Apparent diffusion coefficient histogram shape analysis for monitoring early response in patients with advanced cervical cancers undergoing concurrent chemo-radiotherapy

Abstract

BackgroundTo explore the role of apparent diffusion coefficient (ADC) histogram shape related parameters in early assessment of treatment response during the concurrent chemo-radiotherapy (CCRT) course of advanced cervical cancers.MethodsThis prospective study was approved by the local ethics committee and informed consent was obtained from all patients. Thirty-two patients with advanced cervical squamous cell carcinomas underwent diffusion weighted magnetic resonance imaging (b values, 0 and 800 s/mm2) before CCRT, at the end of 2nd and 4th week during CCRT and immediately after CCRT completion. Whole lesion ADC histogram analysis generated several histogram shape related parameters including skewness, kurtosis, s-sDav, width, standard deviation, as well as first-order entropy and second-order entropies. The averaged ADC histograms of 32 patients were generated to visually observe dynamic changes of the histogram shape following CCRT.ResultsAll parameters except width and standard deviation showed significant changes during CCRT (all P < 0.05), and their variation trends fell into four different patterns. Skewness and kurtosis both showed high early decline rate (43.10 %, 48.29 %) at the end of 2nd week of CCRT. All entropies kept decreasing significantly since 2 weeks after CCRT initiated. The shape of averaged ADC histogram also changed obviously following CCRT.ConclusionsADC histogram shape analysis held the potential in monitoring early tumor response in patients with advanced cervical cancers undergoing CCRT.

Correlation Between Quantitative Perfusion Histogram Parameters of DCE-MRI and PTEN, P-Akt and m-TOR in Different Pathological Types of Lung Cancer

“…With the absence of PTEN, the F p parameter histogram shows a decrease in energy and an increase in entropy value, suggesting that the internal texture features of the tumor are complex. Meng J et al [21] found that as the malignancy of cervical cancer decreases, the ADC entropy value decreases, proving that the entropy value can re ect tumor heterogeneity by re ecting the complexity of the internal texture of the lesion. V e re ects the size of extracellular space.…”

Apparent diffusion coefficient histogram analysis can evaluate radiation-induced parotid damage and predict late xerostomia degree in nasopharyngeal carcinoma

“…ADC histogram analysis provides a series of parameters that can reflect tissue heterogeneity and distribution of ADC values based on pixel distribution. ADC histogram analysis has been successfully used for differential diagnosis, histological differentiation, and assessing therapeutic response in various tumors [ 19 – 22 ]. We detected the dynamic microstructural changes of parotid glands with whole-volume ADC histogram parameters before and after radiotherapy, and assessed if those parameters could predict late xerostomia degrees of NPC patients.…”

Histogram analysis of T2 mapping for detecting early involvement of extraocular muscles in patients with thyroid-associated ophthalmopathy

“…Based on histogram analysis of the ROIs, T2 histograms and a variety of parameters were generated through combined analysis by Fire Voxel and SPSS software (version 24, IBM Corp., Armonk, NY, USA). These parameters were divided into two categories 25 , including (1) point-specific parameters, which directly mirrored the T2 values: mean (representing average values within a specific ROI) and other cumulative T2 histograms: lower percentiles (5th, 10th, 25th, 50th) , higher percentiles (75th, 90th, 95th), where the nth percentile was the point EOM involvement evaluation. EOMs were divided into involved or normal-appearing in a binary manner.…”

“…Based on histogram analysis of the ROIs, T2 histograms and a variety of parameters were generated through combined analysis by Fire Voxel and SPSS software (version 24, IBM Corp., Armonk, NY, USA). These parameters were divided into two categories 25 , including (1) point-specific parameters, which directly mirrored the T2 values: mean (representing average values within a specific ROI) and other cumulative T2 histograms: lower percentiles (5th, 10th, 25th, 50th) , higher percentiles (75th, 90th, 95th), where the nth percentile was the point at which n percent of voxel values were situated on the left area of the histogram 26 , 27 ; and (2) histogram shape-related parameters, which indirectly reflected the heterogeneous distribution of T2: (a) standard deviation (SD, measuring degree of dispersion from the mean value); (b) skewness (reflecting asymmetry of the histogram distributions); (c) kurtosis (representing the peaked nature of the histogram); (d) entropy (describing irregularity or complexity of the distribution of a parameter of interest); and (e) inhomogeneity (quantifying intralesional heterogeneity).…”