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Yang L, Yang J, Kleppe A, Danielsen HE, Kerr DJ. Personalizing adjuvant therapy for patients with colorectal cancer. Nat Rev Clin Oncol 2024; 21:67-79. [PMID: 38001356 DOI: 10.1038/s41571-023-00834-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/26/2023] [Indexed: 11/26/2023]
Abstract
The current standard-of-care adjuvant treatment for patients with colorectal cancer (CRC) comprises a fluoropyrimidine (5-fluorouracil or capecitabine) as a single agent or in combination with oxaliplatin, for either 3 or 6 months. Selection of therapy depends on conventional histopathological staging procedures, which constitute a blunt tool for patient stratification. Given the relatively marginal survival benefits that patients can derive from adjuvant treatment, improving the safety of chemotherapy regimens and identifying patients most likely to benefit from them is an area of unmet need. Patient stratification should enable distinguishing those at low risk of recurrence and a high chance of cure by surgery from those at higher risk of recurrence who would derive greater absolute benefits from chemotherapy. To this end, genetic analyses have led to the discovery of germline determinants of toxicity from fluoropyrimidines, the identification of patients at high risk of life-threatening toxicity, and enabling dose modulation to improve safety. Thus far, results from analyses of resected tissue to identify mutational or transcriptomic signatures with value as prognostic biomarkers have been rather disappointing. In the past few years, the application of artificial intelligence-driven models to digital images of resected tissue has identified potentially useful algorithms that stratify patients into distinct prognostic groups. Similarly, liquid biopsy approaches involving measurements of circulating tumour DNA after surgery are additionally useful tools to identify patients at high and low risk of tumour recurrence. In this Perspective, we provide an overview of the current landscape of adjuvant therapy for patients with CRC and discuss how new technologies will enable better personalization of therapy in this setting.
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Affiliation(s)
- Li Yang
- Department of Gastroenterology, Sichuan University, Chengdu, China
| | - Jinlin Yang
- Department of Gastroenterology, Sichuan University, Chengdu, China
| | - Andreas Kleppe
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway
- Department of Informatics, University of Oslo, Oslo, Norway
- Centre for Research-based Innovation Visual Intelligence, UiT The Arctic University of Norway, Tromsø, Norway
| | - Håvard E Danielsen
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway
- Radcliffe Department of Medicine, Oxford University, Oxford, UK
| | - David J Kerr
- Radcliffe Department of Medicine, Oxford University, Oxford, UK.
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Firmbach D, Benz M, Kuritcyn P, Bruns V, Lang-Schwarz C, Stuebs FA, Merkel S, Leikauf LS, Braunschweig AL, Oldenburger A, Gloßner L, Abele N, Eck C, Matek C, Hartmann A, Geppert CI. Tumor-Stroma Ratio in Colorectal Cancer-Comparison between Human Estimation and Automated Assessment. Cancers (Basel) 2023; 15:2675. [PMID: 37345012 DOI: 10.3390/cancers15102675] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Revised: 04/27/2023] [Accepted: 05/02/2023] [Indexed: 06/23/2023] Open
Abstract
The tumor-stroma ratio (TSR) has been repeatedly shown to be a prognostic factor for survival prediction of different cancer types. However, an objective and reliable determination of the tumor-stroma ratio remains challenging. We present an easily adaptable deep learning model for accurately segmenting tumor regions in hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of colon cancer patients into five distinct classes (tumor, stroma, necrosis, mucus, and background). The tumor-stroma ratio can be determined in the presence of necrotic or mucinous areas. We employ a few-shot model, eventually aiming for the easy adaptability of our approach to related segmentation tasks or other primaries, and compare the results to a well-established state-of-the art approach (U-Net). Both models achieve similar results with an overall accuracy of 86.5% and 86.7%, respectively, indicating that the adaptability does not lead to a significant decrease in accuracy. Moreover, we comprehensively compare with TSR estimates of human observers and examine in detail discrepancies and inter-rater reliability. Adding a second survey for segmentation quality on top of a first survey for TSR estimation, we found that TSR estimations of human observers are not as reliable a ground truth as previously thought.
