Ultrasound-Based Deep Learning Radiomics Nomogram for the Assessment of Lymphovascular Invasion in Invasive Breast Cancer: A Multicenter Study

RATIONALE AND OBJECTIVES

The aim of this study was to develop a deep learning radiomics nomogram (DLRN) based on B-mode ultrasound (BMUS) and color doppler flow imaging (CDFI) images for preoperative assessment of lymphovascular invasion (LVI) status in invasive breast cancer (IBC).

MATERIALS AND METHODS

In this multicenter, retrospective study, 832 pathologically confirmed IBC patients were recruited from eight hospitals. The samples were divided into training, internal test, and external test sets. Deep learning and handcrafted radiomics features reflecting tumor phenotypes on BMUS and CDFI images were extracted. The BMUS score and CDFI score were calculated after radiomics feature selection. Subsequently, a DLRN was developed based on the scores and independent clinic-ultrasonic risk variables. The performance of the DLRN was evaluated for calibration, discrimination, and clinical usefulness.

RESULTS

The DLRN predicted the LVI with accuracy, achieving an area under the receiver operating characteristic curve of 0.93 (95% CI 0.90-0.95), 0.91 (95% CI 0.87-0.95), and 0.91 (95% CI 0.86-0.94) in the training, internal test, and external test sets, respectively, with good calibration. The DLRN demonstrated superior performance compared to the clinical model and single scores across all three sets (p < 0.05). Decision curve analysis and clinical impact curve confirmed the clinical utility of the model. Furthermore, significant enhancements in net reclassification improvement (NRI) and integrated discrimination improvement (IDI) indicated that the two scores could serve as highly valuable biomarkers for assessing LVI.

CONCLUSION

The DLRN exhibited strong predictive value for LVI in IBC, providing valuable information for individualized treatment decisions.

[Simultaneous detection of 7 important Rickettsiales pathogens by TaqMan-probe quantitative real-time PCR]

Objective: To establish and optimize a TaqMan-probe quantitative real-time PCR (qPCR) assay for the detection of 7 important Rickettsiales pathogens and simultaneous identification of the infection types. Methods: Based on the ompB gene of Rickettsia prowazekii, Rickettsia mooseri and spotted fever group rickettsiae, the groEL gene of Orientia tsutsugamushi, the 16S rRNA of Ehrlichia chaffeensis, the gltA gene of Anaplasma phagocytophilum and the com1 gene of Coxiella burnetii, we synthesized primers and TaqMan-probes and optimized the reaction system and reaction process to same solution. The sensitivity, specificity and reproducibility of this assay were evaluated and the assay was used for the detection of simulated and actual samples. Results: The Ct value of the standard curves of the 7 pathogens showed a good linear relationship with the number of DNA copies (all R² >0.990 0), the minimum detection limit was 10 copies/μl, showing good specificity. In the 96 tick nucleic acid extracts, Coxiella burnetii was detected in 1 sampleand spotted fever group Rickettsiae was detected in 3 samples. In the 80 blood samples from patients with undefined febrile illness, Orientia tsutsugamushi was detected in 1 sample and spotted fever group rickettsiae was detected in 2 samples. Conclusions: In this study, based on the established TaqMan-probe qPCR assay, the reaction system and reaction condition of the 7 important pathogens of Rickettsiales were optimized to the same solution. This method overcomes the shortcomings of using different reaction systems and reaction conditions for different pathogens, which can precisely identify the species of 7 important pathogens of Rickettsiales in clinical sample detections and is important for the infection type identification and laboratory detection time reduction to facilitate precise treatment of the patients.

[Establishment of the Mathematical Model for PMI Estimation Using FTIR Spectroscopy and Data Mining Method]

OBJECTIVES

To analyse the relationship between Fourier transform infrared （FTIR） spectrum of rat's spleen tissue and postmortem interval （PMI） for PMI estimation using FTIR spectroscopy combined with data mining method.

METHODS

Rats were sacrificed by cervical dislocation, and the cadavers were placed at 20 ℃. The FTIR spectrum data of rats' spleen tissues were taken and measured at different time points. After pretreatment, the data was analysed by data mining method.

