Perfusion CT measurements predict tumor response in rectal carcinoma

Perfusion Computed Tomography in Rectal Carcinoma: Influence of Optimization of the Patlak Range on Calculation of Equivalent Blood Volume and Flow Extraction

PURPOSE

The aim of the study is to assess the influence of manual adjustment of the Patlak range in computed tomography (CT) perfusion analysis of rectal carcinoma compared with default range of the perfusion software.

METHODS

This study was approved by the institutional review board and informed consent was obtained. Twenty-one patients (12 male, 9 female; mean age ± SD, 59 ± 11 years) with rectal cancer were included and underwent perfusion CT before preoperative chemoradiotherapy. Equivalent blood volume (BV) and flow-extraction (FE) were calculated using the Patlak plot model. Two perfusion sets were calculated per patient, a perfusion set using the default setting as provided by the software (dBV, dFE) and an optimized perfusion set after manual adaption of the Patlak range (aBV, aFE), which was limited to the intravascular space clearance of contrast to the extravascular space. Perfusion values calculated with both methods were compared for significance in differences using the Wilcoxon test. A P value of 0.05 or less was defined as statistically significant.

RESULTS

Adjustment of the Patlak range statistically significantly influenced BV and FE calculation. Median dBV was 23.2 mL/100 mL (interquartile range [IQR], 12.1 mL/100 mL), whereas median aBV was 20.3 mL/100 mL (IQR, 10.9 mL/100 mL). The difference in BV was statistically significant ( P = 0.021). Median dFE was 8.3 mL/min/100 mL (IQR, 4.7 mL/min/100 mL), whereas median aFE was 15.4 mL/min/100 mL (IQR, 5.8 mL/min/100 mL). The difference in FE was statistically significant ( P < 0.001).

CONCLUSIONS

Our findings indicate that in perfusion CT of rectal carcinoma, adjustment of the Patlak range may significantly influence BV and FE compared with default setting of the software. This may contribute to standardization in the use of this technique for functional imaging of rectal cancer.

Machine Learning Models That Integrate Tumor Texture and Perfusion Characteristics Using Low-Dose Breast Computed Tomography Are Promising for Predicting Histological Biomarkers and Treatment Failure in Breast Cancer Patients

Simple Summary

Tumor angiogenesis and heterogeneity are associated with poor prognosis for breast cancer. Advances in computer technology have made it possible to noninvasively quantify tumor angiogenesis and heterogeneity appearing in imaging data. We investigated whether low-dose CT could be used as a method for functional oncology imaging to assess tumor heterogeneity and angiogenesis in breast cancer and to predict noninvasively histological biomarkers and molecular subtypes of breast cancer. Low-dose breast CT has advantages in terms of radiation safety and patient convenience. Our study produced promising results for the use of machine learning with low-dose breast CT to identify histological prognostic factors including hormone receptor and human epidermal growth factor receptor 2 status, grade, and molecular subtype in patients with invasive breast cancer. Machine learning that integrates texture and perfusion features of breast cancer with low-dose CT can provide valuable information for the realization of precision medicine.

Abstract

This prospective study enrolled 147 women with invasive breast cancer who underwent low-dose breast CT (80 kVp, 25 mAs, 1.01–1.38 mSv) before treatment. From each tumor, we extracted eight perfusion parameters using the maximum slope algorithm and 36 texture parameters using the filtered histogram technique. Relationships between CT parameters and histological factors were analyzed using five machine learning algorithms. Performance was compared using the area under the receiver-operating characteristic curve (AUC) with the DeLong test. The AUCs of the machine learning models increased when using both features instead of the perfusion or texture features alone. The random forest model that integrated texture and perfusion features was the best model for prediction (AUC = 0.76). In the integrated random forest model, the AUCs for predicting human epidermal growth factor receptor 2 positivity, estrogen receptor positivity, progesterone receptor positivity, ki67 positivity, high tumor grade, and molecular subtype were 0.86, 0.76, 0.69, 0.65, 0.75, and 0.79, respectively. Entropy of pre- and postcontrast images and perfusion, time to peak, and peak enhancement intensity of hot spots are the five most important CT parameters for prediction. In conclusion, machine learning using texture and perfusion characteristics of breast cancer with low-dose CT has potential value for predicting prognostic factors and risk stratification in breast cancer patients.

