Comprehensive Potential of Artificial Intelligence for Predicting PD-L1 Expression and EGFR Mutations in Lung Cancer: A Systematic Review and Meta-Analysis.

OBJECTIVE: To evaluate the methodological quality and the predictive performance of artificial intelligence (AI) for predicting programmed death ligand 1 (PD-L1) expression and epidermal growth factor receptors (EGFR) mutations in lung cancer (LC) based on systematic review and meta-analysis. METHODS: AI studies based on PET/CT, CT, PET, and immunohistochemistry (IHC)-whole-slide image (WSI) were included to predict PD-L1 expression or EGFR mutations in LC. The modified Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool was used to evaluate the methodological quality. A comprehensive meta-analysis was conducted to analyze the overall area under the curve (AUC). The Cochrane diagnostic test and I2 statistics were used to assess the heterogeneity of the meta-analysis. RESULTS: A total of 45 AI studies were included, of which 10 were used to predict PD-L1 expression and 35 were used to predict EGFR mutations. Based on the analysis using the QUADAS-2 tool, 37 studies achieved a high-quality score of 7. In the meta-analysis of PD-L1 expression levels, the overall AUCs for PET/CT, CT, and IHC-WSI were 0.80 (95% confidence interval [CI], 0.77-0.84), 0.74 (95% CI, 0.69-0.77), and 0.95 (95% CI, 0.93-0.97), respectively. For EGFR mutation status, the overall AUCs for PET/CT, CT, and PET were 0.85 (95% CI, 0.81-0.88), 0.83 (95% CI, 0.80-0.86), and 0.75 (95% CI, 0.71-0.79), respectively. The Cochrane Diagnostic Test revealed an I2 value exceeding 50%, indicating substantial heterogeneity in the PD-L1 and EGFR meta-analyses. When AI was combined with clinicopathological features, the enhancement in predicting PD-L1 expression was not substantial, whereas the prediction of EGFR mutations showed improvement compared to the CT and PET models, albeit not significantly so compared to the PET/CT models. CONCLUSIONS: The overall performance of AI in predicting PD-L1 expression and EGFR mutations in LC has promising clinical implications.

Lymph node metastasis prediction and biological pathway associations underlying DCE-MRI deep learning radiomics in invasive breast cancer.

BACKGROUND: The relationship between the biological pathways related to deep learning radiomics (DLR) and lymph node metastasis (LNM) of breast cancer is still poorly understood. This study explored the value of DLR based on dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in LNM of invasive breast cancer. It also analyzed the biological significance of DLR phenotype based on genomics. METHODS: Two cohorts from the Cancer Imaging Archive project were used, one as the training cohort (TCGA-Breast, n = 88) and one as the validation cohort (Breast-MRI-NACT Pilot, n = 57). Radiomics and deep learning features were extracted from preoperative DCE-MRI. After dual selection by principal components analysis (PCA) and relief methods, radiomics and deep learning models for predicting LNM were constructed by the random forest (RF) method. A post-fusion strategy was used to construct the DLR nomograms (DLRNs) for predicting LNM. The performance of the models was evaluated using the receiver operating characteristic (ROC) curve and Delong test. In the training cohort, transcriptome data were downloaded from the UCSC Xena online database, and biological pathways related to the DLR phenotypes were identified. Finally, hub genes were identified to obtain DLR gene expression (RadDeepGene) scores. RESULTS: DLRNs were based on area under curve (AUC) evaluation (training cohort, AUC = 0.98; validation cohort, AUC = 0.87), which were higher than single radiomics models or GoogLeNet models. The Delong test (radiomics model, P = 0.04; GoogLeNet model, P = 0.01) also validated the above results in the training cohorts, but they were not statistically significant in the validation cohort. The GoogLeNet phenotypes were related to multiple classical tumor signaling pathways, characterizing the biological significance of immune response, signal transduction, and cell death. In all, 20 genes related to GoogLeNet phenotypes were identified, and the RadDeepGene score represented a high risk of LNM (odd ratio = 164.00, P < 0.001). CONCLUSIONS: DLRNs combining radiomics and deep learning features of DCE-MRI images improved the preoperative prediction of LNM in breast cancer, and the potential biological characteristics of DLRN were identified through genomics.

Imaging evaluation of para-aortic lymph nodes in cervical cancer.

