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Aherne S, Donnelly M, Ryan ÉJ, Davey MG, Creavin B, McGrath E, McCarthy A, Geraghty R, Gibbons D, Nagtegaal I, Lugli A, Kirsch R, Martin ST, Winter DC, Sheahan K. Tumour budding as a prognostic biomarker in biopsies and resections of neoadjuvant-treated rectal adenocarcinoma. Histopathology 2024; 85:224-243. [PMID: 38629323 DOI: 10.1111/his.15192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2023] [Revised: 03/02/2024] [Accepted: 03/30/2024] [Indexed: 07/16/2024]
Abstract
BACKGROUND Tumour 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 RESULTS A 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). CONCLUSION Assessment 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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Affiliation(s)
- Susan Aherne
- Centre for Colorectal Disease, St Vincent's University Hospital, Dublin, Ireland
- School of Medicine and Medical Sciences, University College Dublin, Dublin, Ireland
- International Tumour Budding Consortium Funded by the Dutch Cancer Society, Amsterdam, The Netherlands
| | - Mark Donnelly
- Centre for Colorectal Disease, St Vincent's University Hospital, Dublin, Ireland
- School of Medicine and Medical Sciences, University College Dublin, Dublin, Ireland
| | - Éanna J Ryan
- Centre for Colorectal Disease, St Vincent's University Hospital, Dublin, Ireland
- School of Medicine and Medical Sciences, University College Dublin, Dublin, Ireland
| | - Matthew G Davey
- Centre for Colorectal Disease, St Vincent's University Hospital, Dublin, Ireland
| | - Ben Creavin
- Centre for Colorectal Disease, St Vincent's University Hospital, Dublin, Ireland
- School of Medicine and Medical Sciences, University College Dublin, Dublin, Ireland
| | - Erinn McGrath
- Centre for Colorectal Disease, St Vincent's University Hospital, Dublin, Ireland
- School of Medicine and Medical Sciences, University College Dublin, Dublin, Ireland
| | - Aoife McCarthy
- Centre for Colorectal Disease, St Vincent's University Hospital, Dublin, Ireland
| | - Robert Geraghty
- Centre for Colorectal Disease, St Vincent's University Hospital, Dublin, Ireland
| | - David Gibbons
- Centre for Colorectal Disease, St Vincent's University Hospital, Dublin, Ireland
- School of Medicine and Medical Sciences, University College Dublin, Dublin, Ireland
- International Tumour Budding Consortium Funded by the Dutch Cancer Society, Amsterdam, The Netherlands
| | - Iris Nagtegaal
- International Tumour Budding Consortium Funded by the Dutch Cancer Society, Amsterdam, The Netherlands
| | - Alessandro Lugli
- International Tumour Budding Consortium Funded by the Dutch Cancer Society, Amsterdam, The Netherlands
| | - Richard Kirsch
- International Tumour Budding Consortium Funded by the Dutch Cancer Society, Amsterdam, The Netherlands
| | - Sean T Martin
- Centre for Colorectal Disease, St Vincent's University Hospital, Dublin, Ireland
- School of Medicine and Medical Sciences, University College Dublin, Dublin, Ireland
| | - Desmond C Winter
- Centre for Colorectal Disease, St Vincent's University Hospital, Dublin, Ireland
- School of Medicine and Medical Sciences, University College Dublin, Dublin, Ireland
| | - Kieran Sheahan
- Centre for Colorectal Disease, St Vincent's University Hospital, Dublin, Ireland
- School of Medicine and Medical Sciences, University College Dublin, Dublin, Ireland
- International Tumour Budding Consortium Funded by the Dutch Cancer Society, Amsterdam, The Netherlands
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Wang A, Zhou J, Wang G, Zhang B, Xin H, Zhou H. Deep learning of endoscopic features for the assessment of neoadjuvant therapy response in locally advanced rectal cancer. Asian J Surg 2023; 46:3568-3574. [PMID: 37062601 DOI: 10.1016/j.asjsur.2023.03.165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2022] [Revised: 03/28/2023] [Accepted: 03/30/2023] [Indexed: 04/18/2023] Open
Abstract
BACKGROUND For locally advanced rectal cancer (LARC), accurate response evaluation is necessary to select complete responders after neoadjuvant therapy (NAT) for a watch-and-wait (W&W) strategy. Algorithms based on deep learning have shown great value in medical image analyses. Here we used deep learning algorithms of endoscopic images for the assessment of NAT response in LARC. METHOD 214 LARC patients were retrospectively included in the study. After NAT, these patients underwent total mesorectal excision (TME) surgery. Among them, 51 (23.8%) of the patients achieved a pathological complete response (pCR). 160 patients from Shanghai Changzheng Hospital were regarded as primary dataset, and the other 54 patients from Zhejiang Cancer Hospital were regarded as validation dataset. ResNet-18 and DenseNet-121 were applied to train the models based on endoscopic images after NAT. Deep learning models were valid in the validation dataset and compared to manual method. RESULTS The performances were comparable in AUC between deep learning models and manual method. For mean metrics, sensitivity (0.750 vs. 0.417) and AUC (0.716 vs. 0.601) in ResNet-18 deep learning model were higher than those in the manual method. The deep learning models were able to identify the endoscopic features associated with NAT response by the heatmaps. A diagnostic flow diagram which integrated the deep learning model to assist the clinicians in making decisions for W&W strategy was constructed. CONCLUSIONS We created deep learning models using endoscopic features for assessment of NAT in LARC. The deep learning models achieved modest accuracies and performed comparably to manual method.
