Can histologic features predict neoadjuvant therapy response in rectal adenocarcinoma?

Ono, Yuho; Cates, Justin M.; González, Raúl

doi:10.1016/j.prp.2021.153608

Cited by 4 publications

(4 citation statements)

References 38 publications

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“…A recent research showed that tumor differentiation grade presents the same predictive role for pCR (26). However, Ono et al did not observe such associations (27). We found that tumor differentiation grade (P = 0.02) was an independent risk factor for pCR in our study.…”

Section: Discussioncontrasting

confidence: 73%

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A novel prediction model for pathological complete response based on clinical and blood parameters in locally advanced rectal cancer

Liu

Wang

et al. 2022

Front. Oncol.

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BackgroundThe aim of this study was to investigate whether clinical and blood parameters can be used for predicting pathological complete response (pCR) to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC).MethodsWe retrospectively enrolled 226 patients with LARC [allocated in a 7:3 ratio to a training (n = 158) or validation (n = 68) cohort] who received nCRT before radical surgery. Backward stepwise logistic regression was performed to identify clinical and blood parameters associated with achieving pCR. Models based on clinical parameters (CP), blood parameters (BP), and clinical-blood parameters (CBP) were constructed for comparison with previously reported Tan’s model. The performance of the four models was evaluated by receiver operating characteristic (ROC) curve analysis, calibration, and decision curve analysis (DCA) in both cohorts. A dynamic nomogram was constructed for the presentation of the best model.ResultsThe CP and BP models based on multivariate logistic regression analysis showed that interval, Grade, CEA and fibrinogen–albumin ratio index (FARI), sodium-to-globulin ratio (SGR) were the independent clinical and blood predictors for achieving pCR, respectively. The area under the ROC curve of the CBP model achieved a score of 0.818 and 0.752 in both cohorts, better than CP (0.762 and 0.589), BP (0.695 and 0.718), Tan (0.738 and 0.552). CBP also showed better calibration and DCA than other models in both cohorts. Moreover, CBP revealed significant improvement compared with other models in training cohort (P < 0.05), and CBP showed significant improvement compared with CP and Tan’s model in validation cohort (P < 0.05).ConclusionWe demonstrated that CBP predicting model have potential in predicting pCR to nCRT in patient with LARC.

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Section: Discussioncontrasting

confidence: 73%

“…However, Ono et al. did not observe such associations ( 27 ). We found that tumor differentiation grade (P = 0.02) was an independent risk factor for pCR in our study.…”

Section: Discussionmentioning

confidence: 96%

A novel prediction model for pathological complete response based on clinical and blood parameters in locally advanced rectal cancer

Liu

Wang

et al. 2022

Front. Oncol.

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“…Few studies have evaluated TB in post‐neoadjuvant rectal cancer cases. Of those assessing biopsy ITB, 39‐42 one recent study found that biopsy TB did not predict TRG in this setting 45 . This study did not have patient outcome data.…”

Section: Discussionmentioning

confidence: 91%

“…Of those assessing biopsy ITB, [39][40][41][42] one recent study found that biopsy TB did not predict TRG in this setting. 45 This study did not have patient outcome data. Also, using the ITBCC 2016 classification, 100 of 105 included cases were in the Bd1 group, negating the effect of TB as a potential biomarker.…”

Section: Discussionmentioning

confidence: 94%

Tumour budding as a prognostic biomarker in biopsies and resections of neoadjuvant‐treated rectal adenocarcinoma

Aherne,

Donnelly,

Ryan

et al. 2024

Histopathology

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BackgroundTumour budding (TB) is a marker of tumour aggressiveness which, when measured in rectal cancer resection specimens, predicts worse outcomes and response to neoadjuvant therapy. We investigated the utility of TB assessment in the setting of neoadjuvant treatment.Methods and resultsA single‐centre, retrospective cohort study was conducted. TB was assessed using the hot‐spot International Tumour Budding Consortium (ITBCC) method and classified by the revised ITBCC criteria. Haematoxylin and eosin (H&E) and AE1/AE3 cytokeratin (CK) stains for ITB (intratumoural budding) in biopsies with PTB (peritumoural budding) and ITB (intratumoural budding) in resection specimens were compared. Logistic regression assessed budding as predictors of lymph node metastasis (LNM). Cox regression and Kaplan–Meier analyses investigated their utility as a predictor of disease‐free (DFS) and overall (OS) survival. A total of 146 patients were included; 91 were male (62.3%). Thirty‐seven cases (25.3%) had ITB on H&E and 79 (54.1%) had ITB on CK assessment of biopsy tissue. In univariable analysis, H&E ITB [odds (OR) = 2.709, 95% confidence interval (CI) = 1.261–5.822, P = 0.011] and CK ITB (OR = 2.165, 95% CI = 1.076–4.357, P = 0.030) predicted LNM. Biopsy‐assessed H&E ITB (OR = 2.749, 95% CI = 1.258–6.528, P = 0.022) was an independent predictor of LNM. In Kaplan–Meier analysis, ITB identified on biopsy was associated with worse OS (H&E, P = 0.003, CK: P = 0.009) and DFS (H&E, P = 0.012; CK, P = 0.045). In resection specimens, CK PTB was associated with worse OS (P = 0.047), and both CK PTB and ITB with worse DFS (PTB, P = 0.014; ITB: P = 0.019). In multivariable analysis H&E ITB predicted OS (HR = 2.930, 95% CI = 1.261–6.809) and DFS (HR = 2.072, 95% CI = 1.031–4.164). CK PTB grading on resection also independently predicted OS (HR = 3.417, 95% CI = 1.45–8.053, P = 0.005).ConclusionAssessment of TB using H&E and CK may be feasible in rectal cancer biopsy and post‐neoadjuvant therapy‐treated resection specimens and is associated with LNM and worse survival outcomes. Future management strategies for rectal cancer might be tailored to incorporate these findings.

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Machine Learning of Histomorphological Features Predict Response to Neoadjuvant Therapy in Locally Advanced Rectal Cancer

Wang

Zhang

et al. 2023

Journal of Gastrointestinal Surgery

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Can histologic features predict neoadjuvant therapy response in rectal adenocarcinoma?

Cited by 4 publications

References 38 publications

A novel prediction model for pathological complete response based on clinical and blood parameters in locally advanced rectal cancer

A novel prediction model for pathological complete response based on clinical and blood parameters in locally advanced rectal cancer

Tumour budding as a prognostic biomarker in biopsies and resections of neoadjuvant‐treated rectal adenocarcinoma

Machine Learning of Histomorphological Features Predict Response to Neoadjuvant Therapy in Locally Advanced Rectal Cancer

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