Predicting Treatment Response of Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Using Amide Proton Transfer MRI Combined With Diffusion-Weighted Imaging

Chen, Weicui; Mao, Liting; Li, Ling; Wei, Qiurong; Hu, Shaowei; Ye, Yongsong; Feng, Jieping; Liu, Bo; Liu, Xian

doi:10.3389/fonc.2021.698427

Cited by 11 publications

(7 citation statements)

References 45 publications

(63 reference statements)

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“…These studies (16,19,27) have found additional valuable perspectives using multi-modal MRI analysis. Furthermore, in some studies (29), tumor regression degree (mrTRG) on MRI was determined based on changes in tumor size and signal intensity on T1, T2, and WI to predict the outcome of NCRT. Unfortunately, as demonstrated, there is no pathological gold standard.…”

Section: Introductionmentioning

confidence: 99%

MRI-based pre-Radiomics and delta-Radiomics models accurately predict the post-treatment response of rectal adenocarcinoma to neoadjuvant chemoradiotherapy

Wang

Wu²,

Tian³

et al. 2023

Front. Oncol.

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ObjectivesTo develop and validate magnetic resonance imaging (MRI)-based pre-Radiomics and delta-Radiomics models for predicting the treatment response of local advanced rectal cancer (LARC) to neoadjuvant chemoradiotherapy (NCRT).MethodsBetween October 2017 and August 2022, 105 LARC NCRT-naïve patients were enrolled in this study. After careful evaluation, data for 84 patients that met the inclusion criteria were used to develop and validate the NCRT response models. All patients received NCRT, and the post-treatment response was evaluated by pathological assessment. We manual segmented the volume of tumors and 105 radiomics features were extracted from three-dimensional MRIs. Then, the eXtreme Gradient Boosting algorithm was implemented for evaluating and incorporating important tumor features. The predictive performance of MRI sequences and Synthetic Minority Oversampling Technique (SMOTE) for NCRT response were compared. Finally, the optimal pre-Radiomics and delta-Radiomics models were established respectively. The predictive performance of the radionics model was confirmed using 5-fold cross-validation, 10-fold cross-validation, leave-one-out validation, and independent validation. The predictive accuracy of the model was based on the area under the receiver operator characteristic (ROC) curve (AUC).ResultsThere was no significant difference in clinical factors between patients with good and poor reactions. Integrating different MRI modes and the SMOTE method improved the performance of the radiomics model. The pre-Radiomics model (train AUC: 0.93 ± 0.06; test AUC: 0.79) and delta-Radiomcis model (train AUC: 0.96 ± 0.03; test AUC: 0.83) all have high NCRT response prediction performance by LARC. Overall, the delta-Radiomics model was superior to the pre-Radiomics model.ConclusionMRI-based pre-Radiomics model and delta-Radiomics model all have good potential to predict the post-treatment response of LARC to NCRT. Delta-Radiomics analysis has a huge potential for clinical application in facilitating the provision of personalized therapy.

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Section: Introductionmentioning

confidence: 99%

MRI-based pre-Radiomics and delta-Radiomics models accurately predict the post-treatment response of rectal adenocarcinoma to neoadjuvant chemoradiotherapy

Wang

Wu²,

Tian³

et al. 2023

Front. Oncol.

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“…As a non-invasive imaging technique reflecting microscopic tissue changes, APTw imaging has been applied to diagnose cervical cancer, central nervous system tumors, prostate cancer, endometrial cancer, and rectal cancer (19)(20)(21)(27)(28)(29)(30). APT value is positively correlated with the degree of malignancy, i.e., the higher the degree of malignancy, the greater the APT value.…”

Section: Discussionmentioning

confidence: 99%

Amide proton transfer weighted imaging combined with dynamic contrast-enhanced MRI in predicting lymphovascular space invasion and deep stromal invasion of IB1-IIA1 cervical cancer

Song

Song²,

Ma³

et al. 2022

Front. Oncol.

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ObjectivesTo investigate the value of amide proton transfer weighted (APTw) imaging combined with dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) in predicting intermediate-risk factors of deep stromal invasion (DSI) and lymphovascular vascular space invasion (LVSI) in cervical cancer.MethodsSeventy patients with cervical cancer who underwent MRI before operation from July 2019 to February 2022 were retrospectively included in this study. Clinical information including age, histologic subtype etc. were recorded for patients. ATPw imaging parameter APTmean and DCE-MRI parameters Ktrans, Kep and Ve were measured and analyzed. The independent-sample t-test, Mann-Whitney U test, or Chi-square test was used to compare the differences of parameters between DSI/LVSI positive and negative groups. Logistic analysis was used to develop a combined predictive model. The receiver operating characteristic curve was for predictive performance. ANOVA and Kruskal-Wallis test were used to compare the differences of consecutive parameters among multiple groups.ResultsKtrans and SCC-Ag were independent factors in predicting DSI; Ktrans+SCC-Ag had the highest AUC 0.819 with sensitivity and specificity of 71.74% and 91.67%, respectively. APTmean and Ktrans were independent factors in predicting LVSI; APTmean+Ktrans had the highest AUC 0.874 with sensitivity and specificity of 92.86% and 75.00%, respectively. Ktrans and Ve could discriminate coexistence of DSI and LVSI from presence of single one, APTmean could discriminate the presence of DSI or LVSI from no risk factor presence.ConclusionThe combination of APTw and DCE-MRI is valuable in predicting intermediate-risk factors of DSI and LVSI in cervical cancer.

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“…After nCRT, the ability to achieve tumor reduction or even a pathological complete response (pCR) is observed in approximately 75% of treated patients, whereas the remainder exhibited no treatment response[ 3 , 4 ]. The ability to predict the response to nCRT is important for patients with potentially curable LARC who wish to explore personalized treatment to expand their therapeutic outcomes[ 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…Functional magnetic resonance imaging (MRI) techniques, such as diffusion-weighted imaging (DWI), can provide additional physiological information about a tumor’s cellular environment, offering great potential to evaluate the therapeutic response to nCRT[ 5 ]. This is because the apparent diffusion coefficient (ADC), a quantitative parameter used to assess water diffusion through tissue in DWI, shows an inverse relationship with tissue cellularity[ 6 ].…”

Section: Introductionmentioning

confidence: 99%

“…The possibility that ADC may be associated with the nCRT response has been amply investigated in LARC[ 8 - 12 ]; however, significant correlations have not been found in any studies to date[ 10 ]. Inconsistencies in previous findings may be due to a lack of standardized imaging and acquisition techniques[ 5 , 11 ], but they may also be due to the fact that the ADC measurements were performed using a manually drawn region of interest (ROI) from a single slice of the ADC map, which holds limited ability to reflect the actual whole-tumor characteristics[ 13 - 15 ].…”

Section: Introductionmentioning

confidence: 99%

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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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Predicting Treatment Response of Neoadjuvant Chemoradiotherapy in Locally Advanced Rectal Cancer Using Amide Proton Transfer MRI Combined With Diffusion-Weighted Imaging

Cited by 11 publications

References 45 publications

MRI-based pre-Radiomics and delta-Radiomics models accurately predict the post-treatment response of rectal adenocarcinoma to neoadjuvant chemoradiotherapy

MRI-based pre-Radiomics and delta-Radiomics models accurately predict the post-treatment response of rectal adenocarcinoma to neoadjuvant chemoradiotherapy

Amide proton transfer weighted imaging combined with dynamic contrast-enhanced MRI in predicting lymphovascular space invasion and deep stromal invasion of IB1-IIA1 cervical cancer

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

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