Artificial intelligence - based ultrasound elastography for disease evaluation - a narrative review

Ultrasound elastography (USE) provides complementary information of tissue stiffness and elasticity to conventional ultrasound imaging. It is noninvasive and free of radiation, and has become a valuable tool to improve diagnostic performance with conventional ultrasound imaging. However, the diagnostic accuracy will be reduced due to high operator-dependence and intra- and inter-observer variability in visual observations of radiologists. Artificial intelligence (AI) has great potential to perform automatic medical image analysis tasks to provide a more objective, accurate and intelligent diagnosis. More recently, the enhanced diagnostic performance of AI applied to USE have been demonstrated for various disease evaluations. This review provides an overview of the basic concepts of USE and AI techniques for clinical radiologists and then introduces the applications of AI in USE imaging that focus on the following anatomical sites: liver, breast, thyroid and other organs for lesion detection and segmentation, machine learning (ML) - assisted classification and prognosis prediction. In addition, the existing challenges and future trends of AI in USE are also discussed.

Ultrasound elastography

Physicians have used palpation as a diagnostic examination to understand the elastic properties of pathology for a long time since they realized that tissue stiffness is closely related to its biological characteristics. US elastography provided new diagnostic information about elasticity comparing with the morphological feathers of traditional US, and thus expanded the scope of the application in clinic. US elastography is now widely used in the field of diagnosis and differential diagnosis of abnormality, evaluating the degree of fibrosis and assessment of treatment response for a range of diseases. The World Federation of Ultrasound Medicine and Biology divided elastographic techniques into strain elastography (SE), transient elastography and acoustic radiation force impulse (ARFI). The ARFI techniques can be further classified into point shear wave elastography (SWE), 2D SWE, and 3D SWE techniques. The SE measures the strain, while the shear wave-based techniques (including TE and ARFI techniques) measure the speed of shear waves in tissues. In this review, we discuss the various techniques separately based on their basic principles, clinical applications in various organs, and advantages and limitations and which might be most appropriate given that the majority of doctors have access to only one kind of machine.

Added Value of a New Strain Elastography Technique in Conventional Ultrasound for the Diagnosis of Breast Masses: A Prospective Multicenter Study

Objective

This study aimed to explore the value of elasticity score (ES) and strain ratio (SR) combined with conventional ultrasound in distinguishing benign and malignant breast masses and reducing biopsy of BI-RADS (Breast Imaging Reporting and Data System) 4a lesions.

Methods

This prospective, multicenter study included 910 patients from nine different hospitals. The acquisition and analysis of conventional ultrasound and strain elastography (SE) were obtained by radiologists with more than 5 years of experience in breast ultrasound imaging. The diagnostic sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), and area under curve (AUC) of conventional ultrasound alone and combined tests with ES and/or SR were calculated and compared.

Results

The optimal cutoff value of SR for differentiating benign from malignant masses was 2.27, with a sensitivity of 60.2% and a specificity of 84.8%. When combined with ES and SR, the AUC of the new BI-RADS classification increased from 0.733 to 0.824 (p < 0.001); the specificity increased from 48.1% to 68.5% (p < 0.001) without a decrease in the sensitivity (98.5% vs. 96.4%, p = 0.065); and the PPV increased from 52.2% to 63.7% (p < 0.001) without a loss in the NPV (98.2% vs. 97.1%, p = 0.327). All three combinations of conventional ultrasound, ES, and SR could reduce the biopsy rate of category 4a lesions without reducing the malignant rate of biopsy (from 100% to 68.3%, 34.9%, and 50.4%, respectively, all p < 0.001).

Conclusions

SE can be used as a useful and non-invasive additional method to improve the diagnostic performance of conventional ultrasound by increasing AUC and specificity and reducing the unnecessary biopsy of BI-RADS 4a lesions.

A Review of the Role of the S-Detect Computer-Aided Diagnostic Ultrasound System in the Evaluation of Benign and Malignant Breast and Thyroid Masses

Computer-aided diagnosis (CAD) systems have attracted extensive attention owing to their performance in the field of image diagnosis and are rapidly becoming a promising auxiliary tool in medical imaging tasks. These systems can quantitatively evaluate complex medical imaging features and achieve efficient and high-diagnostic accuracy. Deep learning is a representation learning method. As a major branch of artificial intelligence technology, it can directly process original image data by simulating the structure of the human brain neural network, thus independently completing the task of image recognition. S-Detect is a novel and interactive CAD system based on a deep learning algorithm, which has been integrated into ultrasound equipment and can help radiologists identify benign and malignant nodules, reduce physician workload, and optimize the ultrasound clinical workflow. S-Detect is becoming one of the most commonly used CAD systems for ultrasound evaluation of breast and thyroid nodules. In this review, we describe the S-Detect workflow and outline its application in breast and thyroid nodule detection. Finally, we discuss the difficulties and challenges faced by S-Detect as a precision medical tool in clinical practice and its prospects.

