Quantitative Ultrasound Methods Research Articles

Computer aided prognosis for cell death categorization and prediction in vivo using quantitative ultrasound and machine learning techniques.

At present, a one-size-fits-all approach is typically used for cancer therapy in patients. This is mainly because there is no current imaging-based clinical standard for the early assessment and monitoring of cancer treatment response. Here, the authors have developed, for the first time, a complete computer-aided-prognosis (CAP) system based on multiparametric quantitative ultrasound (QUS) spectroscopy methods in association with texture descriptors and advanced machine learning techniques. This system was used to noninvasively categorize and predict cell death levels in fibrosarcoma mouse tumors treated using ultrasound-stimulated microbubbles as novel endothelial-cell radiosensitizers. Sarcoma xenograft tumor-bearing mice were treated using ultrasound-stimulated microbubbles, alone or in combination with x-ray radiation therapy, as a new antivascular treatment. Therapy effects were assessed at 2-3, 24, and 72 h after treatment using a high-frequency ultrasound. Two-dimensional spectral parametric maps were generated using the power spectra of the raw radiofrequency echo signal. Subsequently, the distances between "pretreatment" and "post-treatment" scans were computed as an indication of treatment efficacy, using a kernel-based metric on textural features extracted from 2D parametric maps. A supervised learning paradigm was used to either categorize cell death levels as low, medium, or high using a classifier, or to "continuously" predict the levels of cell death using a regressor. The developed CAP system performed at a high level for the classification of cell death levels. The area under curve of the receiver operating characteristic was 0.87 for the classification of cell death levels to both low/medium and medium/high levels. Moreover, the prediction of cell death levels using the proposed CAP system achieved a good correlation (r = 0.68, p < 0.001) with histological cell death levels as the ground truth. A statistical test of significance between individual treatment groups with the corresponding control group demonstrated that the predicted levels indicated the same significant changes in cell death as those indicated by the ground-truth levels. The technology developed in this study addresses a gap in the current standard of care by introducing a quality control step that generates potentially actionable metrics needed to enhance treatment decision-making. The study establishes a noninvasive framework for quantifying levels of cancer treatment response developed preclinically in tumors using QUS imaging in conjunction with machine learning techniques. The framework can potentially facilitate the detection of refractory responses in patients to a certain cancer treatment early on in the course of therapy to enable switching to more efficacious treatments.

500-MHz scanning acoustic microscopy of cancerous human lymph nodes

Scanning acoustic microscopy (SAM) at 500 MHz permits forming 2D maps of the speed of sound (SOS), attenuation (A) and acoustic impedance (Z) of tissue microstructure with a spatial resolution of 4 μm. Maps from cancerous lymph nodes (LNs) can improve understanding of ultrasound scattering at 25 MHz and thereby can improve the specificity and sensitivity of quantitative ultrasound (QUS) methods for clinically significant micrometastases. SAM data were acquired from ten fixed LNs from colorectal-cancer patients. Deparaffinized, 6-μm thick sections were scanned using a custom-built SAM equipped with a 500-MHz transducer having a 264-MHz bandwidth and a 4-μm lateral beamwidth. Reflected signals were digitized at 2.5 GHz and maps of Z, A, and SOS were generated. Histological thin sections adjacent to the scanned samples were stained using hematoxylin and eosin, and metastatic regions were demarcated on the photomicrograph. The 4-μm spatial resolution permitted distinguishing metastases from other tissue types...

Local Transverse-Slice-Based Level-Set Method for Segmentation of 3-D High-Frequency Ultrasonic Backscatter From Dissected Human Lymph Nodes.

