To evaluate the use of dual-energy spectral computed tomographic (CT) quantitative parameters compared with the use of conventional CT imaging features for preoperative diagnosis of metastasis to the ...cervical lymph nodes in patients with papillary thyroid cancer.
This study was approved by the ethics committee and all patients provided written informed consent. Analyses of quantitative gemstone spectral imaging data and qualitative conventional CT imaging features were independently performed by different groups of radiologists. Excised lymph nodes were located and labeled during surgery according to location on preoperative CT images and were evaluated histopathologically. Single and combined parameters were fitted to simple and multiple logistic regression models, respectively, by means of the generalized estimating equations method. Sensitivity and specificity analyses were performed by using receiver operating characteristic curves and were compared with data from the qualitative analysis.
The slope of the spectral Hounsfield unit curve ( λHU slope of the spectral Hounsfield unit curve ), normalized iodine concentration, and normalized effective atomic number measured during both arterial and venous phases were significantly higher in metastatic than in benign lymph nodes. The best single parameter for detection of metastatic lymph nodes was venous phase λHU, with sensitivity, specificity, accuracy, positive predictive value, and negative predictive value of 62.0%, 91.1%, 80.6%, 79.7%, and 81.0%, respectively. The best combination of parameters was venous phase λHU slope of the spectral Hounsfield unit curve and arterial phase normalized iodine concentration, with values of 73.0%, 88.4%, 82.9%, 78.0%, and 85.3%, respectively. Compared with qualitative analysis, the venous phase λHU slope of the spectral Hounsfield unit curve showed higher specificity (91.1% vs 83.0%, P < .001) and similar sensitivity (62.0% vs 61.9%, P > .99), and the combined venous phase λHU slope of the spectral Hounsfield unit curve and arterial phase normalized iodine concentration showed higher sensitivity (73.0% vs 61.9%, P < .001) and specificity (88.4% vs 83.0%, P < .001).
Quantitative assessment with gemstone spectral imaging quantitative parameters showed higher accuracy than qualitative assessment of conventional CT imaging features for preoperative diagnosis of metastatic cervical lymph nodes in patients with papillary thyroid cancer.
Objectives
To assess methods to improve the accuracy of prognosis for clinical stage I solid lung adenocarcinoma using radiomics based on different volumes of interests (VOIs).
Methods
This ...retrospective study included patients with postoperative clinical stage I solid lung adenocarcinoma from two hospitals, center 1 and center 2. Three databases were generated: dataset A (training set from center 1), dataset B (internal test set from center 1), and dataset C (external validation test from center 2). Disease-free survival (DFS) data were collected. CT radiomics models were constructed based on four VOIs: gross tumor volume (GTV), 3 mm external to the tumor border (peritumoral volume PTV
0~+3
), 6 mm crossing tumor border (PTV
−3~+3
), and 6 mm external to the tumor border (PTV
0~+6
). The area under the receiver operating characteristic curve (AUC) was used to compare the model accuracies.
Results
A total of 334 patients were included (204 and 130 from centers 1 and 2). The model using PTV
−3~+3
(AUC 0.81 95% confidence interval {CI}: 0.75, 0.94, 0.81 0.63, 0.90 for datasets B and C) outperformed the other three models, GTV (0.73 0.58, 0.81, 0.73 0.58, 0.83), PTV
0~+3
(0.76 0.52, 0.87, 0.75 0.60, 0.83), and PTV
0~+6
(0.72 0.60, 0.81, 0.69 0.59, 0.81), in datasets B and C, all
p
< 0.05.
Conclusions
A radiomics model based on a VOI of 6 mm crossing tumor border more accurately predicts prognosis of clinical stage I solid lung adenocarcinoma than that based on VOIs including overall tumor or external rims of 3 mm and 6 mm.
Key Points
• Radiomics is a useful approach to improve the accuracy of prognosis for stage I solid adenocarcinoma.
• The radiomics model based on VOIs that includes 3 mm within and external to the tumor border (peritumoral volume PTV
−3~+3
) outperformed models that included either only the tumor itself or those that only included the peritumoral volume.