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Affiliation(s)
- Daniel Firmbach
- Digital Health Systems Department, Fraunhofer-Institute for Integrated Circuits IIS, Am Wolfsmantel 33, 91058 Erlangen, Germany
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Michaela Benz
- Digital Health Systems Department, Fraunhofer-Institute for Integrated Circuits IIS, Am Wolfsmantel 33, 91058 Erlangen, Germany
| | - Petr Kuritcyn
- Digital Health Systems Department, Fraunhofer-Institute for Integrated Circuits IIS, Am Wolfsmantel 33, 91058 Erlangen, Germany
| | - Volker Bruns
- Digital Health Systems Department, Fraunhofer-Institute for Integrated Circuits IIS, Am Wolfsmantel 33, 91058 Erlangen, Germany
| | - Corinna Lang-Schwarz
- Institute of Pathology, Hospital Bayreuth, Preuschwitzer Str. 101, 95445 Bayreuth, Germany
| | - Frederik A Stuebs
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
- Department of Obstetrics and Gynaecology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Universitätsstraße 21-23, 91054 Erlangen, Germany
| | - Susanne Merkel
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
- Department of Surgery, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 12, 91054 Erlangen, Germany
| | - Leah-Sophie Leikauf
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Anna-Lea Braunschweig
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Angelika Oldenburger
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Laura Gloßner
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Niklas Abele
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Christine Eck
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Christian Matek
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Arndt Hartmann
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
| | - Carol I Geppert
- Institute of Pathology, University Hospital Erlangen, FAU Erlangen-Nuremberg, Krankenhausstr. 8-10, 91054 Erlangen, Germany
- Comprehensive Cancer Center Erlangen-EMN (CCC), University Hospital Erlangen, FAU Erlangen-Nuremberg, Östliche Stadtmauerstr. 30, 91054 Erlangen, Germany
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Kleppe A, Skrede OJ, De Raedt S, Hveem TS, Askautrud HA, Jacobsen JE, Church DN, Nesbakken A, Shepherd NA, Novelli M, Kerr R, Liestøl K, Kerr DJ, Danielsen HE. A clinical decision support system optimising adjuvant chemotherapy for colorectal cancers by integrating deep learning and pathological staging markers: a development and validation study. Lancet Oncol 2022; 23:1221-1232. [PMID: 35964620 DOI: 10.1016/s1470-2045(22)00391-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2022] [Revised: 06/20/2022] [Accepted: 06/23/2022] [Indexed: 11/21/2022]
Abstract
BACKGROUND The DoMore-v1-CRC marker was recently developed using deep learning and conventional haematoxylin and eosin-stained tissue sections, and was observed to outperform established molecular and morphological markers of patient outcome after primary colorectal cancer resection. The aim of the present study was to develop a clinical decision support system based on DoMore-v1-CRC and pathological staging markers to facilitate individualised selection of adjuvant treatment. METHODS We estimated cancer-specific survival in subgroups formed by pathological tumour stage (pT<4 or pT4), pathological nodal stage (pN0, pN1, or pN2), number of lymph nodes sampled (≤12 or >12) if not pN2, and DoMore-v1-CRC classification (good, uncertain, or poor prognosis) in 997 patients with stage II or III colorectal cancer considered to have no residual tumour (R0) from two community-based cohorts in Norway and the UK, and used these data to define three risk groups. An external cohort of 1075 patients with stage II or III R0 colorectal cancer from the QUASAR 2 trial was used for validation; these patients were treated with single-agent capecitabine. The proposed risk stratification system was evaluated using Cox regression analysis. We similarly evaluated a risk stratification system intended to reflect current guidelines and clinical practice. The primary outcome was cancer-specific survival. FINDINGS The new risk stratification system provided a hazard ratio of 10·71 (95% CI 6·39-17·93; p<0·0001) for high-risk versus low-risk patients and 3·06 (1·73-5·42; p=0·0001) for intermediate versus low risk in the primary analysis of the validation cohort. Estimated 3-year cancer-specific survival was 97·2% (95% CI 95·1-98·4; n=445 [41%]) for the low-risk group, 94·8% (91·7-96·7; n=339 [32%]) for the intermediate-risk group, and 77·6% (72·1-82·1; n=291 [27%]) for the high-risk group. The guideline-based risk grouping was observed to be less prognostic and informative (the low-risk group comprised only 142 [13%] of the 1075 patients). INTERPRETATION Integrating DoMore-v1-CRC and pathological staging markers provided a clinical decision support system that risk stratifies more accurately than its constituent elements, and identifies substantially more patients with stage II and III colorectal cancer with similarly good prognosis as the low-risk group in current guidelines. Avoiding adjuvant chemotherapy in these patients might be safe, and could reduce morbidity, mortality, and treatment costs. FUNDING The Research Council of Norway.
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Affiliation(s)
- Andreas Kleppe
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway; Department of Informatics, University of Oslo, Oslo, Norway
| | - Ole-Johan Skrede
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway; Department of Informatics, University of Oslo, Oslo, Norway
| | - Sepp De Raedt
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway; Department of Informatics, University of Oslo, Oslo, Norway
| | - Tarjei S Hveem
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway
| | - Hanne A Askautrud
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway
| | - Jørn E Jacobsen
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway; Department of Research and Development, Vestfold Hospital Trust, Tønsberg, Norway
| | - David N Church
- National Institute of Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, UK; Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
| | - Arild Nesbakken
- Department of Gastrointestinal Surgery, Oslo University Hospital, Oslo, Norway; Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Colorectal Cancer Research Centre, Oslo, Norway
| | - Neil A Shepherd
- Gloucestershire Cellular Pathology Laboratory, Cheltenham General Hospital, Cheltenham, UK
| | - Marco Novelli
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway; Research Department of Pathology, University College London, London, UK
| | - Rachel Kerr
- Department of Oncology, University of Oxford, Oxford, UK
| | - Knut Liestøl
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway; Department of Informatics, University of Oslo, Oslo, Norway
| | - David J Kerr
- Nuffield Division of Clinical Laboratory Sciences, University of Oxford, Oxford, UK
| | - Håvard E Danielsen
- Institute for Cancer Genetics and Informatics, Oslo University Hospital, Oslo, Norway; Department of Informatics, University of Oslo, Oslo, Norway; Nuffield Division of Clinical Laboratory Sciences, University of Oxford, Oxford, UK.
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