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

Amodeo, Salvatore; Rosman, Alan S.; Desiato, Vincenzo; Hindman, Nicole; Newman, Elliot; Berman, Russell S.; Pachter, H. Leon; Melis, Marcovalerio

doi:10.2214/ajr.17.19135

Cited by 33 publications

(30 citation statements)

References 73 publications

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“…One common observer (author AT) delineated on both THUNDER and CLINIC cohorts. Observers (median experience, 4 years; range [1][2][3][4][5][6][7][8][9][10] were trained by a resident radiation oncologist to identify relevant normal and abnormal anatomical structures within the ADC maps. In addition, original CT scans with annotated lesions for all the patients were available to the observers, so that they could be guided in the delineations in the ADC maps.…”

Section: Gross Tumour Volume (Gtv) Delineationmentioning

confidence: 99%

“…Diffusion-weighted imaging in MRI allows construction of 3D maps of apparent diffusion coefficient (ADC) of water molecules, that are promising markers of internal tumour pores and cellular interstices wherein water molecules can migrate [8]. A change in mean value of ADC has been shown to be associated with tumour response in a number of different cancers, including LARC [9,10]. Joye et al showed that combined PET (Positron Emission Tomography) and MRI imaging parameters were strongly associated with pCR or near-pCR [11].…”

Section: Introductionmentioning

confidence: 99%

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Stability of radiomic features of apparent diffusion coefficient (ADC) maps for locally advanced rectal cancer in response to image pre-processing

et al. 2019

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Quantitative imaging features (radiomics) extracted from apparent diffusion coefficient (ADC) maps of rectal cancer patients can provide additional information to support treatment decision. Most available radiomic computational packages allow extraction of hundreds to thousands of features. However, two major factors can influence the reproducibility of radiomic features: interobserver variability, and imaging filtering applied prior to features extraction. In this exploratory study we seek to determine to what extent various commonly-used features are reproducible with regards to the mentioned factors using ADC maps from two different clinics (56 patients). Features derived from intensity distribution histograms are less sensitive to manual tumour delineation differences, noise in ADC images, pixel size resampling and intensity discretization. Shape features appear to be strongly affected by delineation quality. On the whole, textural features appear to be poorly or moderately reproducible with respect to the image pre-processing perturbations we reproduced.

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Section: Gross Tumour Volume (Gtv) Delineationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Stability of radiomic features of apparent diffusion coefficient (ADC) maps for locally advanced rectal cancer in response to image pre-processing

et al. 2019

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“…From a histopathological point of view, we divided our population into two groups: R and non-R patients, obtaining statistically significant differences between basal and post-nCRT ADC values (minimum and mean) in R, while no statistically significant differences were found for either ADC min or ADC mean in non-R patients. In addition, no changes were observed in both minimum and mean basal ADC values between R and non-R, confirming that ADC values at baseline are not good indicators of treatment response in LARC ( 7 ). On the other hand, numerous studies of rectal cancer that have investigated ADC changes after nCRT support our observations in the R group ( 27 , 28 , 31 – 33 ).…”

Section: Discussionmentioning

confidence: 71%

“…One of the latest advancements in MRI technology, the apparent diffusion coefficient (ADC), which is obtained from diffusion-weighted imaging (DWI), has been proposed to increase the specificity of MRI in the evaluation of treatment response after nCTR ( 6 – 11 ).…”

Section: Introductionmentioning

confidence: 99%

The apparent diffusion coefficient is a useful biomarker in predicting treatment response in patients with locally advanced rectal cancer

Santos

Reyes-Pérez

Sandoval-Nava

et al. 2020

Acta Radiologica Open

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Background Apparent diffusion coefficient (ADC) values achieve promising results in treatment response prediction in patients with several types of cancers. Purpose To determine whether ADC values predict neoadjuvant chemoradiation treatment (nCRT) response in patients with locally advanced rectal cancer (LARC). Material and Methods Forty-four patients with LARC who underwent magnetic resonance imaging scans before and after nCRT followed by delayed surgery were enrolled retrospectively. The sample was distributed as follows: responders (R), n = 8; and non-responders (Non-R), n = 36. Three markers of treatment response were considered: post-nCRT measures; ΔADC; and Δ%ADC. Statistical analysis included a Wilcoxon test, a Mann–Whitney U test, and a receiver operating characteristic (ROC) analysis in order to evaluate the diagnostic accuracy for each ADC value marker to differentiate between R and Non-R. Results Both minimum and mean ADC values were significantly higher after nCRT in the R group, while non-significant differences between basal and control ADC values were found in the non-R group. In addition, ΔADC and Δ%ADC exhibited increased values after nCRT in R when compared with non-R. ROC analysis revealed the following diagnostic performance parameters: post-nCRT: ADCmin = 1.05 × 10−3 mm2/s (sensitivity 61.1% and specificity 66.7%), ADCmean = 1.50 × 10−3 mm2/s (sensitivity 72.2% and specificity 83.3%), ΔADC: ADCmin = 0.35 (sensitivity 66.7% and specificity 83.3%), ADCmean = 0.50 (sensitivity 72% and specificity 83%); and Δ%ADC: ADCmin = 44% (sensitivity 66.7% and specificity 83.3%) and ADCmean = 60% (sensitivity 83% and specificity 99%). Conclusion Our findings suggest that post-treatment rectal tumor ADC values, as well changes between pre- and post-treatment values, may be biomarkers for predicting treatment response in patients with LARC who underwent nCRT.

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“…5). ADC maps are widely investigated in oncologic imaging to estimate treatment response with promising results [38,39].…”

Section: Functional Imaging: 18-fdg Pet and Diffusionweighted Mrimentioning

confidence: 99%

Imaging response evaluation after novel neoadjuvant treatments of pancreatic cancer

Gassner

Poskaite

2019

Eur Surg

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Background Emergent neoadjuvant concepts have improved the rate of margin-free (R0) resections in borderline resectable and locally advanced pancreatic ductal adenocarcinoma. However, due to specific properties of pancreatic cancer, imaging response assessment is difficult. Methods The concepts of histologic response patterns and their imaging correlates are outlined, together with efforts in morphologic and functional imaging to overcome the challenges of response assessment. Results Imaging evaluation after neoadjuvant therapy remains a diagnostic dilemma. To date, better understanding of the specific transformations of PDAC under neoadjuvant agents could not be transformed into validated imaging criteria for defining response and selecting surgical candidates. Conclusion Currently, patients with non-metastatic borderline resectable and locally advanced pancreatic ductal adenocarcinoma are brought to diagnostic laparotomy in the absence of overt radiologic progression after neoadjuvant therapy. Apart from morphologic patterns, ongoing research is focused on defining robust functional imaging parameters for response assessment.

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

Cited by 33 publications

References 73 publications

Stability of radiomic features of apparent diffusion coefficient (ADC) maps for locally advanced rectal cancer in response to image pre-processing

Stability of radiomic features of apparent diffusion coefficient (ADC) maps for locally advanced rectal cancer in response to image pre-processing

The apparent diffusion coefficient is a useful biomarker in predicting treatment response in patients with locally advanced rectal cancer

Imaging response evaluation after novel neoadjuvant treatments of pancreatic cancer

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