Preoperative Prediction of Microvascular Invasion in Patients With Hepatocellular Carcinoma Based on Radiomics Nomogram Using Contrast-Enhanced Ultrasound

PURPOSE

This study aimed to develop a radiomics nomogram based on contrast-enhanced ultrasound (CEUS) for preoperatively assessing microvascular invasion (MVI) in hepatocellular carcinoma (HCC) patients.

METHODS

A retrospective dataset of 313 HCC patients who underwent CEUS between September 20, 2016 and March 20, 2020 was enrolled in our study. The study population was randomly grouped as a primary dataset of 192 patients and a validation dataset of 121 patients. Radiomics features were extracted from the B-mode (BM), artery phase (AP), portal venous phase (PVP), and delay phase (DP) images of preoperatively acquired CEUS of each patient. After feature selection, the BM, AP, PVP, and DP radiomics scores (Rad-score) were constructed from the primary dataset. The four radiomics scores and clinical factors were used for multivariate logistic regression analysis, and a radiomics nomogram was then developed. We also built a preoperative clinical prediction model for comparison. The performance of the radiomics nomogram was evaluated via calibration, discrimination, and clinical usefulness.

RESULTS

Multivariate analysis indicated that the PVP and DP Rad-score, tumor size, and AFP (alpha-fetoprotein) level were independent risk predictors associated with MVI. The radiomics nomogram incorporating these four predictors revealed a superior discrimination to the clinical model (based on tumor size and AFP level) in the primary dataset (AUC: 0.849 vs. 0.690; p < 0.001) and validation dataset (AUC: 0.788 vs. 0.661; p = 0.008), with a good calibration. Decision curve analysis also confirmed that the radiomics nomogram was clinically useful. Furthermore, the significant improvement of net reclassification index (NRI) and integrated discriminatory improvement (IDI) implied that the PVP and DP radiomics signatures may be very useful biomarkers for MVI prediction in HCC.

CONCLUSION

The CEUS-based radiomics nomogram showed a favorable predictive value for the preoperative identification of MVI in HCC patients and could guide a more appropriate surgical planning.

Deep Learning Based on ACR TI-RADS Can Improve the Differential Diagnosis of Thyroid Nodules

Objective

The purpose of this study was to improve the differentiation between malignant and benign thyroid nodules using deep learning (DL) in category 4 and 5 based on the Thyroid Imaging Reporting and Data System (TI-RADS, TR) from the American College of Radiology (ACR).

Design and Methods

From June 2, 2017 to April 23, 2019, 2082 thyroid ultrasound images from 1396 consecutive patients with confirmed pathology were retrospectively collected, of which 1289 nodules were category 4 (TR4) and 793 nodules were category 5 (TR5). Ninety percent of the B-mode ultrasound images were applied for training and validation, and the residual 10% and an independent external dataset for testing purpose by three different deep learning algorithms.

Results

In the independent test set, the DL algorithm of best performance got an AUC of 0.904, 0.845, 0.829 in TR4, TR5, and TR4&5, respectively. The sensitivity and specificity of the optimal model was 0.829, 0.831 on TR4, 0.846, 0.778 on TR5, 0.790, 0.779 on TR4&5, versus the radiologists of 0.686 (P=0.108), 0.766 (P=0.101), 0.677 (P=0.211), 0.750 (P=0.128), and 0.680 (P=0.023), 0.761 (P=0.530), respectively.

Conclusions

The study demonstrated that DL could improve the differentiation of malignant from benign thyroid nodules and had significant potential for clinical application on TR4 and TR5.