RESULTS

The absorption peak intensity of rat's spleen tissue spectrum changed with the PMI, while the absorption peak position was unchanged. The results of principal component analysis （PCA） showed that the cumulative contribution rate of the first three principal components was 96%. There was an obvious clustering tendency for the spectrum sample at each time point. The methods of partial least squares discriminant analysis （PLS-DA） and support vector machine classification （SVMC） effectively divided the spectrum samples with different PMI into four categories （0-24 h, 48-72 h, 96-120 h and 144-168 h）. The determination coefficient （R²） of the PMI estimation model established by PLS regression analysis was 0.96, and the root mean square error of calibration （RMSEC） and root mean square error of cross validation （RMSECV） were 9.90 h and 11.39 h respectively. In prediction set, the R² was 0.97, and the root mean square error of prediction （RMSEP） was 10.49 h.

CONCLUSIONS

The FTIR spectrum of the rat's spleen tissue can be effectively analyzed qualitatively and quantitatively by the combination of FTIR spectroscopy and data mining method, and the classification and PLS regression models can be established for PMI estimation.

Efficacy of contrast-enhanced ultrasound washout rate in predicting hepatocellular carcinoma differentiation

The aim of this retrospective study was to evaluate the efficacy of contrast-enhanced ultrasound (CEUS) washout rate in predicting hepatocellular carcinoma (HCC) differentiation. Two hundred seventy-one patients underwent liver resection for HCC between April 2008 and December 2012 after being examined by CEUS using the contrast agent SonoVue with a low mechanical index (<0.1) in a routine procedure. Contrast agent washout rates obtained from video images were divided into four categories from slow to fast: WR1 = no washout in all phases (slowest); WR2 = washout after 120 s from contrast injection (late-phase washout); WR3 = washout between 41 and 120 s from contrast injection (portal venous washout); WR4 = washout before 40 s from contrast injection (fastest washout rate). HCC nodules were graded as well, moderately and poorly differentiated. Spearman rank correlation and χ(2)-tests were used to assess group relationships and differences. Receiver operating characteristic curve analysis was used to determine the diagnostic predictive value of CEUS. Among the 271 patients, 18 (6.6%) had well differentiated, 150 (55.4%) had moderately differentiated and 103 (38.0%) had poorly differentiated HCC. Statistical tests indicated that washout rate was significantly correlated with tumor differentiation (p < 0.05), and the poorly differentiated HCCs had earlier washout. At the cutoff point of WR4, CEUS based on washout rate performed poorly in distinguishing poorly differentiated from moderately and well-differentiated HCCs, with a sensitivity, specificity and accuracy (area under the curve) of 24%, 97% and 0.68, respectively. However, at the cutoff point of WR2, the sensitivity, specificity and accuracy of CEUS in differentiating well-differentiated HCC from other HCCs were significantly better: 98%, 78% and 0.96, respectively. Thus, CEUS washout rate may have a role in identifying patients with well-differentiated HCC.

Analysis of neurotoxin 3-N-oxalyl-L-2,3-diaminopropionic acid and its alpha-isomer in Lathyrus sativus by high-performance liquid chromatography with 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate (AQC) derivatization

A method was developed for the quantitative determination of the neurotoxic nonprotein amino acid, 3-N-oxalyl-L-2,3-diaminopropionic acid (beta-ODAP), and its nontoxic alpha-isomer, 2-N-oxalyl-L-2, 3-diaminopropionic acid (alpha-ODAP), in the plant samples of Lathyrus sativus after derivatization with 6-aminoquinolyl-N-hydroxysuccinimidyl carbamate (AQC) by reversed-phase high-performance liquid chromatography (HPLC). Hippuric acid was used as an internal standard. A linear response was recorded in the concentration rang 0.32-32 nmol with r > 0.999. The RP HPLC detection limit for both isomers is 1.8 ng. According to different experimental needs, a ternary gradient system can be used to determine toxin and other nonprotein amino acids. The RP HPLC method and a colorimetric method were compared for measuring ODAP.