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ObjectiveTo develop a CT-based nomogram to predict the response of advanced esophageal squamous cell carcinoma (ESCC) to neoadjuvant chemotherapy plus immunotherapy.MethodsIn this retrospective study, 158 consecutive patients with advanced ESCC receiving contrast-enhanced CT before neoadjuvant chemotherapy plus immunotherapy were randomized to a training cohort (TC, n = 121) and a validation cohort (VC, n = 37). Response to treatment was assessed with response evaluation criteria in solid tumors. Patients in the TC were divided into the responder (n = 69) and non-responder (n = 52) groups. For the TC, univariate analyses were performed to confirm factors associated with response prediction, and binary analyses were performed to identify independent variables to develop a nomogram. In both the TC and VC, the nomogram performance was assessed by area under the receiver operating characteristic curve (AUC), calibration slope, and decision curve analysis (DCA).ResultsIn the TC, univariate analysis showed that cT stage, cN stage, gross tumor volume, gross volume of all enlarged lymph nodes, and tumor length were associated with the response (all P < 0.05). Binary analysis demonstrated that cT stage, cN stage, and tumor length were independent predictors. The independent factors were imported into the R software to construct a nomogram, showing the discriminatory ability with an AUC of 0.813 (95% confidence interval: 0.735–0.890), and the calibration curve and DCA showed that the predictive ability of the nomogram was in good agreement with the actual observation.ConclusionThis study provides an accurate nomogram to predict the response of advanced ESCC to neoadjuvant chemotherapy plus immunotherapy.

Abstract Background Esophagectomy is the main treatment for esophageal squamous cell carcinoma (ESCC), and patients with histopathologically negative margins still have a relatively higher recurrence rate. Contrast-enhanced CT (CECT) radiomics might noninvasively obtain potential information about the internal heterogeneity of ESCC and its adjacent tissues. This study aimed to develop CECT radiomics models to preoperatively identify the differences between tumor and proximal tumor-adjacent and tumor-distant tissues in ESCC to potentially reduce tumor recurrence. Methods A total of 529 consecutive patients with ESCC from Centers A (n = 447) and B (n = 82) undergoing preoperative CECT were retrospectively enrolled in this study. Radiomics features of the tumor, proximal tumor-adjacent (PTA) and proximal tumor-distant (PTD) tissues were individually extracted by delineating the corresponding region of interest (ROI) on CECT and applying the 3D-Slicer radiomics module. Patients with pairwise tissues (ESCC vs. PTA, ESCC vs. PTD, and PTA vs. PTD) from Center A were randomly assigned to the training cohort (TC, n = 313) and internal validation cohort (IVC, n = 134). Univariate analysis and the least absolute shrinkage and selection operator were used to select the core radiomics features, and logistic regression was performed to develop radiomics models to differentiate individual pairwise tissues in TC, validated in IVC and the external validation cohort (EVC) from Center B. Diagnostic performance was assessed using area under the receiver operating characteristics curve (AUC) and accuracy. Results With the chosen 20, 19 and 5 core radiomics features in TC, 3 individual radiomics models were developed, which exhibited excellent ability to differentiate the tumor from PTA tissue (AUC: 0.965; accuracy: 0.965), the tumor from PTD tissue (AUC: 0.991; accuracy: 0.958), and PTA from PTD tissue (AUC: 0.870; accuracy: 0.848), respectively. In IVC and EVC, the models also showed good performance in differentiating the tumor from PTA tissue (AUCs: 0.956 and 0.962; accuracy: 0.956 and 0.937), the tumor from PTD tissue (AUCs: 0.990 and 0.974; accuracy: 0.952 and 0.970), and PTA from PTD tissue (AUCs: 0.806 and 0.786; accuracy: 0.760 and 0.786), respectively. Conclusion CECT radiomics models could differentiate the tumor from PTA tissue, the tumor from PTD tissue, and PTA from PTD tissue in ESCC.