Introduction
To determine the value of whole-tumor histogram analysis of apparent diffusion coefficient (ADC) maps in differentiating nasopharyngeal carcinoma (NPC) from lymphoma (NPL) at the primary ...site
Method and materials
One hundred forty-seven patients with nasopharyngeal tumors (89 NPCs and 38 NPLs) who had undergone magnetic resonance imaging (MRI) and diffusion-weighted imaging were retrospectively analyzed. ADC histogram-derived parameters were compared between the NPC and NPL groups by using the Mann–Whitney
U
test. Receiver operating characteristic (ROC) curves of the histogram parameters were plotted for diagnostic accuracy. Sensitivity and specificity were calculated for each histogram parameter.
Results
In whole-tumor histogram analysis, the mean, median, and 10th and 25th percentiles of ADC were all significantly higher in NPC than NPL (
P
= 0.045,
P
= 0.035,
P
= 0.005, and
P
= 0.016, respectively). Uniformity was significantly higher in NPC than NPL (
P
= 0.001). Skewness was significantly lower in NPC than NPL (
P
= 0.039). For the conventional ROI-based method, ADC
mean
values were significantly higher in NPC than in NPL (
P
= 0.009). The ROC curve analysis showed that uniformity yielded the largest area under the curve (AUC = 0.768) for differentiating NPC from NPL among all ADC metrics, followed by 10th percentiles of ADC (AUC = 0.725); sensitivity and specificity were 76.5% and 71.4%, respectively.
Conclusion
Whole-tumor histogram analysis of ADC maps could be helpful for differentiating NPC from NPL.
Abstract
Background
Images from magnetic resonance imaging (MRI) are crucial unstructured data for prognostic evaluation in nasopharyngeal carcinoma (NPC). We developed and validated a prognostic ...system based on the MRI features and clinical data of locoregionally advanced NPC (LA-NPC) patients to distinguish low-risk patients with LA-NPC for whom concurrent chemoradiotherapy (CCRT) is sufficient.
Methods
This multicenter, retrospective study included 3444 patients with LA-NPC from January 1, 2010, to January 31, 2017. A 3-dimensional convolutional neural network was used to learn the image features from pretreatment MRI images. An eXtreme Gradient Boosting model was trained with the MRI features and clinical data to assign an overall score to each patient. Comprehensive evaluations were implemented to assess the performance of the predictive system. We applied the overall score to distinguish high-risk patients from low-risk patients. The clinical benefit of induction chemotherapy (IC) was analyzed in each risk group by survival curves.
Results
We constructed a prognostic system displaying a concordance index of 0.776 (95% confidence interval CI = 0.746 to 0.806) for the internal validation cohort and 0.757 (95% CI = 0.695 to 0.819), 0.719 (95% CI = 0.650 to 0.789), and 0.746 (95% CI = 0.699 to 0.793) for the 3 external validation cohorts, which presented a statistically significant improvement compared with the conventional TNM staging system. In the high-risk group, patients who received induction chemotherapy plus CCRT had better outcomes than patients who received CCRT alone, whereas there was no statistically significant difference in the low-risk group.
Conclusions
The proposed framework can capture more complex and heterogeneous information to predict the prognosis of patients with LA-NPC and potentially contribute to clinical decision making.
Objectives
We aimed to develop and validate a deep convolutional neural network (DCNN) model for preoperative prediction of microvascular invasion (MVI) in hepatocellular carcinoma (HCC) and its ...clinical outcomes using contrast-enhanced computed tomography (CECT) in a large population of candidates for surgery.
Methods
This retrospective study included 1116 patients with HCC who had undergone preoperative CECT and curative hepatectomy. Radiological (R), DCNN, and combined nomograms were constructed in a training cohort (
n
= 892) respectively based on clinicoradiological factors, DCNN probabilities, and all factors; the performance of each model was confirmed in a validation cohort (
n
= 244). Accuracy and the AUC to predict MVI were calculated. Disease-free survival (DFS) and overall survival (OS) after surgery were recorded.
Results
The proportion of MVI-positive patients was respectively 38.8% (346/892) and 35.7 % (87/244) in the training and validation cohorts. The AUCs of the R, DCNN, and combined nomograms were respectively 0.809, 0.929, and 0.940 in the training cohorts and 0.837, 0.865, and 0.897 in the validation cohort. The combined nomogram outperformed the R nomogram in the training (
p
< 0.001) and validation (
p
= 0.009) cohorts. There was a significant difference in DFS and OS between the R, DCNN, and combined nomogram-predicted groups with and without MVI (
p
< 0.001).
Conclusions
The combined nomogram based on preoperative CECT performs well for preoperative prediction of MVI and outcome.
