The associations among quantitative spectral CT parameters, Ki-67 expression levels and EGFR mutation status in NSCLC

Predictive value of spectral computed tomography parameters for EGFR gene mutation in non-small-cell lung cancer

AIM

To explore the predictive value of morphological signs and quantitative parameters from spectral CT for EGFR gene mutations in intermediate and advanced non-small-cell lung cancer (NSCLC).

MATERIALS AND METHODS

This retrospective observational study included patients with intermediate or advanced NSCLC at Xinjiang Medical University Affiliated Tumor Hospital between January 2017 and December 2019. The patients were divided into the EGFR gene mutation-positive and -negative groups.

RESULTS

Seventy-nine patients aged 60.75 ± 9.66 years old were included: 32 were EGFR mutation-positive, and 47 were negative. There were significant differences in pathological stage (P<0.001), tumor diameter (P=0.019), lobulation sign, intrapulmonary metastasis, mediastinal lymph node metastasis, distant metastasis (P<0.001), bone metastasis (P<0.001), arterial phase normalized iodine concentration (NIC) (P=0.001), venous phase NIC (P=0.001), slope of the energy spectrum curve (λ) (P<0.001), and CT value at 70 keV in arterial phase (P=0.004) and venous phase (P=0.003) between the EGFR mutation-positive and -negative patients. The multivariable logistic regression analysis showed that intrapulmonary metastasis, distant metastasis, venous phase NIC, venous phase λ, and pathological stage were independent factors predicting EGFR gene mutations, with high diagnostic power (AUC = 0.975, 91.5% sensitivity, and 90.6% specificity).

CONCLUSION

The pathological stage and the spectral CT parameters of intrapulmonary metastasis, distant metastasis, venous phase NIC, and venous phase λ might pre-operatively predict EGFR gene mutations in intermediate and advanced NSCLC.

A Radiological-Radiomics model for differentiation between minimally invasive adenocarcinoma and invasive adenocarcinoma less than or equal to 3 cm: A two-center retrospective study

OBJECTIVE

To develop a Radiological-Radiomics (R-R) combined model for differentiation between minimal invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IA) of lung adenocarcinoma (LUAD) and evaluate its predictive performance.

METHODS

The clinical, pathological, and imaging data of a total of 509 patients (522 lesions) with LUAD diagnosed by surgical pathology from 2 medical centres were retrospectively collected, with 392 patients (402 lesions) from center 1 trained and validated using a five-fold cross-validation method, and 117 patients (120 lesions) from center 2 serving as an independent external test set. The least absolute shrinkage and selection operator (LASSO) method was utilized to filter features. Logistic regression was used to construct three models for predicting IA, namely, Radiological model, Radiomics model, and R-R model. Also, receiver operating curve curves (ROCs) were plotted, generating corresponding area under the curve (AUC), sensitivity, specificity, and accuracy.

RESULTS

The R-R model for IA prediction achieved an AUC of 0.918 (95 % CI: 0.889-0.947), a sensitivity of 80.3 %, a specificity of 88.2 %, and an accuracy of 82.1 % in the training set. In the validation set, this model exhibited an AUC of 0.906 (95 % CI: 0.842-0.970), a sensitivity of 79.9 %, a specificity of 88.1 %, and an accuracy of 81.8 %. In the external test set, the AUC was 0.894 (95 % CI: 0.824-0.964), a sensitivity of 84.8 %, a specificity of 78.6 %, and an accuracy of 83.3 %.

CONCLUSION

The R-R model showed excellent diagnostic performance in differentiating MIA and IA, which can provide a certain reference for clinical diagnosis and surgical treatment plans.