It is important to accurately determine the resectability of thoracic esophageal squamous cell carcinoma (ESCC) for treatment decision-making. Previous studies have revealed that the CT-derived gross tumor volume (GTV) is associated with the staging of ESCC. The present study aimed to explore whether the anatomical distribution-based GTV of non-distant metastatic thoracic ESCC measured using multidetector computed tomography (MDCT) could quantitatively determine the resectability. For this purpose, 473 consecutive patients with biopsy-confirmed non-distant metastatic thoracic ESCC who underwent contrast-enhanced CT were randomly divided into a training cohort (TC; 376 patients) and validation cohort (VC; 97 patients). GTV was retrospectively measured using MDCT. Univariate and multivariate analyses were performed to identify the determinants of the resectability of ESCC in the TC. Receiver operating characteristic (ROC) analysis was performed to clarify whether anatomical distribution-based GTV could help quantitatively determinate resectability. Unweighted Cohen's Kappa tests in VC were used to assess the performance of the previous models. Univariate analysis demonstrated that sex, anatomic distribution, cT stage, cN stage and GTV were related to the resectability of ESCC in the TC (all P<0.05). Multivariate analysis revealed that GTV [P<0.001; odds ratio (OR) 1.158] and anatomic distribution (P=0.027; OR, 1.924) were independent determinants of resectability. ROC analysis revealed that the GTV cut-offs for the determination of the resectability of the upper, middle and lower thoracic portions were 23.57, 22.89 and 22.58 cm3, respectively, with areas under the ROC curves of >0.9. Unweighted Cohen's Kappa tests revealed an excellent performance of the ROC models in the upper, middle and lower thoracic portions with Cohen k-values of 0.913, 0.879 and 0.871, respectively. On the whole, the present study demonstrated that GTV and the anatomic distribution of non-distant metastatic thoracic ESCC may be independent determinants of resectability, and anatomical distribution-based GTV can effectively be used to quantitatively determine resectability. [ABSTRACT FROM AUTHOR]

Esophageal squamous cell carcinoma (ESCC) is prevalent in some geographical regions of the world. ESCC development presents a multistep pathogenic process from inflammation to invasive cancer; however, what is critical in these processes and how they evolve is largely unknown, obstructing early diagnosis and effective treatment. Here, we create a mouse model mimicking human ESCC development and construct a single-cell ESCC developmental atlas. We identify a set of key transitional signatures associated with oncogenic evolution of epithelial cells and depict the landmark dynamic tumorigenic trajectories. An early downregulation of CD8+ response against the initial tissue damage accompanied by the transition of immune response from type 1 to type 3 results in accumulation and activation of macrophages and neutrophils, which may create a chronic inflammatory environment that promotes carcinogen-transformed epithelial cell survival and proliferation. These findings shed light on how ESCC is initiated and developed. The multistep processes involved in the evolution of inflammation to invasive esophageal squamous cell carcinoma (ESCC) is unclear. Here, the authors report a mouse model of ESCC and the role of interplay between carcinogen-transformed epithelial cells and their microenvironment in ESCC development. [ABSTRACT FROM AUTHOR]

Background: Large B cell lymphoma (DLBCL) is the most common type of non-Hodgkin's lymphoma among adults. In some cases, DLBCL may seem similar to carcinoma cells, presenting a round, oval, or polygonal shape and clear nuclei. We found that the expression of P63 accounted for a considerable proportion of DLBCL cases. Under the circumstances, P63 expression may lead to a misdiagnosis, especially with a small biopsy. We aim to investigate the expression status and prognostic significance of P63 in a cohort of Chinese DLBCL patients. Methods: P63, ΔNP63(P40), P53 and Ki67 were detected by immunohistochemistry (IHC). A ROC curve was adopted to find the best cut-off value for positive P63/P53 expression and high Ki67 expression. We defined P53 as positive when ≥50% of the tumor cells showed staining. The relationship between P63 and P53/Ki67 expression was examined. Time-to-event endpoints were estimated according to the Kaplan-Meier method. Moreover, multivariate analyses were conducted to evaluate the prognostic factors in DLBCL. Results: Out of all the 159 DLBCL cases, 76 (47.8%) expressed P63 in the nuclei, while 41 (25.8%) were determined to have high expression by using a ROC cut-off value "≥6". Examination of the different P63 isoforms revealed that the ΔNP63(P40) was unclearly and weakly expressed in only 3 cases, showing a fuzzy yellow cytoplasm. P63 expression was not correlated with subtype (GCB or non-GCB) or P53 but was correlated with a high proliferative index (Ki67). Kaplan-Meier analyses revealed that P63 expression was correlated with overall survival, and P63 positive cases showed poor survival outcomes (P<0.05) in our cohort. Conclusions: ΔNP63(P40) is a useful marker in the differential diagnosis of poorly differentiated squamous cell carcinoma versus DLBCL in small needle biopsy. P63 may be involved in DLBCL tumor progression, and it is an unfavorable prognostic marker in DLBCL. A subgroup of P63 and P53 coexpression DLBCL patients with an extremely poor prognosis should be noted. [ABSTRACT FROM AUTHOR]

