Image_3_Ultrasound radiomics-based artificial intelligence model to assist in the differential diagnosis of ovarian endometrioma and ovarian dermoid cyst.JPEG

BackgroundAccurately differentiating between ovarian endometrioma and ovarian dermoid cyst is of clinical significance. However, the ultrasound appearance of these two diseases is variable, occasionally causing confusion and overlap with each other. This study aimed to develop a diagnostic classification model based on ultrasound radiomics to intelligently distinguish and diagnose the two diseases.MethodsWe collected ovarian ultrasound images from participants diagnosed as patients with ovarian endometrioma or ovarian dermoid cyst. Feature extraction and selection were performed using the Mann-Whitney U-test, Spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO) regression. We then input the final features into the machine learning classifiers for model construction. A nomogram was established by combining the radiomic signature and clinical signature.ResultsA total of 407 participants with 407 lesions were included and categorized into the ovarian endometriomas group (n = 200) and the dermoid cyst group (n = 207). In the test cohort, Logistic Regression (LR) achieved the highest area under curve (AUC) value (0.981, 95% CI: 0.963−1.000), the highest accuracy (94.8%), and the highest sensitivity (95.5%), while LightGBM achieved the highest specificity (97.1%). A nomogram incorporating both clinical features and radiomic features achieved the highest level of performance (AUC: 0.987, 95% CI: 0.967−1.000, accuracy: 95.1%, sensitivity: 88.0%, specificity: 100.0%, PPV: 100.0%, NPV: 88.0%, precision: 93.6%). No statistical difference in diagnostic performance was observed between the radiomic model and the nomogram (P > 0.05). The diagnostic indexes of radiomic model were comparable to that of senior radiologists and superior to that of junior radiologist. The diagnostic performance of junior radiologists significantly improved with the assistance of the model.ConclusionThis ultrasound radiomics-based model demonstrated superior diagnostic performance compared to those of junior radiologists and comparable diagnostic performance to those of senior radiologists, and it has the potential to enhance the diagnostic performance of junior radiologists.</p

Image_2_Ultrasound radiomics-based artificial intelligence model to assist in the differential diagnosis of ovarian endometrioma and ovarian dermoid cyst.JPEG

BackgroundAccurately differentiating between ovarian endometrioma and ovarian dermoid cyst is of clinical significance. However, the ultrasound appearance of these two diseases is variable, occasionally causing confusion and overlap with each other. This study aimed to develop a diagnostic classification model based on ultrasound radiomics to intelligently distinguish and diagnose the two diseases.MethodsWe collected ovarian ultrasound images from participants diagnosed as patients with ovarian endometrioma or ovarian dermoid cyst. Feature extraction and selection were performed using the Mann-Whitney U-test, Spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO) regression. We then input the final features into the machine learning classifiers for model construction. A nomogram was established by combining the radiomic signature and clinical signature.ResultsA total of 407 participants with 407 lesions were included and categorized into the ovarian endometriomas group (n = 200) and the dermoid cyst group (n = 207). In the test cohort, Logistic Regression (LR) achieved the highest area under curve (AUC) value (0.981, 95% CI: 0.963−1.000), the highest accuracy (94.8%), and the highest sensitivity (95.5%), while LightGBM achieved the highest specificity (97.1%). A nomogram incorporating both clinical features and radiomic features achieved the highest level of performance (AUC: 0.987, 95% CI: 0.967−1.000, accuracy: 95.1%, sensitivity: 88.0%, specificity: 100.0%, PPV: 100.0%, NPV: 88.0%, precision: 93.6%). No statistical difference in diagnostic performance was observed between the radiomic model and the nomogram (P > 0.05). The diagnostic indexes of radiomic model were comparable to that of senior radiologists and superior to that of junior radiologist. The diagnostic performance of junior radiologists significantly improved with the assistance of the model.ConclusionThis ultrasound radiomics-based model demonstrated superior diagnostic performance compared to those of junior radiologists and comparable diagnostic performance to those of senior radiologists, and it has the potential to enhance the diagnostic performance of junior radiologists.</p

Image_1_Ultrasound radiomics-based artificial intelligence model to assist in the differential diagnosis of ovarian endometrioma and ovarian dermoid cyst.JPEG

