A multicenter proof-of-concept study on deep learning-based intraoperative discrimination of primary central nervous system lymphoma.

Accurate intraoperative differentiation of primary central nervous system lymphoma (PCNSL) remains pivotal in guiding neurosurgical decisions. However, distinguishing PCNSL from other lesions, notably glioma, through frozen sections challenges pathologists. Here we sought to develop and validate a deep learning model capable of precisely distinguishing PCNSL from non-PCNSL lesions, especially glioma, using hematoxylin and eosin (H&E)-stained frozen whole-slide images. Also, we compared its performance against pathologists of varying expertise. Additionally, a human-machine fusion approach integrated both model and pathologic diagnostics. In external cohorts, LGNet achieved AUROCs of 0.965 and 0.972 in distinguishing PCNSL from glioma and AUROCs of 0.981 and 0.993 in differentiating PCNSL from non-PCNSL lesions. Outperforming several pathologists, LGNet significantly improved diagnostic performance, further augmented to some extent by fusion approach. LGNet's proficiency in frozen section analysis and its synergy with pathologists indicate its valuable role in intraoperative diagnosis, particularly in discriminating PCNSL from glioma, alongside other lesions.

An interpretable deep learning model for identifying the morphological characteristics of dMMR/MSI-H gastric cancer.

Accurate tumor diagnosis by pathologists relies on identifying specific morphological characteristics. However, summarizing these unique morphological features in tumor classifications can be challenging. Although deep learning models have been extensively studied for tumor classification, their indirect and subjective interpretation obstructs pathologists from comprehending the model and discerning the morphological features accountable for classifications. In this study, we introduce a new approach utilizing Style Generative Adversarial Networks, which enables a direct interpretation of deep learning models to detect significant morphological characteristics within datasets representing patients with deficient mismatch repair/microsatellite instability-high gastric cancer. Our approach effectively identifies distinct morphological features crucial for tumor classification, offering valuable insights for pathologists to enhance diagnostic accuracy and foster professional growth.

Elaboration and Validation of a Nomogram Based on Axillary Ultrasound and Tumor Clinicopathological Features to Predict Axillary Lymph Node Metastasis in Patients With Breast Cancer.

Background: This study aimed at constructing a nomogram to predict axillary lymph node metastasis (ALNM) based on axillary ultrasound and tumor clinicopathological features. Methods: A retrospective analysis of 281 patients with pathologically confirmed breast cancer was performed between January 2015 and March 2018. All patients were randomly divided into a training cohort (n = 197) and a validation cohort (n = 84). Univariate and multivariable logistic regression analyses were performed to identify the clinically important predictors of ALNM when developin1 g the nomogram. The area under the curve (AUC), calibration plots, and decision curve analysis (DCA) were used to assess the discrimination, calibration, and clinical utility of the nomogram. Results: In univariate and multivariate analyses, lymphovascular invasion (LVI), axillary lymph node (ALN) cortex thickness, and an obliterated ALN fatty hilum were identified as independent predictors and integrated to develop a nomogram for predicting ALNM. The nomogram showed favorable sensitivity for ALNM with AUCs of 0.87 (95% confidence interval (CI), 0.81-0.92) and 0.84 (95% CI, 0.73-0.92) in the training and validation cohorts, respectively. The calibration plots of the nomogram showed good agreement between the nomogram prediction and actual ALNM diagnosis (P > 0.05). Decision curve analysis (DCA) revealed the net benefit of the nomogram. Conclusions: This study developed a nomogram based on three daily available clinical parameters, with good accuracy and clinical utility, which may help the radiologist in decision-making for ultrasound-guided fine needle aspiration cytology/biopsy (US-FNAC/B) according to the nomogram score.

Immunosuppressive Tumor Microenvironment and Immunotherapy of Epstein-Barr Virus-Associated Malignancies.

The Epstein-Barr virus (EBV) can cause different types of cancer in human beings when the virus infects different cell types with various latent patterns. EBV shapes a distinct and immunosuppressive tumor microenvironment (TME) to its benefit by influencing and interacting with different components in the TME. Different EBV-associated malignancies adopt similar but slightly specific immunosuppressive mechanisms by encoding different EBV products to escape both innate and adaptive immune responses. Strategies reversing the immunosuppressive TME of EBV-associated malignancies have been under evaluation in clinical practice. As the interactions among EBV, tumor cells, and TME are intricate, in this review, we mainly discuss the epidemiology of EBV, the life cycle of EBV, the cellular and molecular composition of TME, and a landscape of different EBV-associated malignancies and immunotherapy by targeting the TME.