Efficient 18F-fluorodeoxyglucose positron emission tomography/computed tomography-based machine learning model for predicting epidermal growth factor receptor mutations in non-small cell lung cancer

BACKGROUND

Beyond the human eye's limitations, radiomics provides more information that can be used for diagnosis. We develop a personalized and efficient model based on 18F-fluorodeoxyglucose (18F-FDG) positron emission tomography/computed tomography (PET/CT) to predict epidermal growth factor receptor (EGFR) mutations to help identify which non-small cell cancer (NSCLC) patients are candidates for EGFR-tyrosine kinase inhibitors (TKIs) therapy.

METHODS

We retrospectively included 100 patients with NSCLC and randomized them according to 70 patients in the training group and 30 patients in the validation group. The least absolute shrinkage and selection operator logistic regression (LLR) algorithm and support vector machine (SVM) classifier were used to build the models and predict whether EGFR is mutated or not. The predictive efficacy of the LLR algorithm-based model and the SVM classifier-based model was evaluated by plotting the receiver operating characteristic (ROC) curves and calculating the area under the curve (AUC).

RESULTS

The AUC, sensitivity and specificity of our radiomics model by LLR algorithm were 0.792, 0.967, and 0.600 for the training group and 0.643, 1.00, and 0.378 for the validation group, respectively, in predicting EGFR mutations. The AUC was 0.838 for the training group and 0.696 for the validation group after combining radiomics features with clinical features. The prediction results based on the SVM classifier showed that the validation group had the best performance when based on radial kernel function with AUC, sensitivity, and specificity of 0.741, 0.667, and 0.825, respectively.

CONCLUSIONS

Radiomics models based on 18F-FDG PET/CT modeled with different machine learning algorithms can improve the predictive efficacy of the models. Models that combine clinical features are more clinically valuable.

Osimertinib resistance prognostic gene signature: STRIP2 is associated with immune infiltration and tumor progression in lung adenocarcinoma

OBJECTIVE

Although the use of osimertinib can significantly improve the survival time of lung adenocarcinoma (LUAD) patients with epithelial growth factor receptor mutation, eventually drug resistance will limit the survival benefit of most patients. This study aimed to develop a novel prognostic predictive signature based on genes associated with osimertinib resistance.

METHODS

The differentially expressed genes (DEGs) associated with osimertinib resistance in LUAD were screened from Gene Expression Omnibus datasets and The Cancer Genome Atlas datasets. Multivariate cox regression was used to establish a prognostic signature, and then a nomogram was developed to predict the survival probability of LUAD patients. We used ROC curve and DCA curve to evaluate its clinical prediction accuracy and net benefit. In addition, the differentially expressed genes significantly associated with prognosis were selected for immune infiltration analysis and drug sensitivity analysis, and their roles in the progression of lung adenocarcinoma were verified by in vitro experiments.

RESULTS

Our evaluation results indicated that the new nomogram had higher clinical prediction accuracy and net benefit value than the TN nomogram. Further analysis showed that patients with low STRIP2 expression had a higher level of immune response, and may be more likely to benefit from immune checkpoint inhibitors and conventional antitumor drugs. This may help to select more precise and appropriate therapy for LUAD patients with osimertinib resistance. Furthermore, in vitro experiments showed that STRIP2 promoted the LUAD cells proliferation, migration and invasion. This further demonstrates the importance of this gene signature for prognostic prediction.

CONCLUSION

We developed a reliable prognostic model based on DEGs associated with osimertinib resistance and screened for biomarker that can predict the immune response in LUAD patients, which may help in the selection of treatment regimens after osimertinib resistance.

Correlation between 18F-FDG PET/MR parameters with the expression level of epidermal growth factor receptor and the diagnostic value of PET/MR in head and neck squamous cell carcinoma

Objective

To investigate the correlation between parameters of PET/MR and the expression level of epidermal growth factor receptor (EGFR) in head and neck squamous cell carcinoma (HNSCC) and to evaluate diagnostic efficacy of independent and combined PET/MR parameters for the expression level of EGFR.