To detect metastases in freshly excised human lymph nodes (LNs) using three-dimensional (3-D), high-frequency, quantitative ultrasound (QUS) methods, the LN parenchyma (LNP) must be segmented to preclude QUS analysis of data in regions outside the LNP and to compensate ultrasound attenuation effects due to overlying layers of LNP and residual perinodal fat (PNF). After restoring the saturated radio-frequency signals from PNF using an approach based on smoothing cubic splines, the three regions, i.e., LNP, PNF, and normal saline (NS), in the LN envelope data are segmented using a new, automatic, 3-D, three-phase, statistical transverseslice-based level-set (STS-LS) method that amends Lankton's method. Due to ultrasound attenuation and focusing effects, the speckle statistics of the envelope data vary with imaged depth. Thus, to mitigate depth-related inhomogeneity effects, the STS-LS method employs gamma probabilitydensity functions to locally model the speckle statistics within consecutive transverse slices. Accurate results were obtained on simulated data. On a representative dataset of 54 LNs acquired from colorectal-cancer patients, the Dice similarity coefficient for LNP, PNF, and NS were 0.938 ± 0.025, 0.832 ± 0.086, and 0.968 ± 0.008, respectively, when compared to expert manual segmentation. The STS-LS outperforms the established methods based on global and local statistics in our datasets and is capable of accurately handling the depth-dependent effects due to attenuation and focusing. This advance permits the automatic QUS-based cancer detection in the LNs. Furthermore, the STS-LS method could potentially be used in a wide range of ultrasound-imaging applications suffering from depth-dependent effects.

Quantitative ultrasound assessment of breast tumor response to chemotherapy using a multi-parameter approach.

PurposeThis study demonstrated the ability of quantitative ultrasound (QUS) parameters in providing an early prediction of tumor response to neoadjuvant chemotherapy (NAC) in patients with locally advanced breast cancer (LABC).MethodsUsing a 6-MHz array transducer, ultrasound radiofrequency (RF) data were collected from 58 LABC patients prior to NAC treatment and at weeks 1, 4, and 8 of their treatment, and prior to surgery. QUS parameters including midband fit (MBF), spectral slope (SS), spectral intercept (SI), spacing among scatterers (SAS), attenuation coefficient estimate (ACE), average scatterer diameter (ASD), and average acoustic concentration (AAC) were determined from the tumor region of interest. Ultrasound data were compared with the ultimate clinical and pathological response of the patient's tumor to treatment and patient recurrence-free survival.ResultsMulti-parameter discriminant analysis using the κ-nearest-neighbor classifier demonstrated that the best response classification could be achieved using the combination of MBF, SS, and SAS, with an accuracy of 60 ± 10% at week 1, 77 ± 8% at week 4 and 75 ± 6% at week 8. Furthermore, when the QUS measurements at each time (week) were combined with pre-treatment (week 0) QUS values, the classification accuracies improved (70 ± 9% at week 1, 80 ± 5% at week 4, and 81 ± 6% at week 8). Finally, the multi-parameter QUS model demonstrated a significant difference in survival rates of responding and non-responding patients at weeks 1 and 4 (p=0.035, and 0.027, respectively).ConclusionThis study demonstrated for the first time, using new parameters tested on relatively large patient cohort and leave-one-out classifier evaluation, that a hybrid QUS biomarker including MBF, SS, and SAS could, with relatively high sensitivity and specificity, detect the response of LABC tumors to NAC as early as after 4 weeks of therapy. The findings of this study also suggested that incorporating pre-treatment QUS parameters of a tumor improved the classification results. This work demonstrated the potential of QUS and machine learning methods for the early assessment of breast tumor response to NAC and providing personalized medicine with regards to the treatment planning of refractory patients.

Quantitative Characterization of Tissue Microstructure in Concentrated Cell Pellet Biophantoms Based on the Structure Factor Model.

Quantitative ultrasound (QUS) methods based on the backscatter coefficient (BSC) are typically model-based. The BSC is estimated from experiments and is fit to a model. The fit parameters are often termed QUS estimates and are used to characterize the scattering properties of the tissue under investigation. Nevertheless, for physical interpretation of QUS estimates to be accurate, the scattering model chosen must also be accurate. The goal of this work was to investigate the use of the structure factor model (SFM) to take into account coherent scattering from high volume fractions of scatterers. The study focuses on comparing the performance of two sparse models (fluid-filled sphere and Gaussian) and one concentrated model (SFM) to estimate QUS parameters from simulations and cell pellet biophantoms with a range of scatterer volume fractions. Results demonstrated the superiority of the SFM for all investigated volume fractions (i.e., from 0.006 to 0.30). In particular, the sparse models underestimated scatterer size and overestimated acoustic concentration when the volume fraction was greater than 0.12. In addition, the SFM has the ability to provide the volume fraction and the relative impedance contrast (instead of only the acoustic concentration provided by the sparse models), which could have a great benefit for tissue characterization. This study demonstrates that the SFM could prove to be an invaluable tool for QUS and could help to more accurately characterize tissue from ultrasound measurements.