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Affiliation(s)
- Anqi Wang
- Department of Colorectal Surgery, Changzheng Hospital, Navy Medical University, China
| | - Jieli Zhou
- UM-SJTU Joint Institute, Shanghai Jiao Tong University, China
| | - Gang Wang
- Department of Colorectal Surgery, Zhejiang Cancer Hospital, China
| | - Beibei Zhang
- Department of Dermatology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, China.
| | - Hongyi Xin
- UM-SJTU Joint Institute, Shanghai Jiao Tong University, China.
| | - Haiyang Zhou
- Department of Colorectal Surgery, Changzheng Hospital, Navy Medical University, China.
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Wang A, Ding R, Zhang J, Zhang B, Huang X, Zhou H. Machine Learning of Histomorphological Features Predict Response to Neoadjuvant Therapy in Locally Advanced Rectal Cancer. J Gastrointest Surg 2023; 27:162-165. [PMID: 35915376 DOI: 10.1007/s11605-022-05409-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 06/25/2022] [Indexed: 02/01/2023]
Abstract
AIM We hypothesize that machine learning of histomorphological features can predict response to neoadjuvant therapy (NAT) in locally advanced rectal cancer (LARC). METHOD This retrospective study included 146 LARC patients who received NAT followed by surgery. The pathologists scanned the H&E slides of pretreatment tumor biopsy into whole slide images (WSIs). We randomly split patients into the primary and validation sets with a ratio of 80%:20%. We cut the WSIs into smaller parts (sample amount: 200-500) and used a convolutional neural network (CNN) to process these blocks directly. Then, a graph neural network (GNN) was applied to train the model in the primary set. The independent validation set was used to assess the performance of the model. RESULT Our model could provide indicative information to identify the patients who were most likely to benefit from NAT. When the sample amount reached 500, the tile-level classifier for distinguishing poor response from good response produced an AUC of 0.779 in the primary set and 0.733 in the validation set. CONCLUSION In this pilot study, we propose a novel predictive model of therapeutic response to NAT in LARC using a routine diagnostic tool employed in daily practice.
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Affiliation(s)
- Anqi Wang
- Department of Colorectal Surgery, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Ruiqi Ding
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China
| | - Jing Zhang
- Department of Pathology, Changzheng Hospital, Navy Medical University, Shanghai, China
| | - Beibei Zhang
- Department of Dermatology, Tongren Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| | - Xiaolin Huang
- Department of Automation, Shanghai Jiao Tong University, Shanghai, China.
| | - Haiyang Zhou
- Department of Colorectal Surgery, Changzheng Hospital, Navy Medical University, Shanghai, China.
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Lu S, Liu Z, Wang Y, Meng Y, Peng R, Qu R, Zhang Z, Fu W, Wang H. A novel prediction model for pathological complete response based on clinical and blood parameters in locally advanced rectal cancer. Front Oncol 2022; 12:932853. [PMID: 36505836 PMCID: PMC9727231 DOI: 10.3389/fonc.2022.932853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2022] [Accepted: 10/19/2022] [Indexed: 11/24/2022] Open
Abstract
Background The 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). Methods We 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. Results The 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). Conclusion We demonstrated that CBP predicting model have potential in predicting pCR to nCRT in patient with LARC.
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Affiliation(s)
- Siyi Lu
- Department of General Surgery, Peking University Third Hospital, Beijing, China
| | - Zhenzhen Liu
- Department of Thoracic Surgery, Beijing Jishuitan Hospital, Beijing, China
| | - Yuxia Wang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
| | - Yan Meng
- Department of General Surgery, Peking University Third Hospital, Beijing, China
| | - Ran Peng
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China
| | - Ruize Qu
- Department of General Surgery, Peking University Third Hospital, Beijing, China
| | - Zhipeng Zhang
- Department of General Surgery, Peking University Third Hospital, Beijing, China,*Correspondence: Hao Wang, ; Wei Fu, ; Zhipeng Zhang,
| | - Wei Fu
- Department of General Surgery, Peking University Third Hospital, Beijing, China,Cancer Center, Peking University Third Hospital, Beijing, China,*Correspondence: Hao Wang, ; Wei Fu, ; Zhipeng Zhang,
| | - Hao Wang
- Department of Radiation Oncology, Peking University Third Hospital, Beijing, China,Cancer Center, Peking University Third Hospital, Beijing, China,*Correspondence: Hao Wang, ; Wei Fu, ; Zhipeng Zhang,
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