Objective: To construct and validate a nomogram model integrating the magnetic resonance imaging (MRI) radiomic features and the kinetic curve pattern for detecting metastatic axillary lymph node (ALN) in invasive breast cancer preoperatively. Materials and Methods: A total of 145 ALNs from two institutions were classified into negative and positive groups according to the pathologic or surgical results. One hundred one ALNs from institution I were taken as the training cohort, and the other 44 ALNs from institution II were taken as the external validation cohort. The kinetic curve was computed using dynamic contrast-enhanced MRI software. The preprocessed images were used for radiomic feature extraction. The LASSO regression was applied to identify optimal radiomic features and construct the Radscore. A nomogram model was constructed combining the Radscore and the kinetic curve pattern. The discriminative performance was evaluated by receiver operating characteristic analysis and calibration curve. Results: Five optimal features were ultimately selected and contributed to the Radscore construction. The kinetic curve pattern was significantly different between negative and positive lymph nodes. The nomogram model showed a better performance in both training cohort [area under the curve (AUC) = 0.91, 95% CI = 0.83-0.96] and external validation cohort (AUC = 0.86, 95% CI = 0.72-0.94); the calibration curve indicated a better accuracy of the nomogram model for detecting metastatic ALN than either Radscore or kinetic curve pattern alone. Conclusion: A nomogram model integrated the Radscore and the kinetic curve pattern could serve as a biomarker for detecting metastatic ALN in patients with invasive breast cancer.

Clinical treatment decision making of bladder cancer (BCa) relies on the absence or presence of muscle invasion and tumor staging. Deep learning (DL) is a novel technique in image analysis, but its potential for evaluating the muscular invasiveness of bladder cancer remains unclear. The purpose of this study was to develop and validate a DL model based on computed tomography (CT) images for prediction of muscle-invasive status of BCa.

A higher ratio of pretreatment C-reactive protein/albumin ratio (CAR) is associated with poor prognosis in nasopharyngeal carcinoma (NPC), and Epstein-Barr virus (EBV) DNA level is known to not only participate in the occurrence of nasopharyngeal carcinoma but also affect the development and prognosis of the disease. Herein, we proposed that a combination of both these markers could improve the predictive prognostic ability.

Metastatic carcinomas of bone marrow (MCBM) are characterized as tumors of non-hematopoietic origin spreading to the bone marrow through blood or lymphatic circulation. The diagnosis is critical for tumor staging, treatment selection and prognostic risk stratification. However, the identification of metastatic carcinoma cells on bone marrow aspiration smears is technically challenging by conventional microscopic screening.

To evaluate the value of tumor-educated platelet (TEP) miR-18a-3p in the early diagnosis and chemotherapy efficacy monitoring of nasopharyngeal carcinoma (NPC).

Purpose: To investigate the associations of metabolite levels derived from magnetic resonance spectroscopic imaging (MRSI) and 18F-fluciclovine positron emission tomography (PET) with prostate tissue characteristics. Methods: In a cohort of 19 high-risk prostate cancer patients that underwent simultaneous PET/MRI, we evaluated the diagnostic performance of MRSI and PET for discrimination of aggressive cancer lesions from healthy tissue and benign lesions. Data analysis comprised calculations of correlations of mean standardized uptake values (SUVmean), maximum SUV (SUVmax), and the MRSI-derived ratio of (total choline + spermine + creatine) to citrate (CSC/C). Whole-mount histopathology was used as gold standard. Results: The results showed a moderate significant correlation between both SUVmean and SUVmax with CSC/C ratio. Conclusions: We demonstrated that the simultaneous acquisition of 18F-fluciclovine PET and MRSI with an integrated PET/MRI system is feasible and a combination of these imaging modalities has potential to improve the diagnostic sensitivity and specificity of prostate cancer lesions.

To establish a nomogram based on preoperative laboratory study variables using least absolute shrinkage and selection operator (LASSO) regression for differentiating combined hepatocellular cholangiocarcinoma (cHCC) from intrahepatic cholangiocarcinoma (iCCA).

