Quantitative Ultrasound features of peritumoral tissue, Scientific Reports, ISSN: 2045-2322, DOI: 10.1038/s41598-019-44376-z, Vol.9, No.7963, pp.1-9, 2019
Klimonda Z., Karwat P., Dobruch-Sobczak K., Piotrzkowska-Wróblewska H., Litniewski J., Breast-lesions characterization using Quantitative Ultrasound features of peritumoral tissue, Scientific Reports, ISSN: 2045-2322, DOI: 10.1038/s41598-019-44376-z, Vol.9, No.7963, pp.1-9, 2019

Abstract:
The presented studies evaluate for the first time the efficiency of tumour classification based on the quantitative analysis of ultrasound data originating from the tissue surrounding the tumour. 116 patients took part in the study after qualifying for biopsy due to suspicious breast changes. The RF signals collected from the tumour and tumour-surroundings were processed to determine quantitative measures consisting of Nakagami distribution shape parameter, entropy, and texture parameters. The utility of parameters for the classification of benign and malignant lesions was assessed in relation to the results of histopathology. The best multi-parametric classifier reached an AUC of 0.92 and of 0.83 for outer and intra-tumour data, respectively. A classifier composed of two types of parameters, parameters based on signals scattered in the tumour and in the surrounding tissue, allowed the classification of breast changes with sensitivity of 93%, specificity of 88%, and AUC of 0.94. Among the 4095 multi-parameter classifiers tested, only in eight cases the result of classification based on data from the surrounding tumour tissue was worse than when using tumour data. The presented results indicate the high usefulness of QUS analysis of echoes from the tissue surrounding the tumour in the classification of breast lesions.

5.Byra M., Wu M., Zhang X., Jang H., Ma Y-J., Chang E.Y., Shah S., Du J., Knee menisci segmentation and relaxometry of 3D ultrashort echo time cones MR imaging using attention U‐Net with transfer learning, Magnetic Resonance in Medicine, ISSN: 1522-2594, DOI: 10.1002/mrm.27969, pp.1-14, 2019
Byra M., Wu M., Zhang X., Jang H., Ma Y-J., Chang E.Y., Shah S., Du J., Knee menisci segmentation and relaxometry of 3D ultrashort echo time cones MR imaging using attention U‐Net with transfer learning, Magnetic Resonance in Medicine, ISSN: 1522-2594, DOI: 10.1002/mrm.27969, pp.1-14, 2019

Abstract:
Jiang Du, Department of Radiology, University of California, San Diego, CA 92103‐8226. Email: jiangdu@ucsd.edu Funding information The authors acknowledge grant support from GE Healthcare, NIH (1R01 AR062581, 1R01 AR068987 and 1R01 NS092650), and the VA Clinical Science Research & Development Service (1I01CX001388, I21RX002367). Purpose: To develop a deep learning‐based method for knee menisci segmentation in 3D ultrashort echo time (UTE) cones MR imaging, and to automatically determine MR relaxation times, namely the T1, T1ρ, and T∗ 2 parameters, which can be used to assess knee osteoarthritis (OA). Methods: Whole knee joint imaging was performed using 3D UTE cones sequences to collect data from 61 human subjects. Regions of interest (ROIs) were outlined by 2 experienced radiologists based on subtracted T1ρ‐weighted MR images. Transfer learning was applied to develop 2D attention U‐Net convolutional neural networks for the menisci segmentation based on each radiologist’s ROIs separately. Dice scores were calculated to assess segmentation performance. Next, the T1, T1ρ, T∗ 2 relaxations, and ROI areas were determined for the manual and automatic segmentations, then compared. Results: The models developed using ROIs provided by 2 radiologists achieved high Dice scores of 0.860 and 0.833, while the radiologists’ manual segmentations achieved a Dice score of 0.820. Linear correlation coefficients for the T1, T1ρ, and T∗ 2 relaxations calculated using the automatic and manual segmentations ranged between 0.90 and 0.97, and there were no associated differences between the estimated average meniscal relaxation parameters. The deep learning models achieved segmentation performance equivalent to the inter‐observer variability of 2 radiologists. Conclusion: The proposed deep learning‐based approach can be used to efficiently generate automatic segmentations and determine meniscal relaxations times. The method has the potential to help radiologists with the assessment of meniscal diseases, such as OA.

