Challenges facing minimal residual disease testing for acute myeloid leukemia and promising strategies to overcome them.

INTRODUCTION: Minimal residual disease (MRD) has been an important biomarker for relapse prediction and treatment choice in patients with acute myeloid leukemia (AML). False-positive or false-negative MRD results due to the low specificity and sensitivity of techniques such as multiparameter flow cytometry (MFC), real-time quantitative polymerase chain reaction, and next-generation sequencing, as well as the biological characteristics of residual leukemia cells, including antigen shift, clone involution, heterogeneous genome of the blast cells, and lack of specific targets, all restrict the clinical use of MRD. AREAS COVERED: We summarized the challenges of the techniques for MRD detection, and their application in the clinical setting. We also discussed strategies to overcome these challenges, such as the MFC MRD method based on leukemia stem cells, single-cell DNA sequencing or single-cell RNA sequencing for the investigation of biological characteristics of residual leukemia cells, and the potential of omics techniques for MRD detection. We further noted out that prospective clinical trials are needed to answer clinical questions related to MRD in patients with AML. EXPERT OPINION: MRD is an important biomarker for individual therapy of patients with AML. In the future, it is important to increase the specificity and sensitivity of the detection techniques.

Comparison between linear and nonlinear machine-learning algorithms for the classification of thyroid nodules.

BACKGROUND: A key challenge in thyroid carcinoma is preoperatively diagnosing malignant thyroid nodules. The purpose of this study was to compare the classification performance of linear and nonlinear machine-learning algorithms for the evaluation of thyroid nodules using pathological reports as reference standard. METHODS: Ethical approval was obtained for this retrospective analysis, and the informed consent requirement was waived. A total of 1179 thyroid nodules (training cohort, n = 700; validation cohort, n = 479) were confirmed by pathological reports or fine-needle aspiration (FNA) biopsy. The following ultrasonography (US) featu res were measured for each nodule: size (maximum diameter), margins, shape, aspect ratio, capsule, hypoechoic halo, composition, echogenicity, calcification pattern, vascularity, and cervical lymph node status. We analyzed five nonlinear and three linear machine-learning algorithms. The diagnostic performance of each algorithm was compared by using the area under the curve (AUC) of the receiver operating characteristic curve. We repeated this process 1000 times to obtain the mean AUC and 95% confidence interval (CI). RESULTS: Overall, nonlinear machine-learning algorithms demonstrated similar AUCs compared with linear algorithms. The Random Forest and Kernel Support Vector Machines algorithms achieved slightly greater AUCs in the validation cohort (0.954, 95% CI: 0.939-0.969; 0.954 95%CI: 0.939-0.969, respectively) than other algorithms. CONCLUSIONS: Overall, nonlinear machine-learning algorithms share similar performance compared with linear algorithms for the evaluation the malignancy risk of thyroid nodules.

A nomogram for individual prediction of vascular invasion in primary breast cancer.

OBJECTIVES: To explore the feasibility of preoperative prediction of vascular invasion (VI) in breast cancer patients using nomogram based on multiparametric MRI and pathological reports. METHODS: We retrospectively collected 200 patients with confirmed breast cancer between January 2016 and January 2018. All patients underwent MRI examinations before the surgery. VI was identified by postoperative pathology. The 200 patients were randomly divided into training (n = 100) and validation datasets (n = 100) at a ratio of 1:1. Least absolute shrinkage and selection operator (LASSO) regression was used to select predictors most associated with VI of breast cancer. A nomogram was constructed to calculate the area under the curve (AUC) of receiver operating characteristics, sensitivity, specificity, accuracy, positive prediction value (PPV) and negative prediction value (NPV). We bootstrapped the data for 2000 times without setting the random seed to obtain corrected results. RESULTS: VI was observed in 79 patients (39.5%). LASSO selected 10 predictors associated with VI. In the training dataset, the AUC for nomogram was 0.94 (95% confidence interval [CI]: 0.89-0.99, the sensitivity was 78.9% (95%CI: 72.4%-89.1%), the specificity was 95.3% (95%CI: 89.1%-100.0%), the accuracy was 86.0% (95%CI: 82.0%-92.0%), the PPV was 95.7% (95%CI: 90.0%-100.0%), and the NPV was 77.4% (95%CI: 67.8%-87.0%). In the validation dataset, the AUC for nomogram was 0.89 (95%CI: 0.83-0.95), the sensitivity was 70.3% (95%CI: 60.7%-79.2%), the specificity was 88.9% (95%CI: 80.0%-97.1%), the accuracy was 77.0% (95%CI: 70.0%-83.0%), the PPV was 91.8% (95%CI: 85.3%-98.0%), and the NPV was 62.7% (95%CI: 51.7%-74.0%). The nomogram calibration curve shows good agreement between the predicted probability and the actual probability. CONCLUSION: The proposed nomogram could be used to predict VI in breast cancer patients, which was helpful for clinical decision-making.

Development and validation of an ultrasound-based nomogram to improve the diagnostic accuracy for malignant thyroid nodules.

