Rectal Cancer Invasiveness: Whole-Lesion Diffusion-Weighted Imaging (DWI) Histogram Analysis by Comparison of Reduced Field-of-View and Conventional DWI Techniques

Yang, Peng; Tang, Hao; Hu, Xuemei; Shen, Yaqi; Kamel, Ihab R.; Li, Zhen; Hu, Daoyu

doi:10.1038/s41598-019-55059-0

Cited by 10 publications

(7 citation statements)

References 40 publications

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“…The association between whole-rectal tumor ADC histogram parameters and different clinical pathologic prognostic factors has been the main subject of many papers, however, with conflicting factors [31,32,[41][42][43][44][45][46][47][48]. Regarding pT stage, a well-recognized powerful prognostic factor, there are several studies that demonstrate significantly lower values of ADC histogram metrics in tumors with pT1-T2 stages [32,41,49]. Conversely, other investigations found no significant differences in ADC histogram percentiles among different pT stages [50].…”

Section: Discussionmentioning

confidence: 99%

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The Utility of ADC First-Order Histogram Features for the Prediction of Metachronous Metastases in Rectal Cancer: A Preliminary Study

Boca

Caraiani

Popa

et al. 2022

Biology

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This study aims the ability of first-order histogram-based features, derived from ADC maps, to predict the occurrence of metachronous metastases (MM) in rectal cancer. A total of 52 patients with pathologically confirmed rectal adenocarcinoma were retrospectively enrolled and divided into two groups: patients who developed metachronous metastases (n = 15) and patients without metachronous metastases (n = 37). We extracted 17 first-order (FO) histogram-based features from the pretreatment ADC maps. Student’s t-test and Mann–Whitney U test were used for the association between each FO feature and presence of MM. Statistically significant features were combined into a model, using the binary regression logistic method. The receiver operating curve analysis was used to determine the diagnostic performance of the individual parameters and combined model. There were significant differences in ADC 90th percentile, interquartile range, entropy, uniformity, variance, mean absolute deviation, and robust mean absolute deviation in patients with MM, as compared to those without MM (p values between 0.002–0.01). The best diagnostic was achieved by the 90th percentile and uniformity, yielding an AUC of 0.74 [95% CI: 0.60–0.8]). The combined model reached an AUC of 0.8 [95% CI: 0.66–0.90]. Our observations point out that ADC first-order features may be useful for predicting metachronous metastases in rectal cancer.

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Section: Discussionmentioning

confidence: 99%

“…Voxel-based histogram analysis of a VOI can assess the whole tumor volume and offer an objective overview of tumor heterogeneity [23,27]. Recent studies have used histogram analysis in different areas of cancer research, including rectal cancer [28][29][30][31][32].…”

Section: Introductionmentioning

confidence: 99%

The Utility of ADC First-Order Histogram Features for the Prediction of Metachronous Metastases in Rectal Cancer: A Preliminary Study

Boca

Caraiani

Popa

et al. 2022

Biology

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“…Hence, e imaging principle of DWI is to detect the Brownian motion and the direction changes of water molecules, providing information about tumor pathological changes, vascular permeability, cell integrity, and water molecule dispersion movement, etc. e ADC value is a quantitative parameter that reflects the dispersion of water molecules within cells and tissue [23]. When using DWI to evaluate the therapeutic effects of NCRT, Zhang et al [24] proposed that the ADC value after treatment increased first and then decreased.…”

Section: Discussionmentioning

confidence: 99%

Low-Rank Matrix Denoising Algorithm-Based MRI Image Feature for Therapeutic Effect Evaluation of NCRT on Rectal Cancer

2021

Journal of Healthcare Engineering

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This study aimed to explore the therapeutic effects of neoadjuvant chemoradiotherapy (NCRT) on rectal cancer patients using the MRI based on low-rank matrix denoising algorithm, which was then compared with the postoperative pathological examination to evaluate its application value in tumor staging after NCRT treatment. 15 patients with rectal cancer who met the requirements of radiotherapy and chemotherapy after conventional MRI were selected as the research subjects. The conventional MRI images before and after NCRT treatment were divided in two groups. One group was not processed and set as the conventional group; the other group was processed with low-rank matrix denoising algorithm and set as the optimized group. The two groups of images were observed for the changes in the ADC value and length and thickness of the tumor before and after NCRT treatment. The two groups were compared with the pathological examination for the complete remission of pathology (pCR) after the NCRT treatment and the tumor stage results. The results showed that Root Mean Square Error (RMSE) and Peak Signal to Noise Ratio (PSNR) (18.9121 and 74.9911 dB) after introducing the low-rank matrix denoising algorithm were significantly better than those before (20.1234 and 70.1234 dB) ( P < 0.05 ); there were notable differences in the tumor index data within the two groups before and after NCRT treatment ( P < 0.05 ), indicating that the NCRT treatment was effective. The pathological examination results of pCR data of the two groups were not much different ( P > 0.05 ); the examination results between the two groups were different, but no notable difference was noted ( P < 0.05 ); in the optimized group, there was no notable difference between the MRI results and the pathological examination results ( P < 0.05 ), while in the conventional group, there were notable differences in the MRI results and pathological examination results ( P < 0.05 ). In conclusion, MRI images based on low-rank matrix denoising algorithm are clearer, which can improve the diagnosis rate of patients and better display the changes of the microenvironment after NCRT treatment. It also indicates that NCRT treatment has significant clinical effects in the treatment of rectal cancer patients, which is worth promoting.