Volume computed tomography perfusion as a predictive marker for treatment response to concurrent chemoradiotherapy in cervical cancer: a prospective study

BACKGROUND

Computed tomography perfusion (CTP) can provide information on blood perfusion as a reliable marker of tumor response to therapy.

PURPOSE

To assess the role of volume CTP (vCTP) parameters in predicting treatment response to concurrent chemoradiotherapy (CCRT) for cervical cancer.

MATERIAL AND METHODS

Thirty-three patients with cervical cancer underwent vCTP. Three CTP parameters of cervical cancer-including arterial flow (AF), blood volume (BV), and permeability surface (PS)-were measured in two different ways: the region of interest incorporating the "local hot" with the highest enhancement and "cold spot" with the lowest enhancement; and "whole-tumor" measurements. The patients were divided into non-residual and residual tumor groups according to the short-term response to treatment. The clinical and perfusion parameters were compared between the two groups.

RESULTS

There was no significant difference in age, body mass index, FIGO stage, pathological grade, or pretreatment tumor size between the two groups (P > 0.05). The non-residual tumor group had higher pretreatment AF in high-perfusion and low-perfusion subregions than the residual tumor group (P <0.05), but the AF in whole-tumor regions was not different between the two groups (P > 0.05). There were no differences in BV and PS between the two groups (P > 0.05). The diagnostic potency of AF in the low-perfusion subregion was higher than that in the high-perfusion subregion.

CONCLUSION

vCTP parameters are valuable for the prediction of short-term effects. The AF in the low-perfusion subregion was a more effective index for predicting treatment response to CCRT of cervical cancer.

The flow-metabolism ratio might predict treatment response and survival in patients with locally advanced esophageal squamous cell carcinoma

Background

Perfusion CT can offer functional information about tumor angiogenesis, and ¹⁸F-FDG PET/CT quantifies the glucose metabolic activity of tumors. This prospective study aims to investigate the value of biologically relevant imaging biomarkers for predicting treatment response and survival outcomes in patients with locally advanced esophageal squamous cell cancer (LA ESCC).

Methods

Twenty-seven patients with pathologically proven ESCC were included. All patients had undergone perfusion CT and ¹⁸F-FDG PET/CT using separate imaging systems before receiving definitive chemoradiotherapy (dCRT). The perfusion parameters included blood flow (BF), blood volume (BV), and time to peak (TTP), and the metabolic parameters included maximum standardized uptake value (SUVmax), metabolic tumor volume (MTV), and total lesion glycolysis (TLG). The flow-metabolism ratio (FMR) was defined as BF divided by SUVmax. Statistical methods used included Spearman’s rank correlation, Mann–Whitney U test or two-sample t test, receiver operating characteristic (ROC) curve analysis, the Kaplan–Meier method, and Cox proportional hazards models.

Results

The median overall survival (OS) and progression-free survival (PFS) were 18 and 11.6 months, respectively. FMR was significantly positively correlated with BF (r = 0.886, p < 0.001) and negatively correlated with SUVmax (r = − 0.547, p = 0.003) and TTP (r = − 0.462, p = 0.015) in the tumors. However, there was no significant correlation between perfusion and PET parameters. After dCRT, 14 patients (51.9%) were identified as responders, and another 13 were nonresponders. The BF and FMR of the responders were significantly higher than those of the nonresponders (42.05 ± 16.47 vs 27.48 ± 8.55, p = 0.007; 3.18 ± 1.15 vs 1.84 ± 0.65, p = 0.001). The ROC curves indicated that the FMR [area under the curve (AUC) = 0.846] was a better biomarker for predicting treatment response than BF (AUC = 0.802). Univariable Cox analysis revealed that of all imaging parameters, only the FMR was significantly correlated with overall survival (OS) (p = 0.015) and progression-free survival (PFS) (p = 0.017). Specifically, patients with a lower FMR had poorer survival. Multivariable analysis showed that after adjusting for age, clinical staging, and treatment response, the FMR remained an independent predictor of OS (p = 0.026) and PFS (p = 0.014).