BackgroundAccurately differentiating between ovarian endometrioma and ovarian dermoid cyst is of clinical significance. However, the ultrasound appearance of these two diseases is variable, occasionally causing confusion and overlap with each other. This study aimed to develop a diagnostic classification model based on ultrasound radiomics to intelligently distinguish and diagnose the two diseases.MethodsWe collected ovarian ultrasound images from participants diagnosed as patients with ovarian endometrioma or ovarian dermoid cyst. Feature extraction and selection were performed using the Mann-Whitney U-test, Spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO) regression. We then input the final features into the machine learning classifiers for model construction. A nomogram was established by combining the radiomic signature and clinical signature.ResultsA total of 407 participants with 407 lesions were included and categorized into the ovarian endometriomas group (n = 200) and the dermoid cyst group (n = 207). In the test cohort, Logistic Regression (LR) achieved the highest area under curve (AUC) value (0.981, 95% CI: 0.963−1.000), the highest accuracy (94.8%), and the highest sensitivity (95.5%), while LightGBM achieved the highest specificity (97.1%). A nomogram incorporating both clinical features and radiomic features achieved the highest level of performance (AUC: 0.987, 95% CI: 0.967−1.000, accuracy: 95.1%, sensitivity: 88.0%, specificity: 100.0%, PPV: 100.0%, NPV: 88.0%, precision: 93.6%). No statistical difference in diagnostic performance was observed between the radiomic model and the nomogram (P > 0.05). The diagnostic indexes of radiomic model were comparable to that of senior radiologists and superior to that of junior radiologist. The diagnostic performance of junior radiologists significantly improved with the assistance of the model.ConclusionThis ultrasound radiomics-based model demonstrated superior diagnostic performance compared to those of junior radiologists and comparable diagnostic performance to those of senior radiologists, and it has the potential to enhance the diagnostic performance of junior radiologists.</p

DHA selectively blocked activation of STAT3 under different conditions in HNSCC cells.

<p>(A) DHA blocked constitutive activation of STAT3. FaDu, Cal-27, and Hep-2 cells incubated with indicated concentrations of DHA or DMSO for 24 h (upper panel), or treated with fixed concentrations (160 μM for Fadu cells, and 80μM for Cal-27 and Hep-2 cells) of DHA for 0, 12, 24, and 48 h (lower panel). Expression of p-STAT3 was determined by Western blotting. (B) DHA inhibited hypoxia-induced activation of STAT3. Three HNSCC cell lines were treated with DHA (160 μM for Fadu, and 80μM for Cal-27 and Hep-2) under hypoxia for 24 h. Levels of p-STAT3and HIF-1α were determined by Western blotting. (C) DHA blocked IL-6-induced activation of STAT3. HNSCC cells were treated with160 μM DHA (Fadu) or 80 μM DHA (Cal-27 and Hep-2) for 24h and exposed to IL-6 (20ng) for 1 h. Levels ofp-Jak2 and p-STAT3 were evaluated by Western blotting. (D) DHA inhibited STAT3 activation in vivo. Tumor-bearing mice were treated with DHA as described in the materials and methods. Expression of p-STAT3 in representative tumor tissues of experimental and control animals was evaluated by Western blotting. All experiments were performed in triplicates.</p

DHA synergistically potentiated cisplatin-induced proliferation inhibition and produced G0/G1 phase cell cycle arrest in HNSCC cells.

<p>(A) Fadu, Cal-27 and Hep-2 cells were treated with either DHA (10 and 20 μM), cisplatin (1, 5, and 10 μM), or a combination of both for 24 h. Proliferation inhibition was determined by MTT assay. Data expressed as means ± SD, **p<0.01. (B) Antiproliferative effects of drug synergy were determined by the CalcuSyn software (version 1.0). Combinations: point 1, DHA 10 μM and cisplatin (DDP) 1 μM; point 2, DHA 10 μM and cisplatin 5 μM; point 3, DHA 10 μM and cisplatin 10 μM; point 4, DHA 20 μM and cisplatin 1 μM; point 5, DHA 20 μM and cisplatin 5 μM; point 6, DHA 20 μM and cisplatin 10 μM. All experiments were performed in triplicates. (C) Cell cycle distribution patterns of FaDu, Cal-27 and Hep-2 cells were determined by flow cytometry after exposure to various concentrations of DHA for 24h. The proportions of cells in G1 were calculated. Data were expressed as means ± SD, *p<0.05, **p<0.01. All experiments were performed in triplicates.</p

DHA inhibited growth of HNSCC in vivo.