A deep learning model and human-machine fusion for prediction of EBV-associated gastric cancer from histopathology.

Epstein-Barr virus-associated gastric cancer (EBVaGC) shows a robust response to immune checkpoint inhibitors. Therefore, a cost-efficient and accessible tool is needed for discriminating EBV status in patients with gastric cancer. Here we introduce a deep convolutional neural network called EBVNet and its fusion with pathologists for predicting EBVaGC from histopathology. The EBVNet yields an averaged area under the receiver operating curve (AUROC) of 0.969 from the internal cross validation, an AUROC of 0.941 on an external dataset from multiple institutes and an AUROC of 0.895 on The Cancer Genome Atlas dataset. The human-machine fusion significantly improves the diagnostic performance of both the EBVNet and the pathologist. This finding suggests that our EBVNet could provide an innovative approach for the identification of EBVaGC and may help effectively select patients with gastric cancer for immunotherapy.

C-Reactive Protein Levels Predict Responses to PD-1 Inhibitors in Hepatocellular Carcinoma Patients.

Background: Serum C-reactive protein (CRP) is a biomarker of an acute inflammatory response and has been successfully used as a prognostic predictor for several malignancies. However, the clinicopathological significance of CRP levels in hepatocellular carcinoma (HCC) patients being treated with PD-1 inhibitors remains unclear. Methods: Serum CRP levels were measured for a total of 101 HCC patients that had been treated with PD-1 inhibitors from July 2018 to November 2019. The clinicopathological data was retrospectively analyzed to identify any clinical implications between CRP levels and responses to PD-1 inhibitors and patients' progression-free survival (PFS). Results: The median PFS was 8.87 months in the CRP-low subgroup and 3.67 months in the CRP-high subgroup (P = 0.009). Univariate and multivariate Cox regression analysis demonstrated that both serum CRP and AFP levels were independent risk factors for the PFS of HCC patients treated with PD-1 inhibitors (P < 0.05). Moreover, Cox regression analysis after Propensity Score Matching showed the similar results. A prognostic model combining CRP and AFP levels could significantly stratify HCC patients receiving PD-1 inhibitors into low-, intermediate-, and high-risk subgroups (P < 0.001). Patients in the risk subgroups reported similar overall response rates (P = 0.625) and significantly different disease control rates (low- vs. intermediate- vs. high-risk groups: 81.6% vs. 65.1% vs. 35%, respectively, P = 0.002). Conclusions: The results of this study support the association between high serum CRP levels with the response and PFS for HCC patients receiving PD-1 inhibitors. Furthermore, the levels of both CRP and AFP in an HCC patient before treatment initiation show great potential for determining the efficacy of PD-1 inhibitors.

Quantitative analysis of shear wave elastic heterogeneity for prediction of lymphovascular invasion in breast cancer.

OBJECTIVE: To evaluate the correlation between elastic heterogeneity (EH) and lymphovascular invasion (LVI) in breast cancers and assess the clinical value of using EH to predict LVI pre-operatively. METHODS: This retrospective study consisted of 376 patients with breast cancers that had undergone shear wave elastography (SWE) with virtual touch tissue imaging quantification between June 2017 and June 2018. The EH was determined as the difference between the averaged three highest and three lowest shear wave value. Clinicalpathological parameters including histological type and grades, LVI, axillary lymph node status and molecular markers (estrogen receptor, progesterone receptor, human epidermal growth factor receptor 2 and Ki-67) were reviewed and recorded. Relationship EH and clinicalpathological parameters was investigated respectively. The diagnostic performance of EH in distinguishing LVI or not was analyzed. RESULTS: At multivariate regression analysis, only EH (p = 0.017) was positively correlated with LVI in all tumors. EH (p = 0.003) and Ki-67 (p = 0.025) were positively correlated with LVI in tumors ≤ 2 cm. None of clinicalpathological parameters were correlated with LVI in tumors > 2 cm (p > 0.05 for all). Using EH to predict LVI in tumors ≤ 2 cm, the sensitivity and negative predictive value were 93 and 89% respectively. CONCLUSION: EH has the potential to be served as an imaging biomarker to predict LVI in breast cancer especially for tumors ≤ 2 cm. ADVANCES IN KNOWLEDGE: There was no association between LVI and other most commonly used elastic features such as SWVmean and SWVmax. Elastic heterogeneity is an independent predictor of LVI, so it can provide additional prognostic information for routine preoperative breast cancer assessment.For tumors ≤ 2cm, using EH value higher than 1.36 m/s to predict LVI involvement, the sensitivity and negative predictive value can reach to 93% and 89%, respectively, suggesting that breast cancer with negative EH value was more likely to be absent of LVI.