Materials and methods

21 patients who had undergone PET/MR and been proven HNSCC pathologically were included in this retrospective study. The PET/MR sequences included 18-flurodeoxyglucose (¹⁸F-FDG) PET, T1, T2-weighted imaging, DWI, ADC and DCE. Parameters including ADC_mean from DWI, Ktrans, Ve, Kep from DCE, and SUV_mean, SUV_max from PET were obtained. Immunohistochemical method was used to detect the expression level of EGFR. The associations between parameters of PET/MR and EGFR expression level were analyzed by Spearman's analysis. Logistic regression was utilized to establish the diagnostic model of EGFR expression level with PET/MR parameters. The efficacy of the independent and combined diagnostic model for EGFR expression level in HNSCC was analyzed by ROC curve. P value ≤ 0.05 was considered statistically significant.

Results

(1) Expression level of EGFR was correlated to SUV_mean with correlation coefficient of 0.47 (p = 0.05). (2) There was significant difference of SUV_mean between the EGFR high- and low-expression groups (p = 0.02). (3) Combination of PET/MR improved the diagnostic efficacy for expression level of EGFR, with AUC = 0.93.

Conclusion

There were different degrees of correlation between PET/MR parameters and EGFR expression level in HNSCC. Combination of PET/MR might improve diagnostic efficacy of EGFR expression level.

Virtual monoenergetic imaging predicting Ki-67 expression in lung cancer

This study aimed to optimize slope and energy levels for evaluating Ki-67 expression in lung cancer using virtual monoenergetic imaging and compare the predictive efficiency of different energy spectrum slopes (λHU) for Ki-67. Forty-three patients with primary lung cancer confirmed via pathological examination were enrolled in this study. They underwent baseline arterial-phase (AP) and venous-phase (VP) energy spectrum computed tomography (CT) scanning before surgery. The CT values were 40-190 keV, with 40-140 keV indicating pulmonary lesions at AP and VP, and P < 0.05 indicating a statistically significant difference. An immunohistochemical examination was conducted, and receiver operating characteristic curves were used to analyze the prediction performance of λHU for Ki-67 expression. SPSS Statistics 22.0 (IBM Corp., NY, USA) was used for statistical analysis, and χ², t, and Mann-Whitney U tests were used for quantitative and qualitative analyses of data. Significant differences were observed at the corresponding CT values of 40 keV (as 40-keV is considered the best for single-energy image for evaluating Ki-67 expression) and 50 keV in AP and at 40, 60, and 70 keV in VP between high- and low-Ki-67 expression groups (P < 0.05). In addition, the λHU values of three-segment energy spectrum curve in both AP and VP were quite different between two groups (P < 0.05). However, the VP data had greater predictive values for Ki-67. The areas under the curve were 0.859, 0.856, and 0.859, respectively. The 40-keV single-energy sequence was the best single-energy sequence to evaluate the expression of Ki-67 in lung cancer and to obtain λHU values using the energy spectrum curve in the VP. The CT values had better diagnostic efficiency.

[Correlation Analysis of Ki67 Expression and EGFR Mutation on the Risk of Recurrence and Metastasis in Postoperative Patients with Stage I Lung Adenocarcinoma]

BACKGROUND

The prognosis of stage I non-small cell lung cancer (NSCLC) is generally good. However, some of the stage I NSCLC patients still may have early recurrence and metastasis, and there is no standard method to screen this part of the population. The aim of this study is to investigate the relationship between Ki67 expression as well as epidermal growth factor receptor (EGFR) mutation and the risk of recurrence in postoperative patients with stage I lung adenocarcinoma.

METHODS

We retrospectively enrolled 118 postoperative patients with stage I lung adenocarcinoma. EGFR mutation was tested using amplification refractory mutation system polymerase chain reaction (ARMS-PCR) , and Ki67 level was detected by immunohistochemistry (IHC), followed by the collection of the patients' clinical characteristics. Kaplan-Meier method, Log-rank test, and Cox proportional hazards regression model were used for the prognostic statistical analysis.