OBJECTIVES: The aim of this study was to develop an ultrasound-based nomogram to improve the diagnostic accuracy of the identification of malignant thyroid nodules. METHODS: A total of 1675 histologically proven thyroid nodules (1169 benign, 506 malignant) were included in this study. The nodules were grouped into the training dataset (n = 700), internal validation dataset (n = 479), or external validation dataset (n = 496). The grayscale ultrasound features included the nodule size, shape, aspect ratio, echogenicity, margins, and calcification pattern. We applied least absolute shrinkage and selection operator (lasso) regression to select the strongest features for the nomogram. Nomogram discrimination (area under the receiver operating characteristic curve, AUC) and calibration were assessed. The nomogram was subjected to bootstrapping validation (1000 bootstrap resamples) to calculate a mean AUC and 95% confidence interval (CI). RESULTS: The nomogram showed good discrimination in the training dataset, with an AUC of 0.936 (95% CI: 0.918-0.953) and good calibration. Application of the nomogram to the internal validation dataset also resulted in good discrimination (AUC: 0.935; 95% CI, 0.915-0.954) and good calibration. The model tested in an external validation dataset demonstrated a lower AUC of 0.782 (95% CI: 0.776-0.789). CONCLUSIONS: This ultrasound-based nomogram can be used to quantify the probability of malignant thyroid nodules. KEY POINTS: • Ultrasound examination is helpful in the differential diagnosis of malignant and benign thyroid nodules. • However, ultrasound accuracy relies heavily on examiner experience. • A less subjective diagnostic model is desired, and the developed nomogram for thyroid nodules showed good discrimination and good calibration.

Development of a preprocedure nomogram for predicting contrast-induced acute kidney injury after coronary angiography or percutaneous coronary intervention.

Most of the risk models for predicting contrast-induced acute kidney injury (CI-AKI) are available for postcontrast exposure prediction, thus have limited values in practice. We aimed to develop a novel nomogram based on preprocedural features for early prediction of CI-AKI in patients after coronary angiography (CAG) or percutaneous coronary intervention (PCI). A total of 245 patients were retrospectively reviewed from January 2015 to January 2017. Least absolute shrinkage and selection operator (Lasso) regression model was applied to select most strong predictors for CI-AKI. The CI-AKI risk score was calculated for each patient as a linear combination of selected predictors that were weighted by their respective coefficients. The discrimination of nomogram was assessed by C-statistic. The occurrence of CI-AKI was 13.9% (34 out of 245). We identified ten predictors including sex, diabetes mellitus, lactate dehydrogenase level, C-reactive protein, years since drinking, chronic kidney disease (CKD), stage of CKD, stroke, acute myocardial infarction, and systolic blood pressure. The CI-AKI prediction nomogram obtained good discrimination (C-statistic, 0.718, 95%CI: 0.637-0.800, p = 7.23 × 10-5). The cutoff value of CI-AKI risk score was -1.953. Accordingly, the novel nomogram we developed is a simple and accurate tool for preprocedural prediction of CI-AKI in patients undergoing CAG or PCI.

Beneficial effect of tripterine on systemic lupus erythematosus induced by active chromatin in BALB/c mice.

The aim of this study was to determine whether tripterine, isolated from Tripterygium wilfordii Hoog f. in China, had beneficial effects on experimental systemic lupus erythematosus induced by active chromatin in BALB/c mice. BALB/c mice were immunized with active chromatin isolated from concanavalin A-activated syngenetic spleno-lymphocytes on day 0. Tripterine 6 or 12 mg kg(-1) day(-1), or prednisone 5 mg kg(-1) day(-1) was given to BALB/c mice intragastrically from day 35 to day 50. Treatment with tripterine 12 mg kg(-1) day(-1) for 15 days protected renal from glomerular injury with a concomitant reduction of serum autoantibodies and total immunoglobulin G (IgG) also with a improvement of splenocyte proliferation stimulated with concanavalin A and lipopolysaccharide. The effects were associated with reduced interleukin-10 production and serum nitric oxide (NO) level but not interferon-gamma compared with vehicle-treated control group. Tripterine 6 mg kg(-1) day(-1) had no significant protective effect against glomerular injury. It inhibited autoantibodies and interleukin-10 production but had no effect on splenocyte proliferation, serum NO level, and interferon-gamma production. These findings suggested that tripterine had a beneficial effect on systemic lupus erythematosus induced by active chromatin in BALB/c mice.

Induction of systemic lupus erythematosus syndrome in BALB/c mice by immunization with active chromatin.

AIM: To establish an animal model for systemic lupus erythematosus (SLE)-like syndrome in mice. METHODS: BALB/c mice were immunized with active chromatin isolated from ConA-activated syngeneic spleno-lymphocytes. Plasma samples of mice were tested by enzyme-linked immunosorbent assays (ELISA) for the presence of IgG anti-dsDNA, -ssDNA, and anti-histone antibodies. Tumor necrosis factor-alpha (TNF-alpha) in serum was measured by ELISA. Spleno-lymphocyte proliferation assays and the levels of interferon-gamma (IFN-gamma) in supernatants were tested respectively. Proteinuria was measured. Kidneys were examined by direct immunohistochemical method and light microscopy. RESULTS: Anti-ds DNA, ssDNA, and histone antibodies were induced in active chromatin-immunized mice, the proliferation response of splenocytes to ConA and LPS were reduced, levels of interferon-gamma in supernatants and TNF-alpha in serum were lowered. Lupus nephritis was assessed by the presence of Ig deposits, glomerular pathology and proteinuria. CONCLUSION: The active chromatin-induced SLE-like mouse model was similar to idiopathic SLE in human.