Deep learning radiomics can predict axillary lymph node status in early-stage breast cancer.

Accurate identification of axillary lymph node (ALN) involvement in patients with early-stage breast cancer is important for determining appropriate axillary treatment options and therefore avoiding unnecessary axillary surgery and complications. Here, we report deep learning radiomics (DLR) of conventional ultrasound and shear wave elastography of breast cancer for predicting ALN status preoperatively in patients with early-stage breast cancer. Clinical parameter combined DLR yields the best diagnostic performance in predicting ALN status between disease-free axilla and any axillary metastasis with areas under the receiver operating characteristic curve (AUC) of 0.902 (95% confidence interval [CI]: 0.843, 0.961) in the test cohort. This clinical parameter combined DLR can also discriminate between low and heavy metastatic burden of axillary disease with AUC of 0.905 (95% CI: 0.814, 0.996) in the test cohort. Our study offers a noninvasive imaging biomarker to predict the metastatic extent of ALN for patients with early-stage breast cancer.

Shear-Wave Elastography of the Breast: Added Value of a Quality Map in Diagnosis and Prediction of the Biological Characteristics of Breast Cancer.

OBJECTIVE: To determine the added value of a shear-wave elastography (SWE) quality map (QM) in the diagnosis of breast lesions and in predicting the biological characteristics of invasive breast cancer. MATERIALS AND METHODS: Between January 2016 and February 2019, this study included 368 women with 368 pathologically proven breast lesions, which appeared as poor-quality regions in the QM of SWE. To measure shear-wave velocity (SWV), seven regions of interest were placed in each lesion with and without QM guidance. Under QM guidance, poor-quality areas were avoided. Diagnostic performance was calculated for mean SWV (SWVmean), max SWV (SWVmax), and standard deviation (SD) with QM guidance (SWVmean + QM, SWVmax + QM, and SD + QM, respectively) and without QM guidance (SWVmean - QM, SWVmax - QM, and SD - QM, respectively). For invasive cancers, the relationship between SWV findings and biological characteristics was investigated with and without QM guidance. RESULTS: Of the 368 women (mean age, 47 years; SD, 10.8 years) enrolled, 159 had benign breast lesions and 209 had malignant breast lesions. SWVmean + QM (3.6 ± 1.39 m/s) and SD + QM (1.02 ± 0.84) were significantly different from SWVmean - QM (3.29 ± 1.22 m/s) and SD - QM (1.46 ± 1.06), respectively (all p < 0.001). For differential diagnosis of breast lesions, the sensitivity and areas under the receiver operating characteristic curve (AUC) of SWVmean + QM (sensitivity: 89%; AUC: 0.932) were better than those of SWVmean - QM (sensitivity, 84.2%; AUC, 0.912) (all p < 0.05). There was no significant difference in sensitivity and specificity between SD + QM and SD - QM (all p = 1.000). Among the biological characteristics of invasive cancers, lymphovascular involvement, axillary lymph node metastasis, negative estrogen receptor status, negative progesterone receptor status, positive human epidermal growth factor receptor status, and aggressive molecular subtypes showed higher SWVmean + QM (all p < 0.05), while only lymphovascular involvement showed higher SWVmean - QM (p = 0.036). CONCLUSION: The use of QM in SWE might improve the diagnostic performance for breast lesions and facilitate prediction of the biological characteristics of invasive breast cancers.

Added Value of Different Types of Elastography in Evaluating Ultrasonography Detected Breast Lesions: A Compared Study With Mammography.

BACKGROUND: The purpose of this study was to compare the diagnostic performance of ultrasonography (US) and mammography in the differential diagnosis of breast lesions after adding different types of elastography to US. PATIENTS AND METHODS: This institutional review board-approved study included 316 breast lesions in 289 women between July 2016 and July 2018. All these lesions were evaluated with conventional US, elastography, and mammography before biopsy or surgery. Elastography, including elasticity imaging (EI), virtual touch tissue imaging (VTI), and virtual touch imaging quantification (VTIQ), were used to downgrade US Breast Imaging-Reporting and Data System category 4A lesions. Diagnostic performances were calculated for mammography, US elastography, and the combination of US and elastography. RESULTS: The sensitivity of US (100%) was significantly higher than that of mammography (84.6%; P < .001), but the specificity of US (14.5%) was significantly lower than that of mammography (59.1%; P < .001). After adding EI, VTI, and VTIQ to US, the specificity was significantly increased from 14.5% to 69.4%, 72.6%, and 78.0%, respectively (P < .001), and were significantly higher than that of mammography (P = .043, P = .006, and P < .001, respectively). The sensitivity of US + EI (96.2%) and US + VTI (96.2%) was lower than that of US alone, although not significantly (100%; P = .063 and P = .063, respectively). CONCLUSION: The addition of different types of elastography to US improved the diagnostic performance in the differential diagnosis of breast lesions when compared with mammography.