To investigate the expression of carcinoembryonic antigen (CEA), cancer antigen 125 (CA125), CA19-9, CA724, and CA242 in serum and ascites of pseudomyxoma peritonei (PMP) patients and evaluate the predictive value of these elevated biomarkers in pathological grade, completeness of cytoreduction (CC), and survival.

Several surgical risk models are widely utilized in general surgery to predict postoperative morbidity. However, no studies have been undertaken to examine the predictive efficacy of these models in biliary tract cancer patients, and other perioperative variables can also influence morbidity. As a result, the study's goal was to examine these models alone, as well as risk models combined with disease-specific factors, in predicting severe complications.

Purpose: This study aimed to explore the role of delta-radiomics in differentiating pre-invasive ground-glass nodules (GGNs) from invasive GGNs, compared with radiomics signature. Materials and Methods: A total of 464 patients including 107 pre-invasive GGNs and 357 invasive GGNs were embraced in radiomics signature analysis. 3D regions of interest (ROIs) were contoured with ITK software. By means of ANOVA/MW, correlation analysis, and LASSO, the optimal radiomic features were selected. The logistic classifier of radiomics signature was constructed and radiomic scores (rad-scores) were calculated. A total of 379 patients including 48 pre-invasive GGNs and 331 invasive GGNs with baseline and follow-up CT examinations before surgeries were enrolled in delta-radiomics analysis. Finally, the logistic classifier of delta-radiomics was constructed. The receiver operating characteristic curves (ROCs) were built to evaluate the validity of classifiers. Results: For radiomics signature analysis, six features were selected from 396 radiomic features. The areas under curve (AUCs) of logistic classifiers were 0.865 (95% CI, 0.823-0.900) in the training set and 0.800 (95% CI, 0.724-0.863) in the testing set. The rad-scores of invasive GGNs were larger than those of pre-invasive GGNs. As the follow-up interval went on, more and more delta-radiomic features became statistically different. The AUC of the delta-radiomics logistic classifier was 0.901 (95% CI, 0.867-0.928), which was higher than that of the radiomics signature. Conclusion: The radiomics signature contributes to distinguish pre-invasive and invasive GGNs. The rad-scores of invasive GGNs were larger than those of pre-invasive GGNs. More and more delta-radiomic features appeared to be statistically different as follow-up interval prolonged. Delta-radiomics is superior to radiomics signature in differentiating pre-invasive and invasive GGNs.

Background: The management of ground glass nodules (GGNs) remains a distinctive challenge. This study is aimed at comparing the predictive growth trends of radiomic features against current clinical features for the evaluation of GGNs. Methods: A total of 110 GGNs in 85 patients were included in this retrospective study, in which follow up occurred over a span ≥2 years. A total of 396 radiomic features were manually segmented by radiologists and quantitatively analyzed using an Analysis Kit software. After feature selection, three models were developed to predict the growth of GGNs. The performance of all three models was evaluated by a receiver operating characteristic (ROC) curve. The best performing model was also assessed by calibration and clinical utility. Results: After using a stepwise multivariate logistic regression analysis and dimensionality reduction, the diameter and five specific radiomic features were included in the clinical model and the radiomic model. The rad-score [odds ratio (OR) = 5.130; P < 0.01] and diameter (OR = 1.087; P < 0.05) were both considered as predictive indicators for the growth of GGNs. Meanwhile, the area under the ROC curve of the combined model reached 0.801. The high degree of fitting and favorable clinical utility was detected using the calibration curve with the Hosmer-Lemeshow test and the decision curve analysis was utilized for the nomogram. Conclusions: A combined model using the current clinical features alongside the radiomic features can serve as a powerful tool to assist clinicians in guiding the management of GGNs.