Conclusions

The flow-metabolism mismatch demonstrated by a low FMR shows good potential in predicting chemoradiotherapy sensitivity and prognosis in ESCC.

Emerging Functional Imaging Biomarkers of Tumour Responses to Radiotherapy

Tumour responses to radiotherapy are currently primarily assessed by changes in size. Imaging permits non-invasive, whole-body assessment of tumour burden and guides treatment options for most tumours. However, in most tumours, changes in size are slow to manifest and can sometimes be difficult to interpret or misleading, potentially leading to prolonged durations of ineffective treatment and delays in changing therapy. Functional imaging techniques that monitor biological processes have the potential to detect tumour responses to treatment earlier and refine treatment options based on tumour biology rather than solely on size and staging. By considering the biological effects of radiotherapy, this review focusses on emerging functional imaging techniques with the potential to augment morphological imaging and serve as biomarkers of early response to radiotherapy.

MRI-Based Apparent Diffusion Coefficient for Predicting Pathologic Response of Rectal Cancer After Neoadjuvant Therapy: Systematic Review and Meta-Analysis

OBJECTIVE

The purpose of this study was to assess the use of apparent diffusion coefficient (ADC) during DWI for predicting complete pathologic response of rectal cancer after neoadjuvant therapy.

MATERIALS AND METHODS

A systematic review of available literature was conducted to retrieve studies focused on the identification of complete pathologic response of locally advanced rectal cancer after neoadjuvant chemoradiation, through the assessment of ADC evaluated before, after, or both before and after treatment, as well as in terms of the difference between pretreatment and posttreatment ADC. Pooled mean pretreatment ADC, posttreatment ADC, and Δ-ADC (calculated as posttreatment ADC minus pretreatment ADC divided by pretreatment ADC and multiplied by 100) in complete responders versus incomplete responders were calculated. For each parameter, we also pooled sensitivity and specificity and calculated the area under the summary ROC curve.

RESULTS

We found 10 prospective and eight retrospective studies. Overall, pathologic complete response was observed in 22.2% of patients. Pooled mean pretreatment ADC in complete responders was 0.84 × 10^-3 mm²/s versus 0.89 × 10^-3 mm²/s in incomplete responders (p = 0.33). Posttreatment ADC values were 1.51 × 10^-3 mm²/s and 1.29 × 10^-3 mm²/s, in complete and incomplete responders, respectively (p = 0.00001). The Δ-ADC percentages were also significantly higher in complete responders than in incomplete responders (59.7% vs 29.7%, respectively, p = 0.016). Pooled sensitivity, specificity, and AUC were 0.743, 0.755, and 0.841 for pretreatment ADC; 0.800, 0.737, and 0.782 for posttreatment ADC; and 0.832, 0.806, and 0.895 for Δ-ADC.

CONCLUSION

Use of ADC during DWI is a promising technique for assessment of results of neoadjuvant treatment of rectal cancer.

Variations of quantitative perfusion measurement on dynamic contrast enhanced CT for colorectal cancer: implication of standardized image protocol

