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

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.

Quantitative CT perfusion imaging in patients with pancreatic cancer: a systematic review

Pancreatic ductal adenocarcinoma (PDAC) is the third leading cause of cancer-related death with a 5-year survival rate of 10%. Quantitative CT perfusion (CTP) can provide additional diagnostic information compared to the limited accuracy of the current standard, contrast-enhanced CT (CECT). This systematic review evaluates CTP for diagnosis, grading, and treatment assessment of PDAC. The secondary goal is to provide an overview of scan protocols and perfusion models used for CTP in PDAC. The search strategy combined synonyms for 'CTP' and 'PDAC.' Pubmed, Embase, and Web of Science were systematically searched from January 2000 to December 2020 for studies using CTP to evaluate PDAC. The risk of bias was assessed using QUADAS-2. 607 abstracts were screened, of which 29 were selected for full-text eligibility. 21 studies were included in the final analysis with a total of 760 patients. All studies comparing PDAC with non-tumorous parenchyma found significant CTP-based differences in blood flow (BF) and blood volume (BV). Two studies found significant differences between pathological grades. Two other studies showed that BF could predict neoadjuvant treatment response. A wide variety in kinetic models and acquisition protocol was found among included studies. Quantitative CTP shows a potential benefit in PDAC diagnosis and can serve as a tool for pathological grading and treatment assessment; however, clinical evidence is still limited. To improve clinical use, standardized acquisition and reconstruction parameters are necessary for interchangeability of the perfusion parameters.

A Bounding-Box Regression Model for Colorectal Tumor Detection in CT Images Via Two Contrary Networks

The field of medical image analysis has been attracted to deep learning. Various deep learning-based techniques have been introduced to aid diagnosis in the CT image of the patient. The auxiliary model for diagnosis that we proposed is to detect colorectal tumors in the CT image. The model is combined with two contrary networks of 'Detection Transformer" and 'Hourglass". Furthermore., to improve the performance of the model., we propose an efficient connection method for two contrary models by using intermediate prediction information. A total of 3.,509 patients (193.,567 CT images) were applied to the experiment and our model outperforms the conventional models in colorectal tumor detection. Clinical Relevance - The proposed model in this paper automatically detects colorectal tumors and provides the bounding box in the CT images. Colorectal tumor is one of the common diseases. In addition, the mortality rate is so high that in-time treatment is required. The model we present here has a sensitivity (or recall) of 84.73 % for tumor detection and a precision of 88.25 % in the patient CT data. The in-slice performance of the tumor detection shows an IoU of 0.56, a sensitivity of 0.67, and a precision of 0.68.

A free time point model for dynamic contrast enhanced exploration

Dynamic-Contrast-Enhanced (DCE) Imaging has been widely studied to characterize microcirculatory disorders associated with various diseases. Although numerous studies have demonstrated its diagnostic interest, the physiological interpretation using pharmacokinetic models often remains debatable. Indeed, to be interpretable, a model must provide, at first instance, an accurate description of the DCE data. However, the evaluation and optimization of this accuracy remain rather limited in DCE. Here we established a non-linear Free-Time-Point-Hermite (FTPH) data-description model designed to fit DCE data accurately. Its performance was evaluated on data generated using two contrasting pharmacokinetic microcirculatory hypotheses (MH). The accuracy of data description of the models was evaluated by calculating the mean squared error (QE) from initial and assessed tissue impulse responses. Then, FTPH assessments were provided to blinded observers to evaluate if these assessments allowed observers to identify MH in their data. Regardless of the initial pharmacokinetic model used for data generation, QE was lower than 3% for the noise-free datasets and increased up to 10% for a signal-to-noise-ratio (SNR) of 20. Under SNR = 20, the sensitivity and specificity of the MH identification were over 80%. The performance of the FTPH model was higher than that of the B-Spline model used as a reference. The accuracy of the FTPH model regardless of the initial MH provided an opportunity to have a reference to check the accuracy of other pharmacokinetic models.

NCTN Assessment on Current Applications of Radiomics in Oncology

Radiomics is a fast-growing research area based on converting standard-of-care imaging into quantitative minable data and building subsequent predictive models to personalize treatment. Radiomics has been proposed as a study objective in clinical trial concepts and a potential biomarker for stratifying patients across interventional treatment arms. In recognizing the growing importance of radiomics in oncology, a group of medical physicists and clinicians from NRG Oncology reviewed the current status of the field and identified critical issues, providing a general assessment and early recommendations for incorporation in oncology studies.