BACKGROUND: In recent years, much literature has reported the diagnostic value of computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET)-CT in para-aortic lymph node metastasis of cervical cancer. PURPOSE: To compare and analyze the para-aortic lymph node presentations found in cervical cancer on different images in order to determine the best precise imaging method for identifying metastatic lymph nodes. MATERIAL AND METHODS: PubMed, Web of Science, MEDLINE, and other databases were searched for the non-invasive detection of metastatic lymph nodes for a comprehensive comparison. RESULTS: Positive lymph nodes on CT are significantly related to the following factors: short axis ≥10 mm; and round or central necrosis. Positive lymph nodes on MRI are significantly related to the following factors: short axis ≥8 mm; inhomogeneous signal intensity; morphology: round, irregular edge, extracapsular invasion, central necrosis, loss of lymph node structure, burrs, or lobes; and ADC value decreases, combined with local actuality. On PET-CT examination, when the short axis of the lymph node is >5 mm, the SUV is >2.5, or the FDG uptake is greater than that of the surrounding tissue, it is a metastatic lymph node. CONCLUSION: In conclusion, different imaging techniques show metastatic lymph nodes in different ways. Combining the patient's medical history with the symptoms of the aforementioned lymph nodes, together with one or more imaging techniques, is important to diagnose para-aortic lymph nodes in cervical cancer.

Case Report: Clinical application of immunotherapy-based combination regimen in primary osteosarcoma of the uterus.

Primary osteosarcoma of the uterus is an extremely rare pure heterologous sarcoma of the uterus. The relevant available information is limited to case reports. To date, only 31 cases of this type of cancer have been reported. Here, we report the first clinical experience with the administration of an immunotherapy-based combination regimen for multiple metastatic primary osteosarcomas of the uterus. The patient had undergone multiple treatments prior to this regimen, but her condition continued to progress. However, after 3 cycles of immunotherapy combined with targeted therapy and chemotherapy, a review showed that the disease was stable and even in partial remission. The patient has a good quality of life, and long-term survival is expected.

Synchronous multiple primary malignancies of clear cell renal cell carcinoma with sarcomatoid, thyroid carcinoma: a case report.

Multiple primary malignant neoplasms (MPMNs) are defined as the presence of two or more malignancies with different histologies in the same patient. MPMNs are rare, accounting for fewer than 4% of all tumor cases. Depending on the time interval between the diagnosis of the different malignancies, they are classified as either simultaneous or metachronous MPMNs, with simultaneous being rarer in MPMNs. Here, we present a 63-year-old female patient presenting with multiple primary renal and thyroid carcinomas and discuss the risk factors, treatment options, and prognosis of rare dual carcinomas. We focus on managing multidisciplinary teams and selecting individualized treatment options to deliver valuable treatment strategies to patients.

Feasibility evaluation of intravoxel incoherent motion diffusion-weighted imaging in the diagnosis of skull-base invasion in nasopharyngeal carcinoma.

Objective: This study aimed to evaluate the feasibility of intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) in the diagnosis of skull-base invasion (SBI) in nasopharyngeal carcinoma (NPC). Materials and methods: A total of 50 patients pathologically diagnosed with NPC and a group of 40 controls comprised of those with either normal nasopharynx or patients with nasopharyngitis underwent conventional MRI and IVIM-DWI scans with 3 different groups of b values. Among the 50 patients, 36 patients diagnosed with SBI in NPC were included in the case group according to SBI criteria. All subjects (including those in the control group and case group) were divided into the b1, b2, and b3 groups based on their b values. The pure diffusion coefficient (D), perfusion-related incoherent microcirculation (D*), and microvascular volume fraction (f) values obtained in each measurement area of each group were tested for variance. Next,2 groups of b-value parameters with statistically significant data in the 3 groups were randomly selected for use in both the control group and the case group. A t-test was performed on the D, D*, and f values obtained by measuring each area of the skull base, and the area under the curve (AUC) of the receiver operating characteristic (ROC) was used to evaluate the diagnostic efficacy of the D, D*, and f values. Results: There was no statistical significance among the D, D*, and f values of the b1 and b3 groups (P>0.05), and the differences in parameters between the b1 and b2 groups were statistically significant(P < 0.05),and the differences in parameters between the b3 and b2 groups were also statistically significant(P < 0.05).The f value of the case group, which was obtained using the b1 and b2 parameters in each area of the skull base, was lower than that of the control group (P <0.05).The D, D*, and f values of the case group obtained by the b1 and b2 parameters in the pars petrosa of the temporal bone (including the foramen lacerum) were lower than those of the control group (P<0.05).When the parameters of the b1 group were used in the corpus of sphenoid bone (including the foramen ovale), the D, D*, and f values of the control group and the case group were compared, yielding a statistically significant difference (P<0.05).When the parameters of the b1 group were used, the diagnostic efficacy of the f value in each area of the skull base was the highest (AUC=0.908-0.991), followed by the D* value (AUC=0.624-0.692). Conclusion: When the number of b values <200 s/mm2 in IVIM-DWI accounts for more than half of the selected b values, IVIM-DWI is highly stable for the diagnosis of SBI in NPC. The D, D*, and f values of the bone and muscle areas of the skull base in patients with SBI of NPC showed a downward trend, and the f value had the best diagnostic performance, followed by the D* value, while the D value had the worst. Thus, IVIM-DWI can be used as a noninvasive method in the diagnosis of SBI in NPC.