The aim of the present study was to investigate the association between cluster of differentiation (CD) 164 expression with clinicopathological parameters and prognosis among patients with oral cavity squamous cell carcinoma (OSCC). The present study retrospectively reviewed 70 patients with OSCC who underwent curative primary surgery. A number of patients subsequently received postoperative chemoradiotherapy although the specimens were not exposed to radiation or chemotherapy prior to anti-CD164 antibody immunohistochemical staining. CD164 overexpression was arbitrarily defined as exhibiting an H-score of ≥120. Univariate and multivariate analyses were performed for sex, age, American Joint Committee on Cancer stage, tumour location, histological grade, surgical margin and H-score. The 5-year overall survival rate was 54.4% and the median follow-up time was 46 months for surviving patients. Univariate analyses revealed that a low overall survival rate was associated with advanced-stage disease (P<0.001), buccogingival tumour location (P=0.038) and a CD164 H-score of <120 (P=0.016). Multivariate Cox's regression analyses revealed that poor overall survival rate was associated with advanced-stage disease (P=0.001) and a CD164 H-score of <120 (P=0.04). CD164 overexpression in OSCC was associated with favourable survival rate. Thus, CD164 expression may be a clinically useful predictor of prognosis in patients with OSCC. [ABSTRACT FROM AUTHOR]

Genome-wide association studies have identified multiple genetic variants associated with risk of esophageal squamous-cell carcinoma (ESCC) in Chinese populations. We examined whether these genetic factors, along with non-genetic factors, can contribute to ESCC risk prediction. We examined 25 single nucleotide polymorphisms (SNPs) and 4 non-genetic factors (sex, age, smoking and drinking) associated with ESCC risk in 9805 cases and 10 493 controls from Chinese populations. Weighted genetic risk score (wGRS) was calculated and logistic regression was used to analyze the association between wGRS and ESCC risk. We calculated the area under the curve (AUC) using receiver operating characteristic curve analysis to measure the discrimination after adding genetic variants to the model with only non-genetic factors. Net reclassification improvement (NRI) was used to quantify the degree of correct reclassification using different models. wGRS of the combined 17 SNPs with significant marginal effect (G SNPs) increased ~4-fold ESCC risk (P = 1.49 × 10−164) and the associations were significant in both drinkers and non-drinkers. However, wGRS of the eight SNPs with significant effect in gene × drinking interaction (GE SNPs) increased ~4-fold ESCC risk only in drinkers (Pinteraction = 8.76 × 10–41). The AUC for a risk model with 4 non-genetic factors, 17 G SNPs, 8 GE SNPs and their interactions with drinking was 70.1%, with the significant improvement of 7.0% compared with the model with only non-genetic factors (P < 0.0001). Our results indicate that incorporating genetic variants, lifestyle factors and their interactions in ESCC risk models can be useful for identifying patients with ESCC. [ABSTRACT FROM AUTHOR]

A recent genome-wide association study (GWAS) of subjects from Japan and South Korea reported a novel association between the TP63 locus on chromosome 3q28 and risk of lung adenocarcinoma ( p = 7.3 × 10); however, this association did not achieve genome-wide significance ( p ≤ 10) among never-smoking males or females. To determine if this association with lung cancer risk is independent of tobacco use, we genotyped the TP63 SNPs reported by the previous GWAS (rs10937405 and rs4488809) in 3,467 never-smoking female lung cancer cases and 3,787 never-smoking female controls from 10 studies conducted in Taiwan, Mainland China, South Korea, and Singapore. Genetic variation in rs10937405 was associated with risk of lung adenocarcinoma [ n = 2,529 cases; p = 7.1 × 10; allelic risk = 0.80, 95% confidence interval (CI) = 0.74-0.87]. There was also evidence of association with squamous cell carcinoma of the lung ( n = 302 cases; p = 0.037; allelic risk = 0.82, 95% CI = 0.67-0.99). Our findings provide strong evidence that genetic variation in TP63 is associated with the risk of lung adenocarcinoma among Asian females in the absence of tobacco smoking. [ABSTRACT FROM AUTHOR]