Objective: To develop and validate a radiomics nomogram for preoperative prediction of tumor necrosis in patients with clear cell renal cell carcinoma (ccRCC). Methods: In total, 132 patients with pathologically confirmed ccRCC in one hospital were enrolled as a training cohort, while 123 ccRCC patients from second hospital served as the independent validation cohort. Radiomic features were extracted from corticomedullary and nephrographic phase contrast-enhanced computed tomography (CT) images. A radiomics signature based on optimal features selected by consistency analysis and the least absolute shrinkage and selection operator was developed. An image features model was constructed based on independent image features according to visual assessment. By integrating the radiomics signature and independent image features, a radiomics nomograph was constructed. The predictive performance of the above models was evaluated using receiver operating characteristic (ROC) curve analysis. Furthermore, the nomogram was assessed using calibration curve and decision curve analysis. Results: Thirty-seven features were used to establish a radiomics signature, which demonstrated better predictive performance than did the image features model constructed using tumor size and intratumoral vessels in the training and validation cohorts (p <0.05). The radiomics nomogram demonstrated satisfactory discrimination in the training (area under the ROC curve [AUC] 0.93 [95% CI 0.87-0.96]) and validation (AUC 0.87 [95% CI 0.79-0.93]) cohorts and good calibration (Hosmer-Lemeshow p>0.05). Decision curve analysis verified that the radiomics nomogram had the best clinical utility compared with the other models. Conclusion: The radiomics nomogram developed in the present study is a promising tool to predict tumor necrosis and facilitate preoperative clinical decision-making for patients with ccRCC.

Lung cancer is one of the most prevalent and life-threatening neoplasias worldwide due to the deficiency of ideal diagnostic biomarkers. Although aberrant glycosylation has been observed in human serum and tissue, little is known about the alterations in bronchoalveolar lavage fluid (BALF) that are extremely associated with lung cancer. In this study, our aim was to systematically investigate and assess the alterations of protein glycopatterns in BALF and possibility as biomarkers for diagnosis of lung cancer. Here, lectin microarrays and blotting analysis were utilized to detect the differential expression of BALF glycoproteins from patients with 80 adenocarcinomas (ADC), 77 squamous carcinomas (SCC), 51 small cell lung cancer (SCLC), and 73 benign pulmonary diseases (BPD). These 281 specimens were then randomly divided into a training cohort and validation cohort for constructing and verifying the diagnostic models based on the glycopattern abundances. Moreover, an independent test was performed with 120 newly collected BALF samples enrolled in the double-blind cohort to further assess the clinical application potential of the diagnostic models. According to the results, there were 15 (e.g., PHA-E, EEL, and BPL) and 14 lectins (e.g., PTL-II, LCA, and SJA) that individually showed significant variations in different types and stages of lung cancer compared to BPD. Notably, the diagnostic models achieved better discriminate power in the validation cohort and exhibited high accuracies of 0.917, 0.864, 0.712, 0.671, and 0.781 in the double-blind cohort for the diagnosis of lung cancer, early stage lung cancer, ADC, SCC, and SCLC, respectively. Taken together, the present study revealed that the abnormally altered protein glycopatterns in BALF are expected to be novel potential biomarkers for the identification and early diagnosis of lung cancer, which will contribute to explain the mechanism of the development of lung cancer from the perspective of glycobiology.

LncRNA-AC02278.4 (ENSG00000248538) is a long non-coding RNA (lncRNA) found to be highly expressed in multiple human cancers including lung adenocarcinoma (LUAD). However, the underlying biological function and potential mechanisms of AC02278.4 driving the progression of LUAD remain unclear. In this study, we investigated the role of AC02278.4 in LUAD and found that AC02278.4 expression was significantly increased in datasets extracted from The Cancer Genome Atlas. Increased expression of lncRNA-AC02278.4 was correlated with advanced clinical parameters. Receiver operating characteristic (ROC) curve analysis revealed the significant diagnostic ability of AC02278.4 [area under the ROC curve (AUC) = 0.882]. In addition, gene set enrichment analysis (GSEA) enrichment showed that AC02278.4 expression was correlated with immune response-related signaling pathways. Finally, we determined that AC02278.4 regulated cell proliferation and migration of LUAD in vitro. Our clinical sample results also confirmed that AC02278.4 was highly expressed in LUAD and correlated with adverse clinical outcomes. In conclusion, our data demonstrated that AC02278.4 was correlated with progression and immune infiltration and could serve as a prognostic biomarker for LUAD.