RESULTS

Among the 118 patients, the rate of high Ki67 expression was 43.22% (51/118), which is related to gender, smoking status, surgical method, differentiation degree, and postoperative stage (P<0.05). Meanwhile, EGFR mutation rate was 61.02% (72/118), of which EGFR exon 19 deletion mutation rate was 19.49% (23/118), and the EGFR exon 21 L858R mutation rate was 41.53% (49/118). However, Ki67 expression was not associated with EGFR mutation status (χ2=1.412, P=0.235). Survival analysis showed that high Ki67 expression was inversely associated with disease-free survival (DFS) and overall survival (OS) in stage I lung adenocarcinoma (P<0.05), but EGFR mutation status was not significantly associated with DFS and OS (P>0.05). In the subgroup analysis, the DFS of the EGFR exon 19 deletion group was significantly decreased compared with the EGFR exon 21 L858R mutation group (P=0.031), but there was no significant difference in OS (P=0.308). Multivariate analysis showed that there was statistical significance between Ki67 expression (P=0.001) and DFS in stage I lung adenocarcinoma; Ki67 expression (P=0.03) and gender (P=0.015) were associated with OS in stage I lung adenocarcinoma.

CONCLUSIONS

Ki67 expression is an independent influencing factor for postoperative recurrence and OS of stage I lung adenocarcinoma and it is not significantly associated with EGFR mutation. There is no significant difference between EGFR mutation status and the prognostis of stage I lung adenocarcinoma. However, the prognosis differed in EGFR mutation types; the patients with EGFR exon 19 deletion are at higher risk of recurrence than EGFR exon 21 L858R mutation.

Association between quantitative spectral CT parameters, Ki-67 expression, and invasiveness in lung adenocarcinoma manifesting as ground-glass nodules

BACKGROUND

Few studies about lung ground-glass nodules (GGNs) have been done using non-enhancement spectral computed tomography (CT) imaging.

PURPOSE

To examine the association between spectral CT parameters, Ki-67 expression, and invasiveness in lung adenocarcinoma manifesting as GGNs.

MATERIAL AND METHODS

Spectral CT parameters were analyzed in 106 patients with lung GGNs. The Ki-67 labeling index (Ki-67 LI) was measured, and patients were divided into low expression and high expression groups according to the number of positive-stained cells (low expression ≤10%; high expression >10%). Spectral CT parameters were compared between low and high expression groups. The correlation between spectral CT parameters and Ki-67 LI was estimated by Spearman correlation analysis. Cases were divided into a preinvasive and minimally invasive adenocarcinoma (MIA) group (atypical adenomatous hyperplasia, adenocarcinoma in situ, and MIA) and invasive adenocarcinoma (IA) group. Spectral CT parameters were compared between the two groups. The diagnostic performance was evaluated using receiver operating characteristic analysis.

RESULTS

There were significant differences in water concentration of lesions (WCL) and monochromatic CT values between the low and high expression groups. CT 40 keV had the highest correlation coefficient with Ki-67 LI. WCL and monochromatic CT values were significantly higher in the IA group than in the pre/MIA group. The value of area under the curve of CT 40 keV was 0.946 (95% confidence interval=0.905-0.988) for differentiating the two groups; the cutoff was -280.66 Hu.

CONCLUSION

Spectral CT is an effective non-invasive method for the prediction of proliferation and invasiveness in lung adenocarcinoma manifesting as GGNs.

Establishment and validation of a radiological-radiomics model for predicting high-grade patterns of lung adenocarcinoma less than or equal to 3 cm

Objective

We aimed to develop a Radiological-Radiomics (R-R) based model for predicting the high-grade pattern (HGP) of lung adenocarcinoma and evaluate its predictive performance.