Lymph Node Metastasis Prediction from Primary Breast Cancer US Images Using Deep Learning

Background Deep learning (DL) algorithms are gaining extensive attention for their excellent performance in image recognition tasks. DL models can automatically make a quantitative assessment of complex medical image characteristics and achieve increased accuracy in diagnosis with higher efficiency. Purpose To determine the feasibility of using a DL approach to predict clinically negative axillary lymph node metastasis from US images in patients with primary breast cancer. Materials and Methods A data set of US images in patients with primary breast cancer with clinically negative axillary lymph nodes from Tongji Hospital (974 imaging studies from 2016 to 2018, 756 patients) and an independent test set from Hubei Cancer Hospital (81 imaging studies from 2018 to 2019, 78 patients) were collected. Axillary lymph node status was confirmed with pathologic examination. Three different convolutional neural networks (CNNs) of Inception V3, Inception-ResNet V2, and ResNet-101 architectures were trained on 90% of the Tongji Hospital data set and tested on the remaining 10%, as well as on the independent test set. The performance of the models was compared with that of five radiologists. The models' performance was analyzed in terms of accuracy, sensitivity, specificity, receiver operating characteristic curves, areas under the receiver operating characteristic curve (AUCs), and heat maps. Results The best-performing CNN model, Inception V3, achieved an AUC of 0.89 (95% confidence interval [CI]: 0.83, 0.95) in the prediction of the final clinical diagnosis of axillary lymph node metastasis in the independent test set. The model achieved 85% sensitivity (35 of 41 images; 95% CI: 70%, 94%) and 73% specificity (29 of 40 images; 95% CI: 56%, 85%), and the radiologists achieved 73% sensitivity (30 of 41 images; 95% CI: 57%, 85%; P = .17) and 63% specificity (25 of 40 images; 95% CI: 46%, 77%; P = .34). Conclusion Using US images from patients with primary breast cancer, deep learning models can effectively predict clinically negative axillary lymph node metastasis. Artificial intelligence may provide an early diagnostic strategy for lymph node metastasis in patients with breast cancer with clinically negative lymph nodes. Published under a CC BY 4.0 license. Online supplemental material is available for this article. See also the editorial by Bae in this issue.

Artificial intelligence in breast ultrasound

Artificial intelligence (AI) is gaining extensive attention for its excellent performance in image-recognition tasks and increasingly applied in breast ultrasound. AI can conduct a quantitative assessment by recognizing imaging information automatically and make more accurate and reproductive imaging diagnosis. Breast cancer is the most commonly diagnosed cancer in women, severely threatening women’s health, the early screening of which is closely related to the prognosis of patients. Therefore, utilization of AI in breast cancer screening and detection is of great significance, which can not only save time for radiologists, but also make up for experience and skill deficiency on some beginners. This article illustrates the basic technical knowledge regarding AI in breast ultrasound, including early machine learning algorithms and deep learning algorithms, and their application in the differential diagnosis of benign and malignant masses. At last, we talk about the future perspectives of AI in breast ultrasound.

Artificial intelligence in medical imaging of the liver

Artificial intelligence (AI), particularly deep learning algorithms, is gaining extensive attention for its excellent performance in image-recognition tasks. They can automatically make a quantitative assessment of complex medical image characteristics and achieve an increased accuracy for diagnosis with higher efficiency. AI is widely used and getting increasingly popular in the medical imaging of the liver, including radiology, ultrasound, and nuclear medicine. AI can assist physicians to make more accurate and reproductive imaging diagnosis and also reduce the physicians’ workload. This article illustrates basic technical knowledge about AI, including traditional machine learning and deep learning algorithms, especially convolutional neural networks, and their clinical application in the medical imaging of liver diseases, such as detecting and evaluating focal liver lesions, facilitating treatment, and predicting liver treatment response. We conclude that machine-assisted medical services will be a promising solution for future liver medical care. Lastly, we discuss the challenges and future directions of clinical application of deep learning techniques.

Identification and sequence analysis of a novel HLA-A*33 allele, HLA-A*33:88

HLA-A*33:88 differs from HLA-A*33:03:01 by one nucleotide exchange at position 475, G>A (codon 135 GCG>ACG).

Characterization and comparison of transgenic Artemisia annua GYR and wild-type NON-GYR plants in an environmental release trial

The anti-malarial drug, artemisinin, is quite expensive as a result of its slow content in Artemisia annua. Recent investigations have suggested that genetic engineering of A. annua is a promising approach to improve the yield of artemisinin. In this study, the transgenic A. annua strain GYR, which has high artemisinin content, was evaluated in an environmental release trial. First, GYR plants were compared with the wild-type variety NON-GYR, with regard to phenotypic characters (plant height, crown width, stem diameter, germination rate, leaf dry weight, 1000-seed weight, leave shape). Second, stress resistance in the two varieties (salt, drought, herbicide, and cold resistance) was evaluated under different experimental conditions. Finally, gene flow was estimated. The results indicated that there were significant differences in several agronomic traits (plant height, stem diameter, and leave dry weight) between the transgenic GYR and NON-GYR plants. Salt stress in transgenic and control plants was similar, except under high NaCl concentrations (1.6%, w/w). Leaf water, proline, and MDA content (increased significantly) were significantly different. Transgenic A. annua GYR plants did not grow better than NON-GYR plants with respect to drought and herbicide resistance. The two varieties maintained vitality through the winter. Third, gene flow was studied in an environmental risk trial for transgenic GYR. The maximum gene flow frequency was 2.5%, while the maximum gene flow distance was 24.4 m; gene flow was not detected at 29.2 m at any direction. Our findings may provide an opportunity for risk assessment in future commercialization of transgenic A. annua varieties.