Computer Aided Theragnosis Using Quantitative Ultrasound Spectroscopy and Maximum Mean Discrepancy in Locally Advanced Breast Cancer.

A noninvasive computer-aided-theragnosis (CAT) system was developed for the early therapeutic cancer response assessment in patients with locally advanced breast cancer (LABC) treated with neoadjuvant chemotherapy. The proposed CAT system was based on multi-parametric quantitative ultrasound (QUS) spectroscopic methods in conjunction with advanced machine learning techniques. Specifically, a kernel-based metric named maximum mean discrepancy (MMD), a technique for learning from imbalanced data based on random undersampling, and supervised learning were investigated with response-monitoring data from LABC patients. The CAT system was tested on 56 patients using statistical significance tests and leave-one-subject-out classification techniques. Textural features using state-of-the-art local binary patterns (LBP), and gray-scale intensity features were extracted from the spectral parametric maps in the proposed CAT system. The system indicated significant differences in changes between the responding and non-responding patient populations as well as high accuracy, sensitivity, and specificity in discriminating between the two patient groups early after the start of treatment, i.e., on weeks 1 and 4 of several months of treatment. The proposed CAT system achieved an accuracy of 85%, 87%, and 90% on weeks 1, 4 and 8, respectively. The sensitivity and specificity of developed CAT system for the same times was 85%, 95%, 90% and 85%, 85%, 91%, respectively. The proposed CAT system thus establishes a noninvasive framework for monitoring cancer treatment response in tumors using clinical ultrasound imaging in conjunction with machine learning techniques. Such a framework can potentially facilitate the detection of refractory responses in patients to treatment early on during a course of therapy to enable possibly switching to more efficacious treatments.

Diagnosis of knee osteoarthritis and gait variability increases risk of falling for osteoporotic older adults: The GAINA study

ObjectivesTo examine the relationship between history of falling, and musculoskeletal disease, bone mass, muscle strength, muscle mass, kyphosis, and gait speed and variability in osteoporotic older adults. MethodsThe GAINA study, which began in 2014, is a population-based prospective study of subject cohorts from the town of Hino, Tottori Prefecture, Japan. Participants were recruited from among individuals who had an annual town-sponsored medical check-up in 2014, between May and June. A total 223 of residents were screened for our study. Inclusion criteria were osteoporotic older adults who had: (1) a ≤70% young adult mean (YAM) bone mass as assessed using quantitative ultrasound (QUS) methods, (2) a prior hip fracture or vertebral fracture, (3) fragility fractures other than hip and vertebral fractures and <80% YAM of bone mass. From the total screened, 91 residents (mean age, 76.0 ± 8.0 years; 22 men and 69 women) met the eligibility criteria. History of falling, diagnosis of musculoskeletal disease, bone mass, grip strength, muscle mass, kyphosis, and gait speed were assessed. Gait variability analysis was based on acceleration in three directions: mediolateral (ML), vertical (VT), and anteroposterior (AP) using a wireless triaxial accelerometer. Subjects were classified as belonging to a nonfall group or fall group based on their history of falling. ResultsDuring the previous 12 months, 26 falls were reported by 16 subjects (5 men and 11 women). Sixteen of the 91 included patients (17.6%) fell (22% men and 15% women), and 8 of the 16 subjects reported multiple falls. In a multivariable analysis, diagnosis of knee osteoarthritis and higher gait variability had a significant independent relationship with falling in these osteoporotic adults. ConclusionsDiagnosis of knee osteoarthritis and gait variability are associated with previous falls in osteoporotic older adults. Accelerometry-based gait analysis may be a useful tool with which to assess the risk of falling in this population.

Special issue on Quantitative Ultrasound-Based Tissue Characterization - Call for Papers

Techiques for quatitative tissue characterization have been extensively developed for decades in the ultrasound community. Several methods have reached a level of maturity that allows for reliable ex vivo and in vivo estimation of intrinsic tissue parameters. Recently, the success of quantitative elastographic methods to stage liver fibrosis has become an example of how ultrasonic tissue characterization has the potential to revolutionize clinical practice. Therefore, a comprehensive review of the current state-of-the-art quantitative ultrasound methods applied to tissue characterization becomes relevant.