Tumor angiogenesis is considered an important prognostic factor. With an increasing emphasis on imaging evaluation of the tumor microenvironment, dynamic contrast enhanced-computed tomography (DCE-CT) has evolved as an important functional technique in this setting. Yet many questions remain as to how and when these functional measurements should be performed for each agent and tumor type, and what quantitative models should be used in the fitting process. In this study, we evaluated the variations of perfusion measurement on DCE-CT for rectal cancer patients from (1) different tracer kinetic models, (2) different scan acquisition lengths, and (3) different scan intervals. A total of seven commonly used models were studied: the adiabatic approximation to the tissue homogeneity (AATH) model, adiabatic approximation to the homogeneity tissue with fixed transit time (AATHFT) model, the Tofts model (TM), the extended Tofts model (ETM), Patlak model, Logan model, and the model-free deconvolution method. Akaike's information criterion was used to identify the best fitting model. The interchangeability of different models was further evaluated using Bland-Altman analysis. All models gave comparable blood volume (BV) measurements except the Patlak method. While for the volume transfer constant (K_trans) estimation, AATHFT, AATH, and ETM generated reasonable agreement among each other but not for the other models. Regarding the blood flow (BF) measurement, no two models were interchangeable. In addition, the perfusion parameters were compared with four acquisition times (45, 65, 85, and 105 s) and four temporal intervals (1, 2, 3, and 4 s). No significant difference was observed in the volume transfer constant (K_trans), BV, and BF measurements when comparing data acquired over 65 s with data acquired over 105 s using any of the DCE models in this study. Yet increasing the temporal interval led to a significant overestimation of BF in the deconvolution method. In conclusion, the perfusion measurement is indeed model dependent and the image acquisition/processing technique is dependent. The radiation dose of DCE-CT was an average of 1.5-2 times an abdomen/pelvic CT, which is not insubstantial. To take the DCE-CT forward as a biomarker in oncology, prospective studies should be carefully designed with the optimal image acquisition and analysis technique.

Variability and Reproducibility of 3rd-generation dual-source dynamic volume perfusion CT Parameters in Comparison to MR-perfusion Parameters in Rectal Cancer

To compare in patients with untreated rectal cancer quantitative perfusion parameters calculated from 3^rd-generation dual-source dynamic volume perfusion CT (dVPCT) with 3-Tesla-MR-perfusion with regard to data variability and tumour differentiation. In MR-perfusion, plasma flow (PF), plasma volume (PV) and mean transit time (MTT) were assessed in two measurements (M1 and M2) by the same reader. In dVPCT, blood flow (BF), blood volume (BV), MTT and permeability (PERM) were assessed respectively. CT dose values were calculated. 20 patients (60 ± 13 years) were analysed. Intra-individual and intra-reader variability of duplicate MR-perfusion measurements was higher compared to duplicate dVPCT measurements. dVPCT-derived BF, BV and PERM could differentiate between tumour and normal rectal wall (significance level for M1 and M2, respectively, regarding BF: p < 0.0001*/0.0001*; BV: p < 0.0001*/0.0001*; MTT: p = 0.93/0.39; PERM: p < 0.0001*/0.0001*), with MR-perfusion this was true for PF and PV (p-values M1/M2 for PF: p = 0.04*/0.01*; PV: p = 0.002*/0.003*; MTT: p = 0.70/0.27*). Mean effective dose of CT-staging incl. dVPCT was 29 ± 6 mSv (20 ± 5 mSv for dVPCT alone). In conclusion, dVPCT has a lower data variability than MR-perfusion while both dVPCT and MR-perfusion could differentiate tumour tissue from normal rectal wall. With 3^rd-generation dual-source CT dVPCT could be included in a standard CT-staging without exceeding national dose reference values.

Advanced Imaging Techniques in Evaluation of Colorectal Cancer

Imaging techniques are clinical decision-making tools in the evaluation of patients with colorectal cancer (CRC). The aim of this article is to discuss the potential of recent advances in imaging for diagnosis, prognosis, therapy planning, and assessment of response to treatment of CRC. Recent developments and new clinical applications of conventional imaging techniques such as virtual colonoscopy, dual-energy spectral computed tomography, elastography, advanced computing techniques (including volumetric rendering techniques and machine learning), magnetic resonance (MR) imaging-based magnetization transfer, and new liver imaging techniques, which may offer additional clinical information in patients with CRC, are summarized. In addition, the clinical value of functional and molecular imaging techniques such as diffusion-weighted MR imaging, dynamic contrast material-enhanced imaging, blood oxygen level-dependent imaging, lymphography with contrast agents, positron emission tomography with different radiotracers, and MR spectroscopy is reviewed, and the advantages and disadvantages of these modalities are evaluated. Finally, the future role of imaging-based analysis of tumor heterogeneity and multiparametric imaging, the development of radiomics and radiogenomics, and future challenges for imaging of patients with CRC are discussed. Online supplemental material is available for this article. ^©RSNA, 2018.