Methods

The clinical, pathological, and imaging data of 374 patients pathologically confirmed with lung adenocarcinoma (374 lesions in total) were retrospectively analyzed. The 374 lesions were assigned to HGP (n = 81) and non-high-grade pattern (n-HGP, n = 293) groups depending on the presence or absence of high-grade components in pathological findings. The least absolute shrinkage and selection operator (LASSO) method was utilized to screen features on the United Imaging artificial intelligence scientific research platform, and logistic regression models for predicting HGP were constructed, namely, Radiological model, Radiomics model, and R-R model. Also, receiver operating curve (ROC) curves were plotted on the platform, generating corresponding area under the curve (AUC), sensitivity, specificity, and accuracy. Using the platform, nomograms for R-R models were also provided, and calibration curves and decision curves were drawn to evaluate the performance and clinical utility of the model. The statistical differences in the performance of the models were compared by the DeLong test.

Results

The R-R model for HGP prediction achieved an AUC value of 0.923 (95% CI: 0.891-0.948), a sensitivity of 87.0%, a specificity of 83.4%, and an accuracy of 84.2% in the training set. In the validation set, this model exhibited an AUC value of 0.920 (95% CI: 0.887-0.945), a sensitivity of 87.5%, a specificity of 83.3%, and an accuracy of 84.2%. The DeLong test demonstrated optimal performance of the R-R model among the three models, and decision curves validated the clinical utility of the R-R model.

Conclusion

In this study, we developed a fusion model using radiomic features combined with radiological features to predict the high-grade pattern of lung adenocarcinoma, and this model shows excellent diagnostic performance. The R-R model can provide certain guidance for clinical diagnosis and surgical treatment plans, contributing to improving the prognosis of patients.

Application of dual-energy computed tomography in preoperative evaluation of Ki-67 expression levels in solid non-small cell lung cancer

To investigate whether there were significant differences in dual-energy CT (DECT) in reflecting different quantitative parameters among different levels of Ki-67 expression in patients with solid non-small cell lung cancer (NSCLC). The diagnosis performance of DECT in patients with solid lung adenocarcinoma (LAC) among NSCLC was further discusses. Two hundred fifteen patients confirmed with solid NSCLC were enrolled and analyzed retrospectively in this study. 148 patients were confirmed with LAC among all patients. Three expression levels of Ki-67 were determined by the percentage of Ki-67 positive cancer cells with immunohistochemistry: high-level group (>30%), middle-level group (10%-30%), and low-level group (≤10%). And the latter two levels also known as non-high-level group. The quantitative parameters of enhanced chest DECT (venous phase, VP), including iodine concentration (IC), water concentration (WC), CT value at 40 keV (CT40keV), the slope of energy spectral attenuation curve (λHU) and normalized iodine concentration (NIC) were measured and calculated by gemstone spectral imaging Viewer software. One-way ANOVA was used for the comparison of normal distribution DECT parameters between three levels for patients with NSCLC and patients with LAC. Non-normal distribution data were tested by non-parametric test. In addition, the receiver operating characteristic curve of statistically significant DECT parameters was drawn to distinguish the non-high-level and the high-level of Ki-67. Area under the curve (AUC), sensitivity, specificity was calculated to measure the diagnostic performance of parameter. Both in solid NSCLC and LAC, the IC, NIC, WC, λHU and CT40keV at VP in the high-level group were significantly lower than those in the middle- and low-level group respectively, and the WC at VP in the high-level group was significantly higher than that in the middle- and low-level group respectively (all P < .05). Receiver operating characteristic analysis showed that IC and λHU at VP performed better in distinguishing the high-level and the non-high-level of Ki-67 (NSCLC: AUC = 0.713 and 0.714 respectively; LAC: AUC = 0.705 and 0.706 respectively). Quantitative parameters of DECT provide a new non-invasive method for evaluating the proliferation of cancer cells in solid NSCLC and LAC.

