Role of iodine density value on dual-energy CT for detection of high tumor cell proportion region in lung cancer during CT-guided transthoracic biopsy

Differentiation of pathological subtypes and Ki-67 and TTF-1 expression by dual-energy CT (DECT) volumetric quantitative analysis in non-small cell lung cancer

BACKGROUND

To explore the value of dual-energy computed tomography (DECT) in differentiating pathological subtypes and the expression of immunohistochemical markers Ki-67 and thyroid transcription factor 1 (TTF-1) in patients with non-small cell lung cancer (NSCLC).

METHODS

Between July 2022 and May 2024, patients suspected of lung cancer who underwent two-phase contrast-enhanced DECT were prospectively recruited. Whole-tumor volumetric and conventional spectral analysis were utilized to measure DECT parameters in the arterial and venous phase. The DECT parameters model, clinical-CT radiological features model, and combined prediction model were developed to discriminate pathological subtypes and predict Ki-67 or TTF-1 expression. Multivariate logistic regression analysis was used to identify independent predictors. The diagnostic efficacy was assessed by the area under the receiver operating characteristic curve (AUC) and compared using DeLong's test.

RESULTS

This study included 119 patients (92 males and 27 females; mean age, 63.0 ± 9.4 years) who was diagnosed with NSCLC. When applying the DECT parameters model to differentiate between adenocarcinoma and squamous cell carcinoma, ROC curve analysis indicated superior diagnostic performance for conventional spectral analysis over volumetric spectral analysis (AUC, 0.801 vs. 0.709). Volumetric spectral analysis exhibited higher diagnostic efficacy in predicting immunohistochemical markers compared to conventional spectral analysis (both P < 0.05). For Ki-67 and TTF-1 expression, the combined prediction model demonstrated optimal diagnostic performance with AUC of 0.943 and 0.967, respectively.

CONCLUSIONS

The combined predictive model based on volumetric quantitative analysis in DECT offers valuable information to discriminate immunohistochemical expression status, facilitating clinical decision-making for patients with NSCLC.

Evaluating Treatment Response in GEJ Adenocarcinoma: The Role of Pretherapeutic and Posttherapeutic Iodine Mapping

BACKGROUND

Neoadjuvant therapy regimens have significantly improved the prognosis of GEJ (gastroesophageal junction) cancer; however, there are a significant percentage of patients who benefit from earlier resection or adapted therapy regimens, and the true response rate can only be determined histopathologically. Methods that allow preoperative assessment of response are lacking.

PURPOSE

The purpose of this retrospective study is to assess the potential of pretherapeutic and posttherapeutic spectral CT iodine density (IoD) in predicting histopathological response to neoadjuvant chemotherapy in patients diagnosed with adenocarcinoma of the GEJ.

METHODS

In this retrospective cohort study, a total of 62 patients with GEJ carcinoma were studied. Patients received a multiphasic CT scan at diagnosis and preoperatively. Iodine-density maps were generated based on spectral CT data. All tumors were histopathologically analyzed, and the tumor regression grade (TRG) according to Becker et al ( Cancer . 2003;98:1521-1530) was determined. Two experienced radiologists blindly placed 5 defined ROIs in the tumor region of highest density, and the maximum value was used for further analysis. Iodine density was normalized to the aortic iodine uptake. In addition, tumor response was assessed according to standard RECIST measurement. After assessing interrater reliability, the correlation of IoD values with treatment response and with histopathologic TRG was evaluated.

RESULTS

The normalized ΔIoD (IoD at diagnosis - IoD after neoadjuvant treatment) and the normalized IoD after neoadjuvant treatment correlated significantly with the TRG. For the detection of responders and nonresponders, the receiver operating characteristic (ROC) curve for normalized ΔIoD yielded the highest area under the curve of 0.95 and achieved a sensitivity and specificity of 92.3% and 92.1%, respectively. Iodine density after neoadjuvant treatment achieved an area under the curve of 0.88 and a sensitivity and specificity of 86.8% and 84.6%, respectively (cutoff, 0.266). Iodine density at diagnosis and RECIST did not provide information to distinguish responders from nonresponders. Using the cutoff value for IoD after neoadjuvant treatment, a reliable classification of responders and nonresponders was achieved for both readers in a test set of 11 patients. Intraclass correlation coefficient revealed excellent interrater reliability (intraclass correlation coefficient, >0.9). Lastly, using the cutoff value for normalized ΔIoD as a definition for treatment response, a significantly longer survival of responders was shown.

CONCLUSIONS

Changes in IoD after neoadjuvant treatment of GEJ cancer may be a potential surrogate for therapy response.

Whole-lesion iodine map histogram analysis versus single-slice spectral CT parameters for determining novel International Association for the Study of Lung Cancer grade of invasive non-mucinous pulmonary adenocarcinomas

PURPOSE

The purpose of this study was to evaluate and compare the performances of whole-lesion iodine map histogram analysis to those of single-slice spectral computed tomography (CT) parameters in discriminating between low-to-moderate grade invasive non-mucinous pulmonary adenocarcinoma (INMA) and high-grade INMA according to the novel International Association for the Study of Lung Cancer grading system of INMA.

MATERIALS AND METHODS

Sixty-one patients with INMA (34 with low-to-moderate grade [i.e., grade I and grade II] and 27 with high grade [i.e., grade III]) were evaluated with spectral CT. There were 28 men and 33 women, with a mean age of 56.4 ± 10.5 (standard deviation) years (range: 29-78 years). The whole-lesion iodine map histogram parameters (mean, standard deviation, variance, skewness, kurtosis, entropy, and 1st, 10th, 25th, 50th, 75th, 90th, and 99th percentile) were measured for each INMA. In other sessions, by placing regions of interest at representative levels of the tumor and normalizing them, spectral CT parameters (iodine concentration and normalized iodine concentration) were obtained. Discriminating capabilities of spectral CT and histogram parameters were assessed and compared using area under the ROC curve (AUC) and logistic regression models.

RESULTS

The 1st, 10th, and 25th percentiles of the iodine map histogram analysis, and iodine concentration and normalized iodine concentration of single-slice spectral CT parameters were significantly different between high-grade and low-to-moderate grade INMAs (P < 0.001 to P = 0.002). The 1st percentile of histogram parameters (AUC, 0.84; 95% confidence interval [CI]: 0.73-0.92) and iodine concentration (AUC, 0.78; 95% CI: 0.66-0.88) from single-slice spectral CT parameters had the best performance for discriminating between high-grade and low-to-moderate grade INMAs. At ROC curve analysis no significant differences in AUC were found between histogram parameters (AUC = 0.86; 95% CI: 0.74-0.93) and spectral CT parameters (AUC = 0.81; 95% CI: 0.74-0.93) (P = 0.60).

CONCLUSION

Both whole-lesion iodine map histogram analysis and single-slice spectral CT parameters help discriminate between low-to-moderate grade and high-grade INMAs according to the novel International Association for the Study of Lung Cancer grading system, with no differences in diagnostic performances.