Around 85% of childhood Acute Lymphoblastic Leukemia (ALL) are of B-cell origin and characterized by the presence of different translocations including BCR-ABL1, ETV6-RUNX1, E2A-PBX1, and MLL fusion proteins. The current clinical investigations used to identify ETV6-RUNX1 translocation include FISH and fusion transcript specific PCR. In the current study we assessed the utility of IGF2BP1, an oncofetal RNA binding protein, that is over expressed specifically in ETV6-RUNX1 translocation positive B-ALL to be used as a diagnostic marker in the clinic. Further, public transcriptomic and Crosslinked Immunoprecipitation (CLIP) datasets were analyzed to identify the putative targets of IGF2BP1. We also studied the utility of using the mRNA expression of two such targets, MYC and EGFL7 as potential diagnostic markers separately or in conjunction with IGF2BP1. We observed that the expression of IGF2BP1 alone measured by RT-qPCR is highly sensitive and specific to be used as a potential biomarker for the presence of ETV6-RUNX1 translocation in future.

Background: To investigate the prognostic value of circulating plasma cells (CPC) and establish novel nomograms to predict individual progression-free survival (PFS) as well as overall survival (OS) of patients with newly diagnosed multiple myeloma (NDMM). Methods: One hundred ninetyone NDMM patients in Wuhan Union Hospital from 2017.10 to 2020.8 were included in the study. The entire cohort was randomly divided into a training (n = 130) and a validation cohort (n = 61). Univariate and multivariate analyses were performed on the training cohort to establish nomograms for the prediction of survival outcomes, and the nomograms were validated by calibration curves. Results: When the cut-off value was 0.038%, CPC could well distinguish patients with higher tumor burden and lower response rates (P < 0.05), and could be used as an independent predictor of PFS and OS. Nomograms predicting PFS and OS were developed according to CPC, lactate dehydrogenase (LDH) and creatinine. The C-index and the area under receiver operating characteristic curves (AUC) of the nomograms showed excellent individually predictive effects in training cohort, validation cohort or entire cohort. Patients with total points of the nomograms ≤ 60.7 for PFS and 75.8 for OS could be defined as low-risk group and the remaining as high-risk group. The 2-year PFS and OS rates of patients in low-risk group was significantly higher than those in high-risk group (p < 0.001). Conclusions: CPC is an independent prognostic factor for NDMM patients. The proposed nomograms could provide individualized PFS and OS prediction and risk stratification.

Objective: The purpose of this study was to evaluate the diagnostic efficiency of combining plasma microRNAs (miRNAs) and computed tomography (CT) features in the diagnosis of pulmonary nodules. Methods: Ninety-two pulmonary nodule patients who had undergone surgery were enrolled in our study from July 2016 to March 2018 at the Sun Yat-sen University Cancer Center. A prediction model was established by combining 3 miRNAs (miRNA-146a, -200b, and -7) and CT features to identify the pulmonary nodules of these patients. We evaluated the diagnostic performance of this prediction model for pulmonary nodules using the Receiver Operating Characteristic (ROC) curve. Results: The expression levels of miRNA-146a, -200b, and -7 in early-stage non-small cell lung cancer (NSCLC) patients are significantly higher than those in benign nodule patients. We used these three miRNAs and CT features (pleural indentation and speculation) to establish a prediction model for early-stage NSCLC, with a sensitivity and specificity of 92.9%, 83.3% in the training set, respectively. For the validation process, with the sensitivity of 71.8% and the specificity of 69.2%. For ROC curve analyses, area under the curve (AUC) for tumor identification in the training stage and validation stage were 0.929 and 0.781, respectively. Conclusion: Plasma miRNA-146a, miRNA-200b, and miRNA-7 may be potential biomarkers for the early diagnosis of lung cancer. Our prediction model can help to identify the nature of pulmonary nodules with a relatively high diagnostic efficiency.