Correlation with Spectral CT Imaging Parameters and Occult Lymph Nodes Metastases in Sufferers with Isolated Lung Adenocarcinoma

For investigating the correlation with spectral CT imaging parameters and occult lymph nodes metastasis in sufferers with isolated lung adenocarcinoma. The clinic cases data of 352 sufferers with isolated lung adenocarcinoma from January 2019 to January 2022 were assembled. In line with whether the sufferers had occult lymph nodes metastasis, they were taken as a part in the metastasis group (n = 172) and the nonmetastasis group. All sufferers were scanned by spectral CT with a dual-phase contrast-enhanced method, and the recording of spectral CT imaging parameters in arteriovenous phase, iodine concentration (IC), water concentration (WC), the slope rate of the spectral HU curve (λHU), the normalized iodine concentration(NIC), the normalized water concentration(NWC), the normalized effective atomic number (Neff-Z)], and receiver operating characteristic (ROC) were employed to analyze the spectral CT imaging parameters of the arteriovenous phase. Evaluation of occult lymph nodes metastases in sufferers with isolated lung adenocarcinoma. The IC, NIC, λHU, and Neff-Z in the arteriovenous phase spectral CT imaging parameters of the metastasis group were obviously smaller than that of the nonmetastasis group, and the discrepancies were statistically obvious (P < 0.05). The results of ROC curve analysis manifested that the area under the curve (AUC) of λHU, IC, NIC, and Neff-Z in the CT parameters of the arterial phase were 0. 840 (95%CI : 0. 796-0.883), 0.763 (95% CI : 0.708-0.818), 0.918 (95% CI : 0.888-0.948), 0.778 (95% CI : 0.731-0.826). The AUCs of λHU, IC, NIC, and Neff-Z in the venous phase spectral CT parameters were 0.909 (95% CI : 0.877-0.941), 0.837 (95% CI : 0.792-0.881), and 0.980 (95% CI : 0.968-0.968), respectively. 0.993), 0.792 (95% CI : 0.742∼0.842). Spectral CT imaging parameters have a certain value in evaluating occult lymph nodes metastasis in sufferers with isolated lung adenocarcinoma, which is helpful for doctors to judge the lymph nodes metastasis in sufferers with this disease before surgery.

Correlation of computed tomography quantitative parameters with tumor invasion and Ki-67 expression in early lung adenocarcinoma

The purpose of the study is to investigate the correlation of computed tomography (CT) quantitative parameters with tumor invasion and Ki-67 expression in early lung adenocarcinoma.The study involved 141 lesions in 141 patients with early lung adenocarcinoma. According to the degree of tumor invasion, the lesions were assigned into (adenocarcinoma in situ + minimally invasive adenocarcinoma) group and invasive adenocarcinoma (IAC) group. Artificial intelligence-assisted diagnostic software was used to automatically outline the lesions and extract corresponding quantitative parameters on CT images. Statistical analysis was performed to explore the correlation of these parameters with tumor invasion and Ki-67 expression.The results of logistic regression analysis showed that the short diameter of the lesion and the average CT value were independent predictors of IAC. Receiver operating characteristic curve analysis identified the average CT value as an independent predictor of IAC with the best performance, with the area under the receiver operating characteristic curve of 0.893 (P < .001), and the threshold of -450 HU. Besides, the predicted probability of logistic regression analysis model was detected to have the area under the curve of 0.931 (P < .001). The results of Spearman correlation analysis showed that the expression level of Ki-67 had the highest correlation with the average CT value of the lesion (r = 0.403, P < .001).The short diameter of the lesion and the average CT value are independent predictors of IAC, and the average CT value is significantly positively correlated with the expression of tumor Ki-67.

