Development of a method for digital assessment of tumor regression grade in patients with rectal cancer following neoadjuvant therapy

Jepsen, Dea Natalie Munch; Høeg, Henrik; Thagaard, Jeppe; Walbech, Julie Sparholt; Gögenür, Ismail; Fiehn, Anne-Marie Kanstrup

doi:10.1016/j.jpi.2022.100152

Cited by 2 publications

(2 citation statements)

References 27 publications

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“…The strict criteria which also excluded the use of certain neoadjuvant agents such as antiangiogenic agents and monoclonal antibodies because of potential confounding effects on pathological response [37] also negatively impacted recruitment. Pathological assessment was by experienced observer assessment rather than using newer more objective digital pathology techniques [38]. Use of digitised histopathological objective assessment of whole lesion necrosis and cell density would be advantageous in future analyses.…”

Section: Discussionmentioning

confidence: 99%

MRI Apparent Diffusion Coefficient (ADC) as a Biomarker of Tumour Response: Imaging-Pathology Correlation in Patients with Hepatic Metastases from Colorectal Cancer (EORTC 1423)

Jackson,

Pathak,

deSouza

et al. 2023

Cancers

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Background: Tumour apparent diffusion coefficient (ADC) from diffusion-weighted magnetic resonance imaging (MRI) is a putative pharmacodynamic/response biomarker but the relationship between drug-induced effects on the ADC and on the underlying pathology has not been adequately defined. Hypothesis: Changes in ADC during early chemotherapy reflect underlying histological markers of tumour response as measured by tumour regression grade (TRG). Methods: Twenty-six patients were enrolled in the study. Baseline, 14 days, and pre-surgery MRI were performed per study protocol. Surgical resection was performed in 23 of the enrolled patients; imaging-pathological correlation was obtained from 39 lesions from 21 patients. Results: There was no evidence of correlation between TRG and ADC changes at day 14 (study primary endpoint), and no significant correlation with other ADC metrics. In scans acquired one week prior to surgery, there was no significant correlation between ADC metrics and percentage of viable tumour, percentage necrosis, percentage fibrosis, or Ki67 index. Conclusions: Our hypothesis was not supported by the data. The lack of meaningful correlation between change in ADC and TRG is a robust finding which is not explained by variability or small sample size. Change in ADC is not a proxy for TRG in metastatic colorectal cancer.

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Section: Discussionmentioning

confidence: 99%

MRI Apparent Diffusion Coefficient (ADC) as a Biomarker of Tumour Response: Imaging-Pathology Correlation in Patients with Hepatic Metastases from Colorectal Cancer (EORTC 1423)

Jackson,

Pathak,

deSouza

et al. 2023

Cancers

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“…As a matter of fact, identifying TRG2 and TRG3 patients, which are less responsive to NAT, might be more important, as NAT do not benefit these patients. Although there is a recent study ( Jepsen et al, 2022 ) that has developed a multi-class quantitative evaluation algorithm for pathological images of rectal cancer after neoadjuvant therapy, multi-class prediction algorithms have not been reported.…”

Section: Introductionmentioning

confidence: 99%

A multi-class classification algorithm based on hematoxylin-eosin staining for neoadjuvant therapy in rectal cancer: a retrospective study

Liu

et al. 2023

PeerJ

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Neoadjuvant therapy (NAT) is a major treatment option for locally advanced rectal cancer. With recent advancement of machine/deep learning algorithms, predicting the treatment response of NAT has become possible using radiological and/or pathological images. However, programs reported thus far are limited to binary classifications, and they can only distinguish the pathological complete response (pCR). In the clinical setting, the pathological NAT responses are classified as four classes: (TRG0-3), with 0 as pCR, 1 as moderate response, 2 as minimal response and 3 as poor response. Therefore, the actual clinical need for risk stratification remains unmet. By using ResNet (Residual Neural Network), we developed a multi-class classifier based on Hematoxylin-Eosin (HE) images to divide the response to three groups (TRG0, TRG1/2, and TRG3). Overall, the model achieved the AUC 0.97 at 40× magnification and AUC 0.89 at 10× magnification. For TRG0, the model under 40× magnification achieved a precision of 0.67, a sensitivity of 0.67, and a specificity of 0.95. For TRG1/2, a precision of 0.92, a sensitivity of 0.86, and a specificity of 0.89 were achieved. For TRG3, the model obtained a precision of 0.71, a sensitivity of 0.83, and a specificity of 0.88. To find the relationship between the treatment response and pathological images, we constructed a visual heat map of tiles using Class Activation Mapping (CAM). Notably, we found that tumor nuclei and tumor-infiltrating lymphocytes appeared to be potential features of the algorithm. Taken together, this multi-class classifier represents the first of its kind to predict different NAT responses in rectal cancer.

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Development of a method for digital assessment of tumor regression grade in patients with rectal cancer following neoadjuvant therapy

Cited by 2 publications

References 27 publications

MRI Apparent Diffusion Coefficient (ADC) as a Biomarker of Tumour Response: Imaging-Pathology Correlation in Patients with Hepatic Metastases from Colorectal Cancer (EORTC 1423)

MRI Apparent Diffusion Coefficient (ADC) as a Biomarker of Tumour Response: Imaging-Pathology Correlation in Patients with Hepatic Metastases from Colorectal Cancer (EORTC 1423)

A multi-class classification algorithm based on hematoxylin-eosin staining for neoadjuvant therapy in rectal cancer: a retrospective study

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