<p>(A) Cal-27 cells were used to establish xenograft tumors in BALB/c mice, and animals were treated with DHA or vehicle (DMSO) as described. Representative xenograft tumors from DHA-treated mice and vehicle-treated mice were presented. (B) Overall weight of the dissected tumors. Data were expressed as means ± SD (n = 7), **p<0.01. (C) The changes of mean tumor volume in DHA-treated mice and vehicle-treated mice. Data were expressed as means ± SD (n = 7), **p<0.01. (D) The dynamic body weight changes of tumor-bearing mice during DHA treatment. (E) Effects of DHA on the downstream proteins ofJAK2/STAT3 pathway in xenograft tumors as demonstrated by Western blotting. Data were expressed as means ± SD (n = 7), *p<0.05, **p<0.01. All experiments were performed in triplicates.</p

A schematic chart showing the pathways by which DHA inhibits STAT3 activation and signaling.

<p>A schematic chart showing the pathways by which DHA inhibits STAT3 activation and signaling.</p

DHA resulted in proliferation inhibition and migration, and induced apoptosis in HNSCC cells.

<p>(A) FaDu, Cal-27, and Hep-2 cells were treated with indicated concentrations of DHA for 24 or 48h, and cell viability was tested via MTT assay. IC50 values of DHA were calculated for the three cell lines. (B) DHA induced apoptosis in HNSCC cell. Three HNSCC cell lines were incubated with indicated concentrations of DHA for 24 h, followed by flow cytometric analysis with Annexin V-FITC and propidium iodide (PI) labeling. (C)DHA induced inhibition of migration of HNSCC cells. Cells were incubated with 40μM (FaDu) or 20μM (Cal-27and Hep-2) DHA or DMSO. The wound healing capacity was measured at 0, 12, 24, and 48 h. Data were expressed as means ± SD (left and middle panels). Simultaneous determination of levels of MMP-2, MMP-9 and p-STAT3 was conducted (right panel). (D) Effects of STAT3 inhibition by DHA on expression of downstream proteinsMcl-1, Bcl-xl, Cyclin-D1 and VEGF in HNSCC cells as determined by Western blotting. All experiments were performed in triplicates.</p

Comparison of inhibitory effect of DHA on STAT3 activation with that of other two available Jak/STAT3 inhibitors.

<p>Fadu, Cal-27 and Hep-2 cells were exposed to different concentrations ofDHA, AZD1480 and AG490 or DMSO for 24 h, after which time the expression of p-Jak2 and p-STAT3 were analyzed by Western blotting. The doses of these inhibitors were chosen based on a preliminary dose-escalation study (S3 Fig).</p

Inhibiton of STAT3 activation by DHA in HNSCC cells was mediated by selective blockade of Jak2 phosphorylation.

<p>(A) DHA inhibited STAT3 activation, but did not affect the expression of p-Erk1/2 and p-Akt. FaDu, Cal-27 and Hep-2 cells were treated with indicated concentrations of DHA or DMSO for 24 h. Western blotting was used to determine the levels of p-STAT3, p-Erk1/2 and p-Akt. (B) DHA inhibited expression of p-Jak2 and p-STAT3, but not of p-EGFR and p-SRC. FaDu, Cal-27 and Hep-2 cells were treated with indicated concentrations of DHA for 24h. Expression of the associated proteins was detected by western blotting. (C) DHA selectively blocked Jak2/STAT3 activation in vivo. Tumor-bearing mice were treated with DHA as described. Expression of p-EGFR, p-Jak2, p-SRC and p-STAT3 in tumor tissues was analyzed by Western blotting. (D) DHA specifically blocked Jak2 activation in HNSCC cells. Cal-27 cells were transfected with DN-EGFR, DN-Jak2, DN-SRC, CA-STAT3, or empty vector, and exposed to 80 μM DHA for 24 h. Expression of p-EGFR, p-Jak2, p-SRC and p-STAT3 were studied by Western blotting. (E) CA-STAT3attenuated the cell cycle arrest induced by DHA. Cal-27 cells were transfected withCA-STAT3or empty vector and exposed to 80 μM DHA for 24 h. Cell cycle and cell apoptosis were analyzed by flow cytometry. All experiments were performed in triplicates.</p