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“…Further, necrotic areas, cysts, and vessels related to each lesion at the corresponding slice were also avoided, as identified on T2WI images. In addition, the highest and lowest slices of the DWI images were excluded given their partial volume effects[ 24 ]. After nCRT, the tumor was defined by focal areas of residual high signal, as identified on the b1200 images within the location of the primary tumor bed and/or corresponding with the residual tumor on T2WI MRI images as a reference standard.…”

Section: Methodsmentioning

confidence: 99%

Whole lesion histogram analysis of apparent diffusion coefficient predicts therapy response in locally advanced rectal cancer

Santos¹,

Reyes-Pérez²,

Osorio³

et al. 2022

WJG

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BACKGROUND Whole-tumor apparent diffusion coefficient (ADC) histogram analysis is relevant to predicting the neoadjuvant chemoradiation therapy (nCRT) response in patients with locally advanced rectal cancer (LARC). AIM To evaluate the performance of ADC histogram-derived parameters for predicting the outcomes of patients with LARC. METHODS This is a single-center, retrospective study, which included 48 patients with LARC. All patients underwent a pre-treatment magnetic resonance imaging (MRI) scan for primary tumor staging and a second restaging MRI for response evaluation. The sample was distributed as follows: 18 responder patients (R) and 30 non-responders (non-R). Eight parameters derived from the whole-lesion histogram analysis (ADCmean, skewness, kurtosis, and ADC10 th , 25 th , 50 th , 75 th , 90 th percentiles), as well as the ADCmean from the hot spot region of interest (ROI), were calculated for each patient before and after treatment. Then all data were compared between R and non-R using the Mann-Whitney U test. Two measures of diagnostic accuracy were applied: the receiver operating characteristic curve and the diagnostic odds ratio (DOR). We also reported intra- and interobserver variability by calculating the intraclass correlation coefficient (ICC). RESULTS Post-nCRT kurtosis, as well as post-nCRT skewness, were significantly lower in R than in non-R (both P < 0.001, respectively). We also found that, after treatment, R had a larger loss of both kurtosis and skewness than non-R (∆%kurtosis and ∆skewness, P < 0.001). Other parameters that demonstrated changes between groups were post-nCRT ADC10 th , ∆%ADC10 th , ∆%ADCmean, and ROI ∆%ADCmean. However, the best diagnostic performance was achieved by ∆%kurtosis at a threshold of 11.85% (Area under the receiver operating characteristic curve [AUC] = 0.991, DOR = 376), followed by post-nCRT kurtosis = 0.78 × 10 -3 mm 2 /s (AUC = 0.985, DOR = 375.3), ∆skewness = 0.16 (AUC = 0.885, DOR = 192.2) and post-nCRT skewness = 1.59 × 10 -3 mm 2 /s (AUC = 0.815, DOR = 168.6). Finally, intraclass correlation coefficient analysis showed excellent intraobserver and interobserver agreement, ensuring the implementation of histogram analysis into routine clinical practice. CONCLUSION Whole-tumor ADC histogram parameters, particularly kurtosis and skewness, are relevant biomarkers for predicting the nCRT response in LARC. Both parameters appear to be more reliable than ADCmean from one-slice ROI.

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Rectal Cancer Invasiveness: Whole-Lesion Diffusion-Weighted Imaging (DWI) Histogram Analysis by Comparison of Reduced Field-of-View and Conventional DWI Techniques

Cited by 10 publications

References 40 publications

The Utility of ADC First-Order Histogram Features for the Prediction of Metachronous Metastases in Rectal Cancer: A Preliminary Study

The Utility of ADC First-Order Histogram Features for the Prediction of Metachronous Metastases in Rectal Cancer: A Preliminary Study

Low-Rank Matrix Denoising Algorithm-Based MRI Image Feature for Therapeutic Effect Evaluation of NCRT on Rectal Cancer

Whole lesion histogram analysis of apparent diffusion coefficient predicts therapy response in locally advanced rectal cancer

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