Key Points
•
A combined nomogram based on clinical information, preoperative CECT, and DCNN can predict MVI and clinical outcomes of patients with HCC.
•
DCNN provides added diagnostic ability to predict MVI.
•
The AUCs of the combined nomogram are 0.940 and 0.897 in the training and validation cohorts, respectively.
Background: in current clinical practice, the standard evaluation for axillary lymph node (ALN) status in breast cancer has a low efficiency and is based on an invasive procedure that causes ...operative-associated complications in many patients. Therefore, we aimed to use machine learning techniques to develop an efficient preoperative magnetic resonance imaging (MRI) radiomics evaluation approach of ALN status and explore the association between radiomics and the tumor microenvironment in patients with early-stage invasive breast cancer.
Methods: in this retrospective multicenter study, three independent cohorts of patients with breast cancer (n = 1,088) were used to develop and validate signatures predictive of ALN status. After applying the machine learning random forest algorithm to select the key preoperative MRI radiomic features, we used ALN and tumor radiomic features to develop the ALN-tumor radiomic signature for ALN status prediction by the support vector machine algorithm in 803 patients with breast cancer from Sun Yat-sen Memorial Hospital and Sun Yat-sen University Cancer Center (training cohort). By combining ALN and tumor radiomic features with corresponding clinicopathologic information, the multiomic signature was constructed in the training cohort. Next, the external validation cohort (n = 179) of patients from Shunde Hospital of Southern Medical University and Tungwah Hospital of Sun Yat-Sen University, and the prospective-retrospective validation cohort (n = 106) of patients treated with neoadjuvant chemotherapy in prospective phase 3 trials NCT01503905, were included to evaluate the predictive value of the two signatures, and their predictive performance was assessed by the area under operating characteristic curve (AUC). This study was registered with ClinicalTrials.gov, number NCT04003558.
Findings: the ALN-tumor radiomic signature for ALN status prediction comprising ALN and tumor radiomic features showed a high prediction quality with AUC of 0·88 in the training cohort, 0·87 in the external validation cohort, and 0·87 in the prospective-retrospective validation cohort. The multiomic signature incorporating tumor and lymph node MRI radiomics, clinical and pathologic characteristics, and molecular subtypes achieved better performance for ALN status prediction with AUCs of 0·90, 0·91, and 0·93 in the training cohort, the external validation cohort, and the prospective-retrospective validation cohort, respectively. Among patients who underwent neoadjuvant chemotherapy in the prospective-retrospective validation cohort, there were significant differences in the key radiomic features before and after neoadjuvant chemotherapy, especially in the gray-level dependence matrix features. Furthermore, there was an association between MRI radiomics and tumor microenvironment features including immune cells, long non-coding RNAs, and types of methylated sites.
Interpretation this study presented a multiomic signature that could be preoperatively and conveniently used for identifying patients with ALN metastasis in early-stage invasive breast cancer. The multiomic signature exhibited powerful predictive ability and showed the prospect of extended application to tailor surgical management. Besides, significant changes in key radiomic features after neoadjuvant chemotherapy may be explained by changes in the tumor microenvironment, and the association between MRI radiomic features and tumor microenvironment features may reveal the potential biological underpinning of MRI radiomics.
No funding.
Background and objectives Trastuzumab is an important targeted drug for HER2-positive gastric cancer. The treatment efficacy of a more cost-effective and accessible trastuzumab biosimilar, HLX02, was ...not well investigated, especially when combined with antiangiogenic treatment. In addition, the tumour microenvironment detected by functional MRI was still unclear during treatment. This study attempts to evaluate the therapeutic effect of antiangiogenic agents combined with HLX02 in a HER2-positive gastric cancer xenograft model and to detect microenvironmental changes using intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI). Materials and methods We subcutaneously injected MKN-45 human gastric cancer cells into BALB/C nude mice to establish a tumour model. Twenty-eight mice were divided into four groups and treated with saline (Group 1), Endostar (Group 2), trastuzumab biosimilar HLX02 (Group 3), or the combination of Endostar and HLX02 (Group 4). We then performed IVIM-DWI before and at different time points after treatment. HE, HER2, TUNEL, E-cadherin staining, and alpha-SMA and CD31 double-staining were used to confirm the pathological changes. Results Group 4 demonstrated the smallest tumour volume at the end of treatment. The D value in Group 4 increased more dramatically, with the highest value on Day 20, compared with the other groups. Perfusion-related parameters (D* and f values) in Groups 2 and 4 increased initially and reversed after Day 10. Group 4 showed the lowest CD31 and HER2 and the highest TUNEL- and E-cadherin-positive staining rates. The D value was positively correlated with TUNEL but negatively correlated with HER2 staining. The D* and f values had positive correlations with CD31 and E-cadherin expression and the vessel maturity index. Conclusions The trastuzumab biosimilar drug HLX02 exhibited good treatment efficacy in HER2-positive gastric cancer, especially when combined with Endostar. IVIM-DWI can noninvasively monitor the process of vascular normalization and reflect the treatment effect early at the molecular level. Keywords: HLX02, IVIM-DWI, Gastric cancer, HER2, Endostar, Vascular normalization
Cisplatin (CDDP) is the first-line chemotherapeutic strategy to treat patients with ovarian cancer (OC). The development of CDDP resistance remains an unsurmountable obstacle in OC treatment and ...frequently induces tumor recurrence. Circular RNAs (circRNAs) are noncoding RNAs with important functions in cancer progression. Whether circRNAs function in CDDP resistance of OC is unclear.