Quantitative-ultrasound detection of cancer in human lymph nodes based on support vector machines

Histological assessment of lymph nodes excised from cancer patients suffers from an unsatisfactory rate of false-negative determinations. We are evaluating high-frequency quantitative ultrasound (QUS) to detect metastatic regions in lymph nodes freshly excised from cancer patients. Three-dimensional (3D) RF data were acquired from 289 lymph nodes of 82 colorectal-, 15 gastric-, and 70 breast-cancer patients with a custom scanner using a 26-MHz, single-element transducer. Following data acquisition, individual nodes underwent step-sectioning at 50-µm to assure that no clinically significant cancer foci were missed. RF datasets were analyzed using 3D regions-of-interest that were processed to yield 13 QUS estimates including spectral-based and envelope-statistics-based parameters. QUS estimates are associated with tissue microstructure and are hypothesized to provide contrast between non-cancerous and cancerous regions. Leave-one-out classifications, ROC curves, and areas under the ROC (AUC) were used to compare the performance of support vector machines (SVMs) and step-wise linear discriminant analyses (LDA). Results showed that SVM performance (AUC = 0.87) was superior to LDA performance (AUC = 0.78). These results suggest that QUS methods may provide an effective tool to guide pathologists towards suspicious regions and also indicate that classification accuracy can be improved using sophisticated and robust classification tools. [Supported in part by NIH grant CA100183.]

250-MHz quantitative acoustic microscopy for assessing human lymph-node microstructure

We employed quantitative acoustic microscopy (QAM) to measure acoustic properties of tissue microstructure. 32 QAM datasets were acquired from 2, fresh and 11, deparaffinized, 12-µm-thick lymph-node samples obtained from cancer patients. Our custom-built acoustic microscope was equipped with an F-1.16, 250-MHz transducer having a 160-MHz bandwidth to acquire reflected signals from the tissue and a substrate that intimately contacted the tissue. QAM images with a spatial resolution of 7 µm were generated of attenuation (A), speed of sound (SOS), and acoustic impedance (Z). Samples then were stained using hematoxylin and eosin, imaged by light microscopy, and co-registered to QAM images. The spatial resolution and contrast of QAM images were sufficient to distinguish tissue regions consisting of lymphocytes, fat cells and fibrous tissue. Average properties for lymphocyte-dominated tissue were 1552.6 ± 30 m/s for SOS, 9.53 ± 3.6 dB/MHz/cm for A, and 1.58 ± 0.08 Mrayl for Z. Values for Z obtained from fresh samples agreed well with those obtained from 12-µm sections from the same node. Such 2D images provide a basis for developing improved ultrasound-scattering models underlying quantitative ultrasound methods currently used to detect cancerous regions within lymph nodes. [NIH Grant R21EB016117.]

Quantitative ultrasound methods for uterine-cervical assessment

The cervix is a remarkable organ. One of its tasks is to remain firm and “closed” (5 mm diameter cervical canal) prior to pregnancy. Shortly after conception the cervix begins to soften through collagen remodeling and increased hydration. As the fetus reaches full-term there is a profound breakdown in the collagen structure. At the end of this process, the cervix is as soft as warm butter and the cervical canal has dilated to about 100 mm diameter. Errors in timing of this process are a cause for preterm birth, which has a cascade of life-threatening consequences. Quantitative ultrasound is well-suited to monitoring these changes. We have demonstrated the ability to accurately assess the elastic properties and acoustic scattering properties (anisotropy in backscatter and attenuation) of the cervix in non-pregnant hysterectomy specimens and in third trimester pregnancy. We’ve shown that acoustic and mechanical properties vary along the length of the cervix. When anisotropy and spatially variability are accounted for, there are clear differences in parameter values with subtle differences in softening. We are corroborating acoustic observations with nonlinear optical microscopy imaging for a reality check on underlying tissue structure. This presentation will provide an overview of this effort.

Dynamic Ultrasound Imaging Applications to Quantify Musculoskeletal Function

Advances in imaging methods have led to new capability to study muscle and tendon motion in vivo. Direct measurements of muscle and tendon kinematics using imaging may lead to improved understanding of musculoskeletal function. This review presents quantitative ultrasound methods for muscle dynamics that can be used to assess in vivo musculoskeletal function when integrated with other conventional biomechanical measurements.