Report on the 14th International Society of Blood Transfusion Platelet Immunology Workshop

BACKGROUND AND OBJECTIVES

The aims of the 14th ISBT Platelet Immunology Workshop were to evaluate in-house methods for detection of antibodies to human platelet antigens, to compare the sensitivity and specificity of antibody detection using a panel of monoclonal antibodies and to evaluate genotyping methods and establish procedures for drug-dependent antibody detection.

MATERIALS AND METHODS

Forty-two laboratories from 23 countries participated. Samples and reagents provided for the five different exercises.

RESULTS

The ability of participating laboratories to correctly identify the HPA antibody specificity in the nine samples ranged from 20% to 97%. The greatest difficulty was observed with samples that contained antibodies against HPA-3b and GPIV. The significant differences in optical density values by monoclonal antibody of immobilization of platelet antigens (MAIPA) assay were observed when testing the same platelet-specific antibodies. HPA genotyping of DNA with novel mutations did not significantly affect the results. The overall average discrepancy rate was 2·15% for genotyping of 10 DNA samples from well-characterized Epstein–Barr virus transformed cell lines. For detection of drug-dependent antibodies, excellent results for specificity and sensitivity were obtained by the laboratories using the MAIPA and flow cytometry.

CONCLUSIONS

Most laboratories were able to identify the majority of HPA antibodies; however, significant disparities were observed in proficiency testing. MAIPA assay sensitivity is influenced by the monoclonal antibody clone used. DNA with new mutations and EBV cell lines are valuable samples to ensure accurate genotyping. A sensitive and specific drug-dependent antibody assay performed well in the hands of participants.

Molecular genetic analysis for Ax phenotype of the ABO blood group system in Chinese

BACKGROUND AND OBJECTIVES

To elucidate the molecular genetic background of the Ax phenotype in the Chinese population.

MATERIALS AND METHODS

The ABO genes of eight Ax phenotype samples, four Ax and four AxB, were amplified by polymerase chain reaction (PCR) and were cloned, along with those of 10 random A(1) Chinese subjects. We analysed the ABO gene transcript structure and the sequences of two exons and one intron at the ABO locus.

RESULTS

Among the four Ax phenotype samples, we identified one Ax02, two Ax03 and one novel Ax allele with the 543G > T mutation in the A102 background. Two of five family members also carry the allele. Of the four AxB phenotypes, one was designated as cis-AB-1/B101; the other three were shown to carry one B allele and one O with the nt261G deletion. The B alleles of the latter three were identical to B101 except for single point mutation at nt700C > G, nt640A > G and nt641T > C, respectively. The novel B101-like alleles were first associated with A(weak)B phenotypes.

CONCLUSIONS

Two ABO*B(A) alleles and an Ax allele clearly differ from all previously reported ABO alleles, suggesting that the molecular genetic background of Ax is heterogeneous in the Chinese population.

An anion-induced regio- and chemoselective acylation and its application to the synthesis of an anticancer agent

[reaction--see text] An efficient Grignard- and organolithium-induced regio- and chemoselective anionic acylation is reported. A number of tricyclic ketones are prepared in good to excellent yields via this method. This method is complementary to the Frieldel-Crafts acylation for electron-deficient substrates. A novel anisole-based Grignard reagent was developed to effect the cyclization of sterically hindered substrates. This novel reagent has been successfully applied to the synthesis of Sch 66336, a candidate for oncologic treatment.

Racial differences in survival of patients on dialysis

BACKGROUND

Recent studies have documented racial differences in the crude mortality rates of patients on dialysis. However, proper interpretation of these findings requires adjustment for potential confounders and comorbid risk factors between the racial groups.

METHODS

We examined the clinical data on 3752 Caucasian patients, 451 Southeast Asian patients, 322 South Asian patients, and 319 black patients who were treated with hemodialysis or peritoneal dialysis under a Universal Health Care system in Toronto and prospectively followed between 1981 and 1995. In all patients, a number of comorbid risk factors for survival was assessed at the start of dialysis and was reassessed with their outcome status (that is, continued dialysis, transplantation, death, or loss to follow-up) at least every six months. Cox proportional hazards analysis was used to fit multivariate models predicting patient survival. Pairwise comparisons of the relative hazards of death between the racial groups were performed after stratifying for cardiovascular disease, diabetes mellitus, and hypertension at the start of dialysis, and were adjusted for differences in other comorbid risk factors.