Platinum-resistant circRNAs were screened via circRNA deep sequencing and examined using in situ hybridization (ISH) in OC. The role of circPLPP4 in CDDP resistance was assessed by clone formation and Annexin V assays in vitro, and by OC patient-derived xenografts and intraperitoneal tumor models in vivo. The mechanism underlying circPLPP4-mediated activation of miR-136/PIK3R1 signaling was examined by luciferase reporter assay, RNA pull-down, RIP, MeRIP and ISH.
circPLPP4 was remarkably upregulated in platinum resistant OC. circPLPP4 overexpression significantly enhanced, whereas circPLPP4 silencing reduced, OC cell chemoresistance. Mechanistically, circPLPP4 acts as a microRNA sponge to sequester miR-136, thus competitively upregulating PIK3R1 expression and conferring CDDP resistance. The increased circPLPP4 level in CDDP-resistant cells was caused by increased RNA stability, mediated by increased N6-methyladenosine (m
A) modification of circPLPP4. In vivo delivery of an antisense oligonucleotide targeting circPLPP4 significantly enhanced CDDP efficacy in a tumor model.
Our study reveals a plausible mechanism by which the m
A -induced circPLPP4/ miR-136/ PIK3R1 axis mediated CDDP resistance in OC, suggesting that circPLPP4 may serve as a promising therapeutic target against CDDP resistant OC. A circPLPP4-targeted drug in combination with CDDP might represent a rational regimen in OC.
The kinesin family member 20a (KIF20A) protein has been implicated in the development and progression of many human cancers; however, its precise function and role in cervical cancer remain largely ...unclear. This study aimed to investigate the expression profile and prognostic value of KIF20A in patients with early-stage cervical squamous cell carcinoma.
We examined the mRNA and protein levels of KIF20A in eight cervical cancer cell lines and eight paired cervical cancer samples, compared with normal cervical epithelial cells and adjacent normal cervical tissues, respectively. Immunohistochemistry was performed to detect the expression of KIF20A in paraffin-embedded specimens from 169 early-stage cervical squamous cell carcinoma patients. Statistical analyses were applied to analyze the association between KIF20A expression and clinical variables, as well with patient survival.
The mRNA and protein expression levels of KIF20A were significantly elevated in cervical cancer cell lines and lesions compared with normal cells and corresponding normal tissues (P < 0.05). Immunohistochemistry analysis in 169 cervical cancer cases revealed that increased KIF20A expression was strongly associated with human papillomavirus (HPV) infection (P = 0.008), clinical stage (P = 0.001), tumor recurrence (P = 0.016), vital status (P < 0.001), the property of the surgical margin (P = 0.032), the lymphovascular space involvement (P = 0.014), and pelvic lymph node metastasis (P = 0.001). The overall survival and disease-free survival of patients with high levels of KIF20A expression were significantly poorer than those with low KIF20A expression. KIF20A was an independent survival prognostic factor, as evidenced by univariate and multivariate analysis.
Our results illustrate that elevated KIF20A expression correlates with HPV infection, clinical stage, tumor recurrence, lymphovascular space involvement, pelvic lymph node metastasis, and poor outcome in early-stage cervical squamous cell carcinoma patients. KIF20A aberrant expression is a novel independent unfavorable prognostic factor and may present a potential therapeutic target for cervical cancer.
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