Ultrasound to Assess Bone Quality

Bone quality is determined by a variety of compositional, micro- and ultrastructural properties of the mineralized tissue matrix. In contrast to X-ray-based methods, the interaction of acoustic waves with bone tissue carries information about elastic and structural properties of the tissue. Quantitative ultrasound (QUS) methods represent powerful alternatives to ionizing x-ray based assessment of fracture risk. New in vivo applicable methods permit measurements of fracture-relevant properties, [eg, cortical thickness and stiffness at fragile anatomic regions (eg, the distal radius and the proximal femur)]. Experimentally, resonance ultrasound spectroscopy and acoustic microscopy can be used to assess the mesoscale stiffness tensor and elastic maps of the tissue matrix at microscale resolution, respectively. QUS methods, thus, currently represent the most promising approach for noninvasive assessment of components of fragility beyond bone mass and bone microstructure providing prospects for improved assessment of fracture risk.

Quantitative ultrasound method for assessing stress–strain properties and the cross-sectional area of Achilles tendon

The Achilles tendon is one of the most commonly observed tendons injured with a variety of causes, such as trauma, overuse and degeneration, in the human body. Rupture and tendinosis are relatively common for this strong tendon. Stress–strain properties and shape change are important biomechanical properties of the tendon to assess surgical repair or healing progress. Currently, there are rather limited non-invasive methods available for precisely quantifying the in vivo biomechanical properties of the tendons. The aim of this study was to apply quantitative ultrasound (QUS) methods, including ultrasonic attenuation and speed of sound (SOS), to investigate porcine tendons in different stress–strain conditions. In order to find a reliable method to evaluate the change of tendon shape, ultrasound measurement was also utilized for measuring tendon thickness and compared with the change in tendon cross-sectional area under different stress. A total of 15 porcine tendons of hind trotters were examined. The test results show that the attenuation and broadband ultrasound attenuation decreased and the SOS increased by a smaller magnitude as the uniaxial loading of the stress–strain upon tendons increased. Furthermore, the tendon thickness measured with the ultrasound method was significantly correlated with tendon cross-sectional area (Pearson coefficient = 0.86). These results also indicate that attenuation of QUS and ultrasonic thickness measurement are reliable and potential parameters for assessing biomechanical properties of tendons. Further investigations are needed to warrant the application of the proposed method in a clinical setting.

Characterization of thyroid cancer in mouse models using high-frequency quantitative ultrasound techniques.

Currently, the evaluation of thyroid cancer relies on the use of fine-needle aspiration biopsy, as non-invasive imaging methods do not provide sufficient levels of accuracy for the diagnosis of this disease. In this study, the potential of quantitative ultrasound methods for characterization of thyroid tissues was studied using a rodent model ex vivo. A high-frequency ultrasonic scanning system (40 MHz) was used to scan thyroids extracted from mice that had spontaneously developed thyroid lesions (cancerous or benign). Three sets of mice were acquired having different predispositions to developing three thyroid anomalies: C-cell adenoma, papillary thyroid carcinoma (PTC) and follicular variant papillary thyroid carcinoma (FV-PTC). A fourth set of mice that did not develop thyroid anomalies (normal mice) were used as controls. The backscatter coefficient was estimated from excised thyroid lobes the different mice. From the backscatter coefficient versus frequency (25-45 MHz), the effective scatterer diameter (ESD) and effective acoustic concentration (EAC) were estimated. From the envelope of the backscattered signal, the homodyned K distribution was used to estimate the k parameter (ratio of coherent to incoherent signal energy) and the μ parameter (number of scatterers per resolution cell). Statistically significant differences were observed between cancerous thyroids and normal thyroids based on the ESD, EAC and μ parameters. The mean ESD values were 18.0 ± 0.92, 15.9 ± 0.81 and 21.5 ± 1.80 μm for the PTC, FV-PTC and normal thyroids, respectively. The mean EAC values were 59.4 ± 1.74, 62.7 ± 1.61 and 52.9 ± 3.42 dB (mm(-3)) for the PTC, FV-PTC and normal thyroids, respectively. The mean μ values were 2.55 ± 0.37, 2.59 ± 0.43 and 1.56 ± 0.99 for the PTC, FV-PTC and normal thyroids, respectively. Statistically significant differences were observed between cancerous thyroids and C-cell adenomas based on the ESD and EAC parameters, with an estimated ESD value of 21.3 ± 1.50 μm and EAC value of 54.7 ± 2.24 dB mm(-3) for C-cell adenomas. These results suggest that high-frequency quantitative ultrasound may enhance the ability to detect and classify diseased thyroid tissues.