The Ki-67 Proliferation Index-Related Nomogram to Predict the Response of First-Line Tyrosine Kinase Inhibitors or Chemotherapy in Non-small Cell Lung Cancer Patients With Epidermal Growth Factor Receptor-Mutant Status

Background: The correlation between Ki-67 and epidermal growth factor receptor (EGFR)- or Kristen rat sarcoma viral oncogene homolog (KRAS)-mutant status in advanced or postoperative-recurrent non-small cell lung cancer (NSCLC) has fewer studies reported, and the prognostic role of Ki-67 with first-line EGFR-tyrosine kinase inhibitors (TKIs) or chemotherapy remains controversial. Methods: A total of 295 patients were tested for EGFR-mutant status in advanced or postoperative-recurrent NSCLC and received first-line EGFR-TKIs or chemotherapy for treatment. Ki-67 expression was retrospectively analyzed by immunohistochemistry. The Kaplan-Meier method was used to calculate survival rates. The multivariate Cox proportional hazards model was used to generate a nomogram. The established nomogram was validated using the calibration plots. Results: The expression levels of Ki-67 were divided into low (<60%, n = 186) and high (≥60%, n = 109) groups, based on the receiver operating characteristic curve. The expression levels of Ki-67 were found to be higher in patients with KRAS mutations when compared to KRAS wildtype, and EGFR wildtype was higher than EGFR mutations. The median overall survival (OS) of the low Ki-67 expression group was significantly longer than that of the high Ki-67 group, no matter in all NSCLC, EGFR mutations, EGFR wildtype, KRAS-mutant status, EGFR-TKIs, or chemotherapy of patients (P < 0.05). Subgroup analysis showed that the KRAS wildtype or EGFR mutations combine with low Ki-67 expression group had the longest median OS than KRAS mutations or EGFR wildtype combine with Ki-67 high expression group (P < 0.05). In the training cohort, the multivariate Cox analysis identified age, serum lactate dehydrogenase (LDH), serum Cyfra211, EGFR mutations, and Ki-67 as independent prognostic factors, and a nomogram was developed based on these covariates. The calibration curve for predicting the 12-, 24-, and 30-month OS showed an optimal agreement between the predicted and actual observed outcomes. Conclusions: The Ki-67 expression-based nomogram can well predict the efficacy of first-line therapy in NSCLC patients with EGFR- or KRAS-mutant status, high expression levels of Ki-67 correlated with a poor prognosis.

Improving radiation physics, tumor visualisation, and treatment quantification in radiotherapy with spectral or dual-energy CT

Over the past decade, spectral or dual‐energy CT has gained relevancy, especially in oncological radiology. Nonetheless, its use in the radiotherapy (RT) clinic remains limited. This review article aims to give an overview of the current state of spectral CT and to explore opportunities for applications in RT.

In this article, three groups of benefits of spectral CT over conventional CT in RT are recognized. Firstly, spectral CT provides more information of physical properties of the body, which can improve dose calculation. Furthermore, it improves the visibility of tumors, for a wide variety of malignancies as well as organs‐at‐risk OARs, which could reduce treatment uncertainty. And finally, spectral CT provides quantitative physiological information, which can be used to personalize and quantify treatment.

Pre-operative Prediction of Ki-67 Expression in Various Histological Subtypes of Lung Adenocarcinoma Based on CT Radiomic Features

Purpose: The aims of this study were to combine CT images with Ki-67 expression to distinguish various subtypes of lung adenocarcinoma and to pre-operatively predict the Ki-67 expression level based on CT radiomic features.

Methods: Data from 215 patients with 237 pathologically proven lung adenocarcinoma lesions who underwent CT and immunohistochemical Ki-67 from January 2019 to April 2021 were retrospectively analyzed. The receiver operating curve (ROC) identified the Ki-67 cut-off value for differentiating subtypes of lung adenocarcinoma. A chi-square test or t-test analyzed the differences in the CT images between the negative expression group (n = 132) and the positive expression group (n = 105), and then the risk factors affecting the expression level of Ki-67 were evaluated. Patients were randomly divided into a training dataset (n = 165) and a validation dataset (n = 72) in a ratio of 7:3. A total of 1,316 quantitative radiomic features were extracted from the Analysis Kinetics (A.K.) software. Radiomic feature selection and radiomic classifier were generated through a least absolute shrinkage and selection operator (LASSO) regression and logistic regression analysis model. The predictive capacity of the radiomic classifiers for the Ki-67 levels was investigated through the ROC curves in the training and testing groups.