Abstract Background Computed tomography (CT) is commonly used in all stages of oesophageal squamous cell carcinoma (SCC) management. Compared to basic CT features, CT radiomic features can objectively obtain more information about intratumour heterogeneity. Although CT radiomics has been proved useful for predicting treatment response to chemoradiotherapy in oesophageal cancer, the best way to use CT radiomic biomarkers as predictive markers for determining resectability of oesophageal SCC remains to be developed. This study aimed to develop CT radiomic features related to resectability of oesophageal SCC with five predictive models and to determine the most predictive model. Methods Five hundred ninety-one patients with oesophageal SCC undergoing contrast-enhanced CT were enrolled in this study, and were composed by 270 resectable cases and 321 unresectable cases. Of the 270 resectable oesophageal SCCs, 91 cases were primary resectable tumours; and the remained 179 cases received neoadjuvant therapy after CT, shrank on therapy, and changed to resectable tumours. Four hundred thirteen oesophageal SCCs including 189 resectable cancers and 224 unresectable cancers were randomly allocated to the training cohort; and 178 oesophageal SCCs including 81 resectable tumours and 97 unresectable tumours were allocated to the validation group. Four hundred ninety-five radiomic features were extracted from CT data for identifying resectability of oesophageal SCC. Useful radiomic features were generated by dimension reduction using least absolute shrinkage and selection operator. The optimal radiomic features were chosen using multivariable logistic regression, random forest, support vector machine, X-Gradient boost and decision tree classifiers. Discriminating performance was assessed with area under receiver operating characteristic curve (AUC), accuracy and F-1score. Results Eight radiomic features were selected to create radiomic models related to resectability of oesophageal SCC (P-values

ObjectiveTo investigate relationship of tumor stage-based gross tumor volume (GTV) of esophageal squamous cell carcinoma (ESCC) measured on computed tomography (CT) with early recurrence (ER) after esophagectomy.Materials and MethodsTwo hundred and four consecutive patients with resectable ESCC including 159 patients enrolled in the training cohort (TC) and 45 patients in validation cohort (VC) underwent contrast-enhanced CT less than 2 weeks before esophagectomy. GTV was retrospectively measured by multiplying sums of all tumor areas by section thickness. For the TC, univariate and multivariate analyses were performed to determine factors associated with ER. Mann-Whitney U test was conducted to compare GTV in patients with and without ER. Receiver operating characteristic (ROC) analysis was performed to determine if tumor stage-based GTV could predict ER. For the VC, unweighted Cohen’s Kappa tests were used to evaluate the performances of the previous ROC predictive models.ResultsER occurred in 63 of 159 patients (39.6%) in the TC. According to the univariate analysis, histologic differentiation, cT stage, cN stage, and GTV were associated with ER after esophagectomy (all P-values < 0.05). Multivariate analysis revealed that cT stage and GTV were independent risk factors with hazard ratios of 3.382 [95% confidence interval (CI): 1.533–7.459] and 1.222 (95% CI: 1.125–1.327), respectively (all P-values < 0.05). Mann-Whitney U tests showed that GTV could help differentiate between ESCC with and without ER in stages cT1-4a, cT2, and cT3 (all P-values < 0.001), and the ROC analysis demonstrated the corresponding cutoffs of 13.31, 17.22, and 17.83 cm3 with areas under the curve of more than 0.8, respectively. In the VC, the Kappa tests validated that the ROC predictive models had good performances for differentiating between ESCC with and without ER in stages cT1-4a, cT2, and cT3 with Cohen k of 0.696 (95% CI, 0.498–0.894), 0.733 (95% CI, 0.386–1.080), and 0.862 (95% CI, 0.603–1.121), respectively.ConclusionGTV and cT stage can be independent risk factors of ER in ESCC after esophagectomy, and tumor stage-based GTV measured on CT can help predict ER.