To develop and validate a radiomics nomogram for preoperative prediction of lymph node (LN) metastasis in patients with colorectal cancer (CRC).
The prediction model was developed in a primary cohort ...that consisted of 326 patients with clinicopathologically confirmed CRC, and data was gathered from January 2007 to April 2010. Radiomic features were extracted from portal venous-phase computed tomography (CT) of CRC. Lasso regression model was used for data dimension reduction, feature selection, and radiomics signature building. Multivariable logistic regression analysis was used to develop the predicting model, we incorporated the radiomics signature, CT-reported LN status, and independent clinicopathologic risk factors, and this was presented with a radiomics nomogram. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness. Internal validation was assessed. An independent validation cohort contained 200 consecutive patients from May 2010 to December 2011.
The radiomics signature, which consisted of 24 selected features, was significantly associated with LN status (P < .001 for both primary and validation cohorts). Predictors contained in the individualized prediction nomogram included the radiomics signature, CT-reported LN status, and carcinoembryonic antigen level. Addition of histologic grade to the nomogram failed to show incremental prognostic value. The model showed good discrimination, with a C-index of 0.736 (C-index, 0.759 and 0.766 through internal validation), and good calibration. Application of the nomogram in the validation cohort still gave good discrimination (C-index, 0.778 95% CI, 0.769 to 0.787) and good calibration. Decision curve analysis demonstrated that the radiomics nomogram was clinically useful.
This study presents a radiomics nomogram that incorporates the radiomics signature, CT-reported LN status, and clinical risk factors, which can be conveniently used to facilitate the preoperative individualized prediction of LN metastasis in patients with CRC.
The lymphatic system, composed of initial and collecting lymphatic vessels as well as lymph nodes that are present in almost every tissue of the human body, acts as an essential transport system for ...fluids, biomolecules, and cells between peripheral tissues and the central circulation. Consequently, it is required for normal body physiology but is also involved in the pathogenesis of various diseases, most notably cancer. The important role of tumor-associated lymphatic vessels and lymphangiogenesis in the formation of lymph node metastasis has been elucidated during the last two decades, whereas the underlying mechanisms and the relation between lymphatic and peripheral organ dissemination of cancer cells are incompletely understood. Lymphatic vessels are also important for tumor-host communication, relaying molecular information from a primary or metastatic tumor to regional lymph nodes and the circulatory system. Beyond antigen transport, lymphatic endothelial cells, particularly those residing in lymph node sinuses, have recently been recognized as direct regulators of tumor immunity and immunotherapy responsiveness, presenting tumor antigens and expressing several immune-modulatory signals including PD-L1. In this review, we summarize recent discoveries in this rapidly evolving field and highlight strategies and challenges of therapeutic targeting of lymphatic vessels or specific lymphatic functions in cancer patients.
Triple negative breast cancers are a heterogeneous group of tumors characterized by poor patient survival and lack of targeted therapeutics. Androgen receptor has been associated with triple negative ...breast cancer pathogenesis, but its role in the different subtypes has not been clearly defined. We examined androgen receptor protein expression by immunohistochemical analysis in 678 breast cancers, including 396 triple negative cancers. Fifty matched lymph node metastases were also examined. Association of expression status with clinical (race, survival) and pathological (basal, non-basal subtype, stage, grade) features was also evaluated. In 160 triple negative breast cancers, mRNA microarray expression profiling was performed, and differences according to androgen receptor status were analyzed. In triple negative cancers the percentage of androgen receptor positive cases was lower (24.8% vs 81.6% of non-triple negative cases), especially in African American women (16.7% vs 25.5% of cancers of white women). No significant difference in androgen receptor expression was observed in primary tumors vs matched metastatic lesions. Positive androgen receptor immunoreactivity was inversely correlated with tumor grade (p<0.01) and associated with better overall patient survival (p = 0.032) in the non-basal triple negative cancer group. In the microarray study, expression of three genes (HER4, TNFSF10, CDK6) showed significant deregulation in association with androgen receptor status; eg CDK6, a novel therapeutic target in triple negative cancers, showed significantly higher expression level in androgen receptor negative cases (p<0.01). These findings confirm the prognostic impact of androgen receptor expression in non-basal triple negative breast cancers, and suggest targeting of new androgen receptor-related molecular pathways in patients with these cancers.
The goal of this study was to evaluate various clinicopathologic and imaging characteristics as independent predictors of axillary pathologic complete response (pCR) to neoadjuvant chemotherapy (NAC) ...and to determine the added value of a model that integrates imaging characteristics of ultrasound (US) restaging with known clinicopathologic characteristics. A total of 227 clinically node-positive breast cancer patients underwent axillary US after NAC (termed US restaging) before surgery. We constructed a clinicopathologic model with independent predictors of clinicopathologic characteristics in multivariate analyses. A combined model was created by integrating imaging characteristics with clinicopathologic characteristics. The predictive values of the models were compared using the area under the receiver operating characteristic curve. Of the 227 patients, 106 (46.7%) achieved axillary pCR. Multivariate analysis revealed that higher histologic grades (odds ratio OR = 4.21 and 10.11 for moderate and high grade, respectively), negative hormonal receptor status (OR = 2.88), smaller (≤1.5 cm) residual tumor size (OR = 2.83), absence of fatty hilum loss (OR = 14.06) and absence of eccentric cortical thickening of the axillary lymph node (OR = 4.42) were independently associated with the axillary pCR (all p values < 0.05). Integrating the imaging characteristics of the US restaging significantly increased the predictive capability of the model that applied only the clinicopathologic characteristics (c-index, 0.783 vs. 0.657; p < 0.001). Imaging characteristics of the US restaging were independently associated with axillary pCR after NAC and they significantly improved the predictive capability of the model that used only the clinicopathologic characteristics.