Results: The cut-off value of the Ki-67 to distinguish subtypes of lung adenocarcinoma was 5%. A comparison of clinical data and imaging features between the two groups showed that histopathological subtypes and air bronchograms could be used as risk factors to evaluate the expression of Ki-67 in lung adenocarcinoma (p = 0.005, p = 0.045, respectively). Through radiomic feature selection, eight top-class features constructed the radiomic model to pre-operatively predict the expression of Ki-67, and the area under the ROC curves of the training group and the testing group were 0.871 and 0.8, respectively.

Conclusion: Ki-67 expression level with a cut-off value of 5% could be used to differentiate non-invasive lung adenocarcinomas from invasive lung adenocarcinomas. It is feasible and reliable to pre-operatively predict the expression level of Ki-67 in lung adenocarcinomas based on CT radiomic features, as a non-invasive biomarker to predict the degree of malignant invasion of lung adenocarcinoma, and to evaluate the prognosis of the tumor.

Prognostic Value of Ki-67 Expression in Advanced Lung Squamous Cell Carcinoma Patients Treated with Chemotherapy

Background

The relationship between the Ki-67 expression level and chemotherapy response and survival prognosis in advanced lung squamous cell carcinoma (SCC) remains unclear.

Methods

A total of 101 patients were included in the study. All patients received systemic first-line platinum-based chemotherapy. The Ki-67 expression level was determined by immunohistochemistry analysis.

Results

The Ki-67 expression level was positively correlated with an increase in tumor T stage (P = 0.0140), N stage (P < 0.0001), and M stage (P < 0.0001) in advanced lung SCC. High Ki-67 expression could predict chemotherapy response (area under the curve = 0.7524, P < 0.0001). Patients with tumors that expressed high levels of Ki-67 had shorter overall survival (OS) (18.8 months vs 25.5 months, P = 0.0002) and progression-free survival (PFS) (4.8 months vs 6.7 months, P < 0.0001). Cox analysis found Ki-67 expression to be an independent prognostic biomarker of shortened OS (P = 0.009) and PFS (P = 0.008).

Conclusion

Ki-67 expression may affect chemotherapy response and thus has prognostic value. Ki-67 expression may be a promising prognostic biomarker for advanced lung SCC.

Dual-energy CT in predicting Ki-67 expression in laryngeal squamous cell carcinoma

PURPOSE

To investigate whether multiple dual-energy computed tomography (DECT) parameters can noninvasively predict the Ki-67 expression (associated with survival and prognosis) in laryngeal squamous cell carcinoma (LSCC).

METHODS

Eighty-eight patients with histologically proven LSCC were retrospectively reviewed. Multiple DECT-derived parameters were measured and correlated with Ki-67 expression by Spearman correlation analysis. Comparisons of the DECT-derived parameters between tumors with low- and high-level expression of Ki-67 were made with the t-tests.

RESULTS

The iodine concentration (IC), normalized IC (NIC), effective atomic number (Z_eff), 40-80 keV, and slope (k) values were positively correlated with Ki-67 expression (all p < 0.05, rho=0.367-0.548). Among all DECT-derived parameters, NIC value had the highest r value in correlation with Ki-67 expression. The IC, NIC, Z_eff, 40-80 keV, and slope (k) values were significantly higher in LSCC with high Ki-67 expression than in those with low Ki-67 expression (all p < 0.05).

CONCLUSIONS

Multiple DECT-derived parameters (IC, NIC, Z_eff, 40-80 keV, and slope (k)) can be used as predictors of survival and prognosis in LSCC, among which the NIC value is the strongest.