Distinguishing intrahepatic cholangiocarcinoma from hepatocellular carcinoma in patients with and without risks: the evaluation of the LR-M criteria of contrast-enhanced ultrasound liver imaging reporting and data system version 2017.

PURPOSE: To assess the diagnostic performance of the LR-M criteria of Contrast-Enhanced Ultrasound Liver Imaging Reporting and Data System version 2017 in differentiating intrahepatic cholangiocarcinoma (ICC) from hepatocellular carcinoma (HCC) in patients with and without risk factors for HCC. METHODS: Fifty-four ICC in patients with risks and 55 ICC in patients without risks and matched control cases of HCC with and without risks (n = 59 and n = 55, respectively) were enrolled. The enhanced features of the lesions were retrospectively analyzed according to LR-M criteria. The diagnostic performances including the area under the receiver operating characteristic curve (AUC), sensitivity, and specificity of LR-M criteria were assessed. RESULT: Peripheral rim-like hyperenhancement, early washout (< 45 or 60s), and marked washout did not differ between ICCs with and without risks, while all of these features were more common in ICCs than in HCCs (p < 0.05) no matter if patients were with and without risk factors. Using the LR-M criteria to differentiate ICC from HCC, the AUC, sensitivity, specificity, and accuracy were 0.92, 97.25%, 87.72%, and 92.38%, respectively. If early washout onset was adjusted to < 45 s, the specificity was significantly increased to 95.61% (p = 0.004) without losing sensitivity (96.33%, p = 0.945). The rate of HCCs misdiagnosed as ICCs would decrease from 12.3 to 4.4%. CONCLUSION: Although the LR-M criteria showed high sensitivity in distinguishing ICCs from HCCs in patients with and without risks, the specificity would be significantly increased after adjustments to current criteria. KEY POINTS: • The LR-M criteria of CEUS-LI-RADS v2017 could be used for distinguishing ICC from HCC not only in patients with risk factors for HCC but also in those without risk factors. • The diagnostic performance of differentiating ICC from HCC by using the LR-M criteria showed high AUC (0.92), high sensitivity (97.25%), intermediate specificity (87.72%), and high accuracy (92.38%). • If the onset of early washout was adjusted to < 45 s, the specificity was significantly increased from 87.72 to 95.61% (p = 0.004) without losing sensitivity (p = 0.945), and the rate of HCCs misdiagnosed as ICCs would decrease from 12.3 to 4.4%.

Early differentiating between the chemotherapy responders and nonresponders: preliminary results with ultrasonic spectrum analysis of the RF time series in preclinical breast cancer models.

BACKGROUND: This study was aimed to assess whether ultrasonic spectrum analysis of radiofrequency (RF) time series using a clinical ultrasound system allows for early differentiating between the chemotherapy responders and nonresponders in human breast cancer xenografts that imitate clinical responding and nonresponding tumors. METHODS: Clinically responding (n = 20; MCF-7) and nonresponding (n = 20; MBA-MD-231) breast cancer xenografts were established in 40 nude mice. Ten mice from each group received either chemotherapy (adriamycin, 4 mg/kg) or saline as controls. Each tumor was imaged longitudinally with a clinical ultrasound scanner at baseline (day 0) and subsequently on days 2, 4, 6, 8 and 12 following treatment, and the corresponding RF time-series data were collected. Changes in six RF time-series parameters (slope, intercept, S1, S2, S3 and S4) were compared with the measurement of the tumor cell density, and their differential performances of the treatment response were analyzed. RESULTS: Adriamycin significantly inhibited tumor growth and decreased the cancer cell density in responders (P < 0.001) but not in nonresponders (P > 0.05). Fold changes of slope were significantly increased in responders two days after adriamycin treatment (P = 0.002), but not in nonresponders (P > 0.05). Early changes in slope on day 2 could differentiate the treatment response in 100% of both responders (95% CI, 62.9-100.0%) and nonresponders (95% CI, 88.4-100%). CONCLUSIONS: Ultrasonic RF time series allowed for the monitoring of the tumor response to chemotherapy and could further serve as biomarkers for early differentiating between the treatment responders and nonresponders.