RESULTS

The risk of death in Caucasian patients was significantly increased when compared with Southeast Asian patients, South Asian patients, and black patients [multivariate relative hazards (95% CI): 1.63 (1.36 to 1.97), 1.36 (1.07 to 1.73), 1.34 (1.07 to 1.67), respectively]. Additionally, we detected an interaction between race and cigarette smoking (P < 0. 004), suggesting that in the dialysis patients who smoked, whites had a higher mortality risk compared with non-whites.

CONCLUSIONS

Differences in patient survival on dialysis exist between racial groups. However, the genetic and environmental determinants that underlie these differences are presently unknown.

Novel 2,2-Bipyridine Ligand for Palladium-Catalyzed Regioselective Carbonylation

[reaction: see text] A palladium-catalyzed highly regioselective one-step carbonylation of 2,5-dibromo-3-methylpyridine is reported. A range of alkyl esters and amides can be prepared in good yield with better than 95:5 regioselectivity via this method. Key to the high regioselectivity for the formation aromatic amides is the introduction of a novel nonphosphine-based 2,2-bipyridine ligand. This novel reaction was scaled up smoothly in the plant to a 130-kg batch size and facilitated the delivery of bulk material for the clinical trials of Sch 66336, a candidate for oncologic treatments.

Quinine-induced immune thrombocytopenia with hemolytic uremic syndrome: clinical and serological findings in nine patients and review of literature

Quinine-induced immune thrombocytopenia with hemolytic uremic syndrome (HUS) is a recently defined clinical entity. In this paper we have attempted to characterize the natural history and laboratory abnormalities typical of quinine-induced immune thrombocytopenia associated with hemolytic uremic syndrome in nine patients experiencing ten episodes of the disease. In addition, review of other reported cases of probable quinine-induced HUS is presented. The disease was characterized by the onset of chills, diapheresis, nausea and vomiting, abdominal pain, decreased urine output, and petechiae following quinine exposure. All patients experience significant anemia, severe thrombocytopenia, increased lactate dehydrogenase, elevated serum creatinine, and oliguria. Quinine-dependent platelet-reactive antibodies were identified in eight of nine using flow cytometry. Unexpectedly, drug-dependent antibodies reactive with red cells and granulocytes were identified in four and eight patients, respectively. All patients were treated with plasma exchange (range 1-12 procedures), and seven required hemodialysis. All survive without residual abnormality. Our experience with nine patients with quinine-induced HUS and the nine additional cases reported by others and reviewed in this paper establishes this condition as a distinct clinical entity. Adult patients presenting with HUS should routinely be asked about exposure to quinine in the form of medication or beverages. The mechanism by which quinine-dependent antibodies produce renal failure is uncertain, but preliminary studies (described elsewhere) suggest that drug-induced antibodies reactive with endothelial cells and possibly margination of granulocytes in renal glomeruli may be responsible for this complication. The prognosis in quinine-induced HUS is better than in other forms of adult HUS.

Antibodies in sulfonamide-induced immune thrombocytopenia recognize calcium-dependent epitopes on the glycoprotein IIb/IIIa complex

Drug-dependent IgG antibodies (DDAb) induced by sulfamethoxazole (SMX) and sulfisoxazole (SIX) were identified by flow cytometry in 15 patients who developed thrombocytopenia while taking one of these medications. Fourteen of the 15 DDAb were specific solely for the glycoprotein (GP)IIb/IIIa complex, and 13 of these reacted wholly or in part with epitopes present only on the intact GPIIb/IIIa heterodimer. None of 12 SMX-induced DDAb cross-reacted with SIX, but one of three SIX-induced antibodies reacted with SMX. Each of 10 SMX-induced DDAb tested reacted with the N1-acetyl metabolite of SMX, but only one reacted fully with the N4-acetyl derivative. Detection of the SMX- and SIX-dependent antibodies was facilitated by using bovine serum albumin (BSA) to achieve suspension of these weakly soluble drugs in an aqueous medium. Our findings indicate that DDAb induced by SMX and SIX, in contrast to those induced by quinidine and quinine, are mainly specific for GPIIb/IIIa and react preferentially with calcium-dependent epitopes present only on the intact GPIIb/IIIa heterodimer.