WE‐E‐134‐08: Characterization of Carotid Plaque with Histology and Quantitative Ultrasound

Purpose: To compare and correlate localization and registration of calcified, fibrous and lipid regions within heterogeneous plaque using histology and ultrasound quantitative imaging, and to better characterize carotid plaque. Methods: Atherosclerotic plaque excised after carotid endarterectomy, was imaged ex vivo using a VisualSonics Vevo 770 ultrasound system. Attenuation coefficient images were obtained from the three‐dimensional radiofrequency data collected and rendered into a 3D volume. Fixed plaque tissue was then longitudinally sectioned in increments of 5 um, and sections separated by 100 um were acquired. The thickness of each section of the sample was 5 um. Histopathology images were digitized using a PathScan Enabler IV. The two‐dimensional histology information obtained were registered using Matlab and reconstructed into a 3D volume. Different regions such as the lumen, calcified, fibrous and lipid region were segmented by a pathologist and digitally color‐coded into the 3D histology data. We then compared similar tissue types from the 3D histology volume to the estimated 3D attenuation coefficient using quantitative ultrasound methods. Results: Our results indicate that calcified, fibrous and lipid regions in the two volumes showed a good correlation. Calcified regions delineated on the histology volume correspond to the high attenuation coefficient region (2.48 dB/cm/MHz) in the 3D attenuation coefficient volume, while lipid regions tend to have lower attenuation coefficient values (1.47 dB/cm/MHz). Fibrous regions in histology, correlate to the lowest attenuation coefficient value (0.86 dB/cm/MHz). Conclusion: Most plaques are heterogeneous and difficult to characterize precisely. This work demonstrates a direct correlation between pathology and ultrasound quantitative imaging characterization of plaque. It provides a promising way for direct localization of plaque regions and structures, and for improved determination of plaque composition. This work was supported in part by NIH grants R21 EB010098‐02, R01 NS064034‐04, and 2R01 CA112192‐06.

Acoustical imaging of internal spheroid structures at a variety of frequencies

We have previously shown that ultrasound is capable of identifying apoptosis in individual cells and cell pellets due to changes in the ultrasound attenuation, speed of sound, and backscatter. Spheroids can be used to more accurately model non-vascularized tumors due to their three-dimensional growth pattern, cell-cell interaction, disorganized growth, and development of a necrotic core when grown to sufficiently large sizes. To examine cell death in spheroids due to necrosis or chemotherapy, quantitative ultrasound methods were used on the ultrasound backscatter power spectrum throughout the spheroid. MCF7 spheroids ranging in size from 100 to 1000 μm were probed at 25 and 55 MHz using a VEVO770 high frequency ultrasound machine, and at 80 and 200 MHz using an acoustic microscope. Changes in spheroid structure as the necrotic core develops, and after it is exposed to chemotherapeutic agents were recorded and analyzed. An increase in the ultrasound backscatter amplitude from necrotic cells within the core of the spheroid versus the viable cells around the core was observed. Changes in the ultrasound backscatter were also observed for spheroids treated with chemotherapeutics to induce apoptosis. This work furthers our understanding of non-invasively identifying the viability of cancerous tumors, and the efficacy of chemotherapeutic treatments.

High-frequency quantitative ultrasound approaches for cancer detection in freshly-excised lymph nodes

Histology performed to assess lymph nodes excised during node-dissection surgeries from cancer patients suffers an unsatisfactory rate of false-negative determinations due to labor and time constraints. In this study, more than 300 lymph nodes were scanned in 3D using a 26-MHz high-frequency ultrasound transducer. Following scanning, individual nodes underwent a special histology procedure that involved step-sectioning each node at 50-µm intervals to guarantee that no significant cancer foci were missed. The 3D radio-frequency ultrasound dataset was analyzed using overlapping 3D regions-of-interests that were individually processed to yield thirteen quantitative ultrasound (QUS) estimates associated with tissue microstructure and were hypothesized to show contrast between normal and cancerous regions in lymph nodes. Step-wise linear discriminant analyses were performed to yield an optimal QUS-based classifier. ROC curves and areas under the ROC curves (AUCs) were obtained to assess cancer-detection performance. The AUC for the linear combination of four QUS estimates was 0.83 for a dataset of 110 axillary nodes of breast-cancer patients. Similarly, using five QUS estimates, an AUC of 0.97 was obtained for a dataset of 180 nodes of gastrointestinal-cancer patients. These studies demonstrate that QUS methods may provide an effective tool to guide pathologist towards suspicious regions in lymph nodes.