In this study we analyzed the expression levels of markers of epithelial-to-mesenchymal transition (EMT) in several papillary thyroid carcinomas (PTCs) and the relation with tumor genotypes and ...clinicopathological characteristics. The role of fibronectin-1 (FN1) was investigated by analyzing the effects of FN1 silencing in two human thyroid cancer cell lines.
Most of EMT markers were significantly over-expressed in a group of 36 PTCs. In particular, FN1 mRNA levels were higher in tumor vs non-tumor tissue (117.3, p < 0.001) and also in aggressive and BRAFV600E samples. Similar results were observed (and confirmed at the protein level) when FN1 expression was analyzed in a validation group of 50 PTCs and six lymph node (LN) metastases. Silencing of FN1 in TPC-1 and BCPAP thyroid cancer cells significantly reduced proliferation, adhesion, migration, and invasion in both cell lines.
Collectively, our data indicate that FN1 overexpression is an important determinant of thyroid cancer aggressiveness.
•EMT markers are overexpressed in papillary thyroid cancers.•Fibronectin-1 is the marker most highly overexpressed in aggressive tumors.•FN1 silencing impairs thyroid cancer cell growth, migration, and invasion.
Metastatic disease is the main cause of cancer-related mortality due to almost universal therapeutic resistance. Despite its high clinical relevance, our knowledge of how cancer cell populations ...change during metastatic progression is limited. Here, we investigated intratumor genetic and phenotypic heterogeneity during metastatic progression of breast cancer. We analyzed cellular genotypes and phenotypes at the single cell level by performing immunoFISH in intact tissue sections of distant metastatic tumors from rapid autopsy cases and from primary tumors and matched lymph node metastases collected before systemic therapy. We calculated the Shannon index of intratumor diversity in all cancer cells and within phenotypically distinct cell populations. We found that the extent of intratumor genetic diversity was similar regardless of the chromosomal region analyzed, implying that it may reflect an inherent property of the tumors. We observed that genetic diversity was highest in distant metastases and was generally concordant across lesions within the same patient, whereas treatment-naïve primary tumors and matched lymph node metastases were frequently genetically more divergent. In contrast, cellular phenotypes were more discordant between distant metastases than primary tumors and matched lymph node metastases. Diversity for 8q24 was consistently higher in HER2(+) tumors compared with other subtypes and in metastases of triple-negative tumors relative to primary sites. We conclude that our integrative method that couples ecologic models with experimental data in human tissue samples could be used for the improved prognostication of patients with cancer and for the design of more effective therapies for progressive disease.
Preoperative evaluation of the number of lymph node metastasis (LNM) is the basis of individual treatment of locally advanced gastric cancer (LAGC). However, the routinely used preoperative ...determination method is not accurate enough.
We enrolled 730 LAGC patients from five centers in China and one center in Italy, and divided them into one primary cohort, three external validation cohorts, and one international validation cohort. A deep learning radiomic nomogram (DLRN) was built based on the images from multiphase computed tomography (CT) for preoperatively determining the number of LNM in LAGC. We comprehensively tested the DLRN and compared it with three state-of-the-art methods. Moreover, we investigated the value of the DLRN in survival analysis.
The DLRN showed good discrimination of the number of LNM on all cohorts overall C-indexes (95% confidence interval): 0.821 (0.785–0.858) in the primary cohort, 0.797 (0.771–0.823) in the external validation cohorts, and 0.822 (0.756–0.887) in the international validation cohort. The nomogram performed significantly better than the routinely used clinical N stages, tumor size, and clinical model (P < 0.05). Besides, DLRN was significantly associated with the overall survival of LAGC patients (n = 271).
A deep learning-based radiomic nomogram had good predictive value for LNM in LAGC. In staging-oriented treatment of gastric cancer, this preoperative nomogram could provide baseline information for individual treatment of LAGC.
•Evaluation of the lymph node metastasis (LNM) is the basis of individual treatment of locally advanced gastric cancer (LAGC).•Deep leaning radiomic nomogram (DLRN) based on CT images can preoperatively determine the number of LNM in LAGC.•DLRN is significantly superior to the routinely used clinical N stages, tumor size, and clinical model.•DLRN is significantly associated with the overall survival of LAGC.
To evaluate the predictive performance of the radiomics model in predicting axillary lymph node (ALN) metastasis through the associations between radiomics features and genomic features in patients ...with breast cancer.
Patients with breast cancer were enrolled retrospectively from a public database (111 patients as training group) and one hospital (15 patients as external validation group). The genomics features from transcriptome data and radiomics features from dynamic contrast-enhanced magnetic resonance imaging (MRI) were collected. Firstly, overlapping genes were identified using the Kyoto Encyclopedia of Genes and Genomes and differentially expressed gene analysis, while radiomics features were reduced using a data-driven method. Then, the associations between overlapping genes and retained radiomics features were assessed to obtain key pairs of radiomics–genomics features. Furthermore, the least absolute shrinkage and selection operator (LASSO) algorithm was used to detect the key-pairs features. Finally, radiomics and genomics models were constructed to predict ALN metastasis.
After using the hybrid data- and gene-driven selection method, key pairs of features were detected, which consisted of six radiomic features associated with four genomic features. The radiomics model exhibited comparable performance to the genomics model in predicting ALN metastasis (radiomic model: area under the curve AUC = 0.71, sensitivity = 77%, specificity = 56%; genomic model: AUC = 0.72, sensitivity = 85%, specificity = 74%). The four genomic features were enriched in six pathways and related to metabolism and human diseases.
The radiomics model established using the gene-driven hybrid selection method could predict ALN metastasis in breast cancer, which showed comparable performance to the genomics model.
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