Keywords:
deep learning, menisci, osteoarthritis, quantitative MR, segmentation

6.Byra M., Wan L., Wong J.H., Du J., Shah SB., Andre M.P., Chang E.Y., Quantitative ultrasound and b-mode image texture featurescorrelate with collagen and myelin content in human ulnarnerve fascicles, ULTRASOUND IN MEDICINE AND BIOLOGY, ISSN: 0301-5629, DOI: 10.1016/j.ultrasmedbio.2019.02.019, Vol.45, No.7, pp.1830-1840, 2019
Byra M., Wan L., Wong J.H., Du J., Shah SB., Andre M.P., Chang E.Y., Quantitative ultrasound and b-mode image texture featurescorrelate with collagen and myelin content in human ulnarnerve fascicles, ULTRASOUND IN MEDICINE AND BIOLOGY, ISSN: 0301-5629, DOI: 10.1016/j.ultrasmedbio.2019.02.019, Vol.45, No.7, pp.1830-1840, 2019

Abstract:
We investigate the usefulness of quantitative ultrasound and B-mode texture features for characterization of ulnar nerve fascicles. Ultrasound data were acquired from cadaveric specimens using a nominal 30-MHz probe. Next, the nerves were extracted to prepare histology sections. Eighty-five fascicles were matched between the B-mode images and the histology sections. For each fascicle image, we selected an intra-fascicular region of interest. We used histology sections to determine features related to the concentration of collagen and myelin and ultrasound data to calculate the backscatter coefficient (–24.89 ± 8.31 dB), attenuation coefficient (0.92 ± 0.04 db/cm-MHz), Nakagami parameter (1.01 ± 0.18) and entropy (6.92 ± 0.83), as well as B-mode texture features obtained via the gray-level co-occurrence matrix algorithm. Significant Spearman rank correlations between the combined collagen and myelin concentrations were obtained for the backscatter coefficient (R = –0.68), entropy (R = –0.51) and several texture features. Our study indicates that quantitative ultrasound may potentially provide information on structural components of nerve fascicles.

Keywords:
NerveQuantitative ultrasoundHigh frequencyHistologyPattern recognitionTexture analysis

7.Dobruch-Sobczak K., Piotrzkowska-Wróblewska H., Klimonda Z., Roszkowska-Purska K., Litniewski J., Ultrasound echogenicity reveals the response of breast cancer to chemotherapy, Clinical Imaging , ISSN: 0899-7071, DOI: 10.1016/j.clinimag.2019.01.021, Vol.55, pp.41-46, 2019
Dobruch-Sobczak K., Piotrzkowska-Wróblewska H., Klimonda Z., Roszkowska-Purska K., Litniewski J., Ultrasound echogenicity reveals the response of breast cancer to chemotherapy, Clinical Imaging , ISSN: 0899-7071, DOI: 10.1016/j.clinimag.2019.01.021, Vol.55, pp.41-46, 2019

Abstract:
Purpose: To evaluate the ultrasound (US) response in patients with breast cancer (BC) during neoadjuvant chemotherapy (NAC). Methods: Prospective US analysis was performed on 19 malignant tumors prior to NAC treatment and 7days after each first four courses of NAC in 13 patients (median age=57years). Echogenicity, size, vascularity, and sonoelastography were measured and compared with posttreatment scores of residual cancers burden. Results: Changes in the echogenicity of tumors after 3 courses of NAC had the most statistically strong correlation with the percentage of residual malignant cells used in histopathology to assess the response to treatment (odds ratio=60, p < 0.05). Changes in lesion size and elasticity were also significant (p < 0.05). Conclusions: There is a statistically significant relationship between breast tumors' echogenicity in US, neoplasm size, and stiffness and the response to NAC. In particular, our results show that the change in tumor echogenicity could predict a pathological response with satisfactory accuracy and may be considered in NAC monitoring.

Keywords:
Breast ultrasonography, Neoadjuvant chemotherapy, Clinical response, Breast cancer, Sonoelastography

8.Fura Ł., Kujawska T., Selection of Exposure Parameters for a HIFU Ablation System Using an Array of Thermocouples and Numerical Simulations, ARCHIVES OF ACOUSTICS, ISSN: 0137-5075, DOI: 10.24425/aoa.2019.128498, Vol.44, No.2, pp.349-355, 2019
Fura Ł., Kujawska T., Selection of Exposure Parameters for a HIFU Ablation System Using an Array of Thermocouples and Numerical Simulations, ARCHIVES OF ACOUSTICS, ISSN: 0137-5075, DOI: 10.24425/aoa.2019.128498, Vol.44, No.2, pp.349-355, 2019