Combination of different types of elastography in downgrading ultrasound Breast Imaging-Reporting and Data System category 4a breast lesions.

OBJECTIVES: To determine whether a combination of different types of elastography could improve the accuracy of elastography-aided downgrading ultrasound (US) Breast Imaging-Reporting and Data System (BI-RADS) category 4a lesions. MATERIALS AND METHODS: From January 2016 to May 2018, 458 consecutive women with 494 US BI-RADS category 4a breast lesions were enrolled in the prospective study. These lesions were subject to conventional US supplemented with strain elastography of elasticity imaging (EI), virtual touch tissue imaging (VTI), and shear wave elastography of virtual touch imaging quantification (VTIQ). Diagnostic performances were calculated for BI-RADS, EI, VTI, and VTIQ as well as the combination of EI, VTI, and VTIQ (combination of EI and VTI [EI + VTI], combination of EI and VTIQ [EI + VTIQ], and combination of VTI and VTIQ [VTI + VTIQ]). RESULTS: Pathologically, 445 lesions (90.1%) were benign, and 49 (9.9%) were malignant. The specificities of EI, VTI, and VTIQ were significantly higher than those of BI-RADS (69.9%, 83.8%, 75.5% vs. 0, respectively, P < 0.001), while their sensitivities were significantly lower than those of BI-RADS (83.7%, 73.5%, 65.3% vs. 100%, respectively, P < 0.05). Among the combinations, EI + VTI and EI + VTIQ showed similar sensitivity to BI-RADS (98% vs 100%, P = 1.000; 93.9% vs 100%, P = 0.25), while the specificity of EI + VTI was significantly higher than that of EI + VTIQ and BI-RADS (P < 0.001). When using EI + VTI to downgrade lesions, 58.7% of these lesions were downgraded, among those 99.7% were benign. CONCLUSIONS: Combinations of EI and VTI to downgrade BI-RADS category 4a lesions may reduce the misdiagnosis of breast cancers and the number of unnecessary biopsies.

Ultraviolet A irradiation inhibits thymus and activation-regulated chemokine (TARC/CCL17) production by a human keratinocyte HaCaT cell line.

Ultraviolet A (UVA) irradiation modulates the immunological functions of skin. We examined the effect of UVA irradiation on the basal and the IFN-gamma-and TNF-alpha-stimulation-induced production of thymus-and activation-regulated chemokine (TARC/CCL17) using HaCaT cells. UVA irradiation inhibited the basal levels of both TARC mRNA expression and TARC protein production. UVA irradiation also significantly inhibited TARC mRNA expression and TARC protein secretion that were induced by co-stimulation with IFN-y and TNF-alpha. A time course study showed that: the significant suppression of TARC mRNA expression was detected 8 hours after irradiation and continued for 36 hours; the strongest inhibition of TARC protein secretion occurred in the first 8 hours after UVA irradiation and continued for 36 hours. Our data provide the first evidence that UVA inhibits TARC mRNA expression and TARC protein production by keratinocytes in a dose-dependent manner. These results may suggest an explanation for the UV-induced therapeutic effect.

TGF-beta1-mediated regulation of thymus and activation-regulated chemokine (TARC/CCL17) synthesis and secretion by HaCaT cells co-stimulated with TNF-alpha and IFN-gamma.

Thymus and activation-regulated chemokine (TARC/CCL17) contributes not only to the recruitment of leukocytes, but is also involved in immune disorders, such as atopic dermatitis (AD) and bronchial asthma. We have previously reported that the levels of TARC were high in patients with AD and that lesional epidermis were strongly immunoreactive for TARC. In this paper, the effects of transforming growth factor (TGF)-beta(1) on the expression of TARC/CCL17 were examined in HaCaT cells, a human keratinocytes (KCs) cell line, co-stimulated with TNF-alpha and IFN-gamma. We found that TGF-beta(1) down-regulated the TARC synthesis and secretion of HaCaT cells co-stimulated with TNF-alpha and IFN-gamma in a dose-dependent manner. TGF-beta(1) at a concentration of 10ng/ml maximally inhibited this secretion. Northern blot analysis showed a similar inhibitory effect of TGF-beta(1) on TARC mRNA expression by HaCaT cells. The TGF-beta(1)-induced down-regulation of TARC/CCL17 in HaCaT cells suggests that TGF-beta(1) might regulate the TARC-related inflammatory processes, which may be important for understanding the pathogenesis of allergic diseases.