Purpose: The construction and validation of a radiomics nomogram based on machine learning using magnetic resonance image (MRI) for predicting the efficacy of neoadjuvant chemotherapy (NACT) in patients with breast cancer (BCa). Methods: This retrospective investigation consisted of 158 patients who were diagnosed with BCa and underwent MRI before NACT, of which 33 patients experienced pathological complete response (pCR) by the postoperative pathological examination. The patients with BCa were divided into the training set (n = 110) and test set (n = 48) randomly. The features were selected by the maximum relevance minimum redundancy (mRMR) and absolute shrinkage and selection operator (LASSO) algorithm in the training set. In return, the radiomics signature was established using machine learning. The predictive score of each patient was calculated using the radiomics signature formula. Finally, the predictive scores and clinical factors were used to perform the multivariate logistic regression and construct the nomogram. Receiver operating characteristics (ROC) analyses were used to assess and validate the diagnostic accuracy of the nomogram in the test set. Lastly, the usefulness of the nomogram was confirmed via decision curve analysis (DCA). Results: The radiomics signature was well-discriminated in the training set [AUC 0.835, specificity 71.32%, and sensitivity 82.61%], and test set (AUC 0.834, specificity 73.21%, and sensitivity 80%). Containing the radiomics signature and hormone status, the radiomics nomogram showed good calibration and discrimination in the training set [AUC 0.888, specificity 79.31%, and sensitivity 86.96%] and test set (AUC 0.879, specificity 82.19%, and sensitivity 83.57%). The decision curve indicated the clinical usefulness of our nomogram. Conclusion: Our radiomics nomogram showed good discrimination in patients with BCa who experience pCR after NACT. The model may aid physicians in predicting how specific patients may respond to BCa treatments in the future.

The aim of this study was to investigate the diagnostic value of hsa_circ_0058514 in plasma extracellular vesicles (EVs) in BC patients and its predictive value for neoadjuvant chemotherapy. The expression of hsa_circ_0058514 in a large sample of BC plasma and healthy subjects' plasma was detected by qPCR, and the ROC curve was drawn to verify its diagnostic value as a plasma tumor marker. Furthermore, the association between the expression of hsa_circ_0058514 and clinicopathological characteristics before and after treatment was detected in the plasma of 40 pairs of BC patients undergoing neoadjuvant therapy. The expression level of hsa_circ_0058514 in the plasma of BC patients was significantly higher than that of healthy subjects. The ROC curve showed that plasma hsa_circ_0058514 ROC in differentiating non-metastatic BC and healthy people had better diagnostic efficiency than conventional tumor markers CA153, CA125, and CEA. In patients with neoadjuvant therapy, the decrease in plasma hsa_circ_0058514 value before and after treatment correlated with pathological MP grade (r = 0.444, p = 0.004) and imaging tumor regression value (r = 0.43, p = 0.005) positive correlation. The detection of hsa_circ_0058514 in both extracellular vesicles of BC cell culture medium and human plasma was demonstrated. Hsa_circ_0058514 is detected in the plasma from BC cells secreted in the form of vesicles. Hsa_circ_0058514 can be used as an early plasma biological indicator for the diagnosis of BC in clinical applications, with a higher risk of recurrence and metastasis, and as a predictor of the effect of neoadjuvant therapy to guide the clinical use of neoadjuvant therapy.

Objective: Gallbladder cancer (GBC) is one of the most aggressive malignant tumors, and there is no effective and convenient method for predicting cancer-specific survival (CSS). We aim to develop a novel nomogram staging system based on the positive lymph node ratio (pLNR) for GBC patients. Methods:A total of 1,356 patients enrolled in the study. We evaluated the prognostic value of the pLNR and built a prognostic nomogram staging system based on the pLNR in the training cohort. The concordance index and calibration plots were used to evaluate model discrimination. The predictive accuracy and clinical value of the nomograms were measured by decision curve analysis (DCA). The CSS nomogram was further validated in an internal validation cohort. Results:The pLNR was an independent prognostic factor for CSS based on Cox regression analyses. A prognostic nomogram that combined T classification, pLNR, M classification, histologic grade, live metastasis, and tumor size was formulated with a c-index of 0.763 (95% CI, 0.728-0.798), while the c-indexes for the staging system of AJCC 8th, 7th, and 6th for CSS prediction were 0.718, 0.718, and 0.717, respectively. The calibration curves showed perfect agreement. The DCA showed that the nomogram provided substantial clinical value. The nomogram (the AUCs for 1, 3, and 5 years were 0.693, 0.716, and 0.726, respectively,) showed high prognostic accuracy. Conclusion:We have developed a formulated nomogram staging system based on the pLNR that allows more accurate individualized predictions of CSS for resected GBC patients than the AJCC staging systems.