Abstract:
Image-guided High Intensity Focused Ultrasound (HIFU) technique is dynamically developing technology for treating solid tumors due to its non-invasive nature. Before a HIFU ablation system is ready for use, the exposure parameters of the HIFU beam capable of destroying the treated tissue without damaging the surrounding tissues should be selected to ensure the safety of therapy. The purpose of this work was to select the threshold acoustic power as well as the step and rate of movement of the HIFU beam, generated by a transducer intended to be used in the HIFU ablation system being developed, by using an array of thermocouples and numerical simulations. For experiments a bowl-shaped 64-mm, 1.05 MHz HIFU transducer with a 62.6 mm focal length (f-number 0.98) generated pulsed waves propagating in two-layer media: water/ex vivo pork loin tissue (50 mm/40 mm) was used. To determine a threshold power of the HIFU beam capable of creating the necrotic lesion in a small volume within the tested tissue during less than 3 s each tissue sample was sonicated by multiple parallel HIFU beams of different acoustic power focused at a depth of 12.6 mm below the tissue surface. Location of the maximum heating as well as the relaxation time of the tested tissue were determined from temperature variations recorded during and after sonication by five thermo-couples placed along the acoustic axis of each HIFU beam as well as from numerical simulations. The obtained results enabled to assess the location of each necrotic lesion as well as to determine the step and rate of the HIFU beam movement. The location and extent of the necrotic lesions created was verified using ultrasound images of tissue after sonication and visual inspection after cutting the samples. The threshold acoustic power of the HIFU beam capable of creating the local necrotic lesion in the tested tissue within 3 s without damaging of surrounding tissues was found to be 24 W, and the pause between sonications was found to be more than 40 s.

Keywords:
automated HIFU ablation system; threshold acoustic power of HIFU beam; ex vivo tissue; necrotic lesion; thermocouple array

9.Dobruch-Sobczak K., Piotrzkowska-Wróblewska H., Klimoda Z., Secomski W., Karwat P., Markiewicz-Grodzicka E., Kolasińska-Ćwikła A., Roszkowska-Purska K., Litniewski J., Monitoring the response to neoadjuvant chemotherapy in patients with breast cancer using ultrasound scattering coefficient: A preliminary report , Journal of Ultrasonography, ISSN: 2084-8404, DOI: 10.15557/JoU.2019.0013, Vol.19, No.77, pp.89-97, 2019
Dobruch-Sobczak K., Piotrzkowska-Wróblewska H., Klimoda Z., Secomski W., Karwat P., Markiewicz-Grodzicka E., Kolasińska-Ćwikła A., Roszkowska-Purska K., Litniewski J., Monitoring the response to neoadjuvant chemotherapy in patients with breast cancer using ultrasound scattering coefficient: A preliminary report , Journal of Ultrasonography, ISSN: 2084-8404, DOI: 10.15557/JoU.2019.0013, Vol.19, No.77, pp.89-97, 2019

Abstract:
Objective: Neoadjuvant chemotherapy was initially used in locally advanced breast cancer, and currently it is recommended for patients with Stage 3 and with early-stage disease with human epidermal growth factor receptors positive or triple-negative breast cancer. Ultrasound imaging in combination with a quantitative ultrasound method is a novel diagnostic approach. Aim of study: The aim of this study was to analyze the variability of the integrated backscatter coefficient, and to evaluate their use to predict the effectiveness of treatment and compare to ultrasound examination results. Material and method: Ten patients (mean age 52.9) with 13 breast tumors (mean dimension 41 mm) were selected for neoadjuvant chemotherapy. Ultrasound was performed before the treatment and one week after each course of neoadjuvant chemotherapy. The dimensions were assessed adopting the RECIST criteria. Tissue responses were classified as pathological response into the following categories: not responded to the treatment (G1, cell reduction by ≤9%) and responded to the treatment partially: G2, G3, G4, cell reduction by 10–29% (G2), 30–90% (G3), >90% (G4), respectively, and completely. Results: In B-mode examination partial response was observed in 9/13 cases (completely, G1, G3, G4), and stable disease was demonstrated in 3/13 cases (completely, G1, G4). Complete response was found in 1/13 cases. As for backscatter coefficient, 10/13 tumors (completely, and G2, G3, and G4) were characterized by an increased mean value of 153%. Three tumors 3/13 (G1) displayed a decreased mean value of 31%. Conclusion: The variability of backscatter coefficient, could be associated with alterations in the structure of the tumor tissue during neoadjuvant chemotherapy. There were unequivocal differences between responded and non-responded patients. The backscatter coefficient analysis correlated better with the results of histopathological verification than with the B-mode RECIST criteria.

Keywords:
integrated backscatter coefficient (IBSCs), neoadjuvant chemotherapy (NAC), breast cancer, ultrasound