Histopathological atlas of desmoplastic reaction characterization in colorectal cancer

Ueno, Hideki; Kajiwara, Yoshiki; Ajioka, Youichi; Sugai, Tamotsu; Sekine, Shigeki; Ishiguro, Megumi; Takashima, Atsuo; Kanemitsu, Yukihide

doi:10.1093/jjco/hyab040

Cited by 40 publications

(41 citation statements)

References 36 publications

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“…First, this study was a retrospective cohort, and selection bias cannot be avoided. Second, some studies have demonstrated that desmoplastic reaction (DR) classification is associated with the prognosis of colorectal cancer 54,55 . However, we cannot clarify the relationship between these four collagen features and the DR category due to the endoscopic forceps being unable to obtain tumor biopsy tissue from the intrinsic muscle layer.…”

Section: Discussionmentioning

confidence: 97%

Association of the collagen signature with pathological complete response in rectal cancer patients

et al. 2022

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Collagen in the tumor microenvironment is recognized as a potential biomarker for predicting treatment response. This study investigated whether the collagen features are associated with pathological complete response (pCR) in locally advanced rectal cancer (LARC) patients receiving neoadjuvant chemoradiotherapy (nCRT) and develop and validate a prediction model for individualized prediction of pCR. The prediction model was developed in a primary cohort (353 consecutive patients). In total, 142 collagen features were extracted from the multiphoton image of pretreatment biopsy, and the least absolute shrinkage and selection operator (Lasso) regression was applied for feature selection and collagen signature building. A nomogram was developed using multivariable analysis. The performance of the nomogram was assessed with respect to its discrimination, calibration, and clinical utility. An independent cohort (163 consecutive patients) was used to validate the model. The collagen signature comprised four collagen features significantly associated with pCR both in the primary and validation cohorts (p < 0.001). Predictors in the individualized prediction nomogram included the collagen signature and clinicopathological predictors.The nomogram showed good discrimination with area under the ROC curve (AUC)

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Section: Discussionmentioning

confidence: 97%

Association of the collagen signature with pathological complete response in rectal cancer patients

et al. 2022

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“…29 The current study showed that the largest single myxoid stroma area ACP was found to be the most robust prognostic factor, surpassing the significance of both manual DR and automatically detected DR categorised according to the manually determined cutoff value. For manual DR classification via microscopy, the minimum amount of myxoid stroma in immature type is defined as a microscopic field of 409 objective lens, 27 which corresponds to 0.196 mm 2 . Since the area of myxoid stroma within the field of view of a microscope is challenging to assess, this definition is clinically reasonable.…”

Section: Discussionmentioning

confidence: 99%

Deep‐learning‐based classification of desmoplastic reaction on H&E predicts poor prognosis in oesophageal squamous cell carcinoma

et al. 2022

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Aims Desmoplastic reaction (DR) categorisation has been shown to be a promising prognostic factor in oesophageal squamous cell carcinoma (ESCC). The usual DR evaluation is performed using semiquantitative scores, which can be subjective. This study aimed to investigate whether a deep‐learning classifier could be used for DR classification. We further assessed the prognostic significance of the deep‐learning classifier and compared it to that of manual DR reporting and other pathological factors currently used in the clinic. Methods and results From 222 surgically resected ESCC cases, 31 randomly selected haematoxylin–eosin‐digitised whole slides of patients with immature DR were used to train and develop a deep‐learning classifier. The classifier was trained for 89 370 iterations. The accuracy of the deep‐learning classifier was assessed to 30 unseen cases, and the results revealed a Dice coefficient score of 0.81. For survival analysis, the classifier was then applied to the entire cohort of patients, which was split into a training (n = 156) and a test (n = 66) cohort. The automated DR classification had a higher prognostic significance for disease‐specific survival than the manually classified DR in both the training and test cohorts. In addition, the automated DR classification outperformed the prognostic accuracy of the gold‐standard factors of tumour depth and lymph node metastasis. Conclusions This study demonstrated that DR can be objectively and quantitatively assessed in ESCC using a deep‐learning classifier and that automatically classed DR has a higher prognostic significance than manual DR and other features currently used in the clinic.

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“…The altered function of CAF induced the transcription of DCN and the leakage into the circulation after degradation, resulting in the attenuation of local DCN and elevation of plasma DCN simultaneously, at least in some patients. Indeed, the expression of stromal DCN was attenuated in the myxoid periductal stromal architecture [ 23 ], produced by CAF [ 24 , 25 ]. Since the stromal expression of DCN exerted protective functions in tumor growth and tumor spreading [ 26 , 27 ], the local delivery of DCN could be a potent novel therapy for inhibiting tumor progression and spreading.…”

Section: Discussionmentioning

confidence: 99%

Plasma Levels of Decorin Increased in Patients during the Progression of Breast Cancer

Hosoya

Oda

Nakagawa

et al. 2021

JCM

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Decorin (DCN), an extracellular matrix proteoglycan found in tumor surrounding tissues, is a natural inhibitor of tumor cell proliferation and invasion. We conducted a cross-sectional observation study to evaluate the association of the pathological stage with the levels of DCN in plasma or tumor surrounding tissue. Among 118 patients who underwent breast surgery, 35 were designated as carcinoma in situ (Stage 0), 39 were Stage I, and 44 were Stage II or III. The stromal expression of DCN was quantified using a semiquantitative digital image analysis after immunohistochemical staining. The concentration of DCN was evaluated with a specific ELISA. As we have previously shown, stromal DCN expression was attenuated in the patients with Stage I, whereas stromal and plasma DCN was elevated paradoxically in those with Stage II/III. The elevated plasma DCN is an independent predictive factor of Stage II/III by the multivariate logistic regression analysis. The plasma level of DCN was negatively correlated with stromal DCN expression only in patients with advanced disease (Stage II/III). The plasma level of DCN could become a useful biomarker for patients in the advanced stages. Extensive studies and further assessments are warranted for evaluating the prognostic significance and tumor characteristics to understand the clinical significances of stromal and systemic DCN.

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Histopathological atlas of desmoplastic reaction characterization in colorectal cancer

Cited by 40 publications

References 36 publications

Association of the collagen signature with pathological complete response in rectal cancer patients

Association of the collagen signature with pathological complete response in rectal cancer patients

Deep‐learning‐based classification of desmoplastic reaction on H&E predicts poor prognosis in oesophageal squamous cell carcinoma

Plasma Levels of Decorin Increased in Patients during the Progression of Breast Cancer

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