Quantitative Ultrasound Evaluation of Tumor Cell Death Response in Locally Advanced Breast Cancer Patients Receiving Chemotherapy

Quantitative ultrasound techniques have been recently shown to be capable of detecting cell death through studies conducted on in vitro and in vivo models. This study investigates for the first time the potential of early detection of tumor cell death in response to clinical cancer therapy administration in patients using quantitative ultrasound spectroscopic methods. Patients (n = 24) with locally advanced breast cancer received neoadjuvant chemotherapy treatments. Ultrasound data were collected before treatment onset and at 4 times during treatment (weeks 1, 4, and 8, and preoperatively). Quantitative ultrasound parameters were evaluated for clinically responsive and nonresponding patients. Results indicated that quantitative ultrasound parameters showed significant changes for patients who responded to treatment, and no similar alteration was observed in treatment-refractory patients. Such differences between clinically and pathologically determined responding and nonresponding patients were statistically significant (P < 0.05) after 4 weeks of chemotherapy. Responding patients showed changes in parameters related to cell death with, on average, an increase in mid-band fit and 0-MHz intercept of 9.1 ± 1.2 dBr and 8.9 ± 1.9 dBr, respectively, whereas spectral slope was invariant. Linear discriminant analysis revealed a sensitivity of 100% and a specificity of 83.3% for distinguishing nonresponding patients by the fourth week into a course of chemotherapy lasting several months. This study reports for the first time that quantitative ultrasound spectroscopic methods can be applied clinically to evaluate cancer treatment responses noninvasively. The results form a basis for monitoring chemotherapy effects and facilitating the personalization of cancer treatment.

Three-dimensional quantitative ultrasound for detecting lymph node metastases

PurposeDetection of metastases in lymph nodes (LNs) is critical for cancer management. Conventional histological methods may miss metastatic foci. To date, no practical means of evaluating the entire LN volume exists. The aim of this study was to develop fast, reliable, operator-independent, high-frequency, quantitative ultrasound (QUS) methods for evaluating LNs over their entire volume to effectively detect LN metastases. MethodsWe scanned freshly excised LNs at 26 MHz and digitally acquired echo-signal data over the entire three-dimensional (3D) volume. A total of 146 LNs of colorectal, 26 LNs of gastric, and 118 LNs of breast cancer patients were enrolled. We step-sectioned LNs at 50-μm intervals and later compared them with 13 QUS estimates associated with tissue microstructure. Linear-discriminant analysis classified LNs as metastatic or nonmetastatic, and we computed areas (Az) under receiver-operator characteristic curves to assess classification performance. The QUS estimates and cancer probability values derived from discriminant analysis were depicted in 3D images for comparison with 3D histology. ResultsOf 146 LNs of colorectal cancer patients, 23 were metastatic; Az = 0.952 ± 0.021 (95% confidence interval [CI]: 0.911–0.993); sensitivity = 91.3% (specificity = 87.0%); and sensitivity = 100% (specificity = 67.5%). Of 26 LNs of gastric cancer patients, five were metastatic; Az = 0.962 ± 0.039 (95% CI: 0.807–1.000); sensitivity = 100% (specificity = 95.3%). A total of 17 of 118 LNs of breast cancer patients were metastatic; Az = 0.833 ± 0.047 (95% CI: 0.741–0.926); sensitivity = 88.2% (specificity = 62.5%); sensitivity = 100% (specificity = 50.5%). 3D cancer probability images showed good correlation with 3D histology. ConclusionsThese results suggest that operator- and system-independent QUS methods allow reliable entire-volume LN evaluation for detecting metastases. 3D cancer probability images can help pathologists identify metastatic foci that could be missed using conventional methods.