The role of artificial intelligence to quantify the tumour-stroma ratio for survival in colorectal cancer

Smit, Marije; Mesker, Wilma E.

doi:10.1016/j.ebiom.2020.103070

Cited by 5 publications

(4 citation statements)

References 7 publications

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“…Moreover, the first step toward a deep learning model to quantify the TSR based on WSI of colorectal cancer tissue has also been taken [ 45 ]. Thus, further research into the automation of TSR analysis and deep learning models holds great opportunities [ 45 , 46 ].…”

Section: Discussionmentioning

confidence: 99%

Standardization of the tumor-stroma ratio scoring method for breast cancer research

Hagenaars

Vangangelt

Pelt

et al. 2022

Breast Cancer Res Treat

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Purpose The tumor-stroma ratio (TSR) has repeatedly proven to be correlated with patient outcomes in breast cancer using large retrospective cohorts. However, studies validating the TSR often show variability in methodology, thereby hampering comparisons and uniform outcomes. Method This paper provides a detailed description of a simple and uniform TSR scoring method using Hematoxylin and Eosin (H&E)-stained core biopsies and resection tissue, specifically focused on breast cancer. Possible histological challenges that can be encountered during scoring including suggestions to overcome them are reported. Moreover, the procedure for TSR estimation in lymph nodes, scoring on digital images and the automatic assessment of the TSR using artificial intelligence are described. Conclusion Digitized scoring of tumor biopsies and resection material offers interesting future perspectives to determine patient prognosis and response to therapy. The fact that the TSR method is relatively easy, quick, and cheap, offers great potential for its implementation in routine diagnostics, but this requires high quality validation studies.

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Section: Discussionmentioning

confidence: 99%

Standardization of the tumor-stroma ratio scoring method for breast cancer research

Hagenaars

Vangangelt

Pelt

et al. 2022

Breast Cancer Res Treat

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“…AI also has the potential to accurately assess the tumor-stroma ratio (TSR) and its association with survival outcomes in CRC [107]. The research utilizes deep learning techniques to train AI models on a large dataset of CRC histopathology images.…”

Section: Exploring the Potential Of Ai In Crc: Advancements Challenge...mentioning

confidence: 99%

Current Status and Emerging Trends in Colorectal Cancer Screening and Diagnostics

Beniwal,

Lamo,

Kaushik

et al. 2023

Biosensors

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Colorectal cancer (CRC) is a prevalent and potentially fatal disease categorized based on its high incidences and mortality rates, which raised the need for effective diagnostic strategies for the early detection and management of CRC. While there are several conventional cancer diagnostics available, they have certain limitations that hinder their effectiveness. Significant research efforts are currently being dedicated to elucidating novel methodologies that aim at comprehending the intricate molecular mechanism that underlies CRC. Recently, microfluidic diagnostics have emerged as a pivotal solution, offering non-invasive approaches to real-time monitoring of disease progression and treatment response. Microfluidic devices enable the integration of multiple sample preparation steps into a single platform, which speeds up processing and improves sensitivity. Such advancements in diagnostic technologies hold immense promise for revolutionizing the field of CRC diagnosis and enabling efficient detection and monitoring strategies. This article elucidates several of the latest developments in microfluidic technology for CRC diagnostics. In addition to the advancements in microfluidic technology for CRC diagnostics, the integration of artificial intelligence (AI) holds great promise for further enhancing diagnostic capabilities. Advancements in microfluidic systems and AI-driven approaches can revolutionize colorectal cancer diagnostics, offering accurate, efficient, and personalized strategies to improve patient outcomes and transform cancer management.

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“…Surgical resection combined with systemic chemotherapy facilitates the only chance of long-term survival. Moreover, decisions on surgery and adjuvant treatment should be based on an assessment of the tumor stage and surgery-related risks ( 2 , 3 ). However, patients with similar tumor stages based on the TNM categories have extremely different clinical outcomes ( 4 ).…”

Section: Introductionmentioning

confidence: 99%

CT Radiomics and Machine-Learning Models for Predicting Tumor-Stroma Ratio in Patients With Pancreatic Ductal Adenocarcinoma

Meng

Zhang

et al. 2021

Front. Oncol.

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PurposeTo develop and validate a machine learning classifier based on multidetector computed tomography (MDCT), for the preoperative prediction of tumor–stroma ratio (TSR) expression in patients with pancreatic ductal adenocarcinoma (PDAC).Materials and MethodsIn this retrospective study, 227 patients with PDAC underwent an MDCT scan and surgical resection. We quantified the TSR by using hematoxylin and eosin staining and extracted 1409 arterial and portal venous phase radiomics features for each patient, respectively. Moreover, we used the least absolute shrinkage and selection operator logistic regression algorithm to reduce the features. The extreme gradient boosting (XGBoost) was developed using a training set consisting of 167 consecutive patients, admitted between December 2016 and December 2017. The model was validated in 60 consecutive patients, admitted between January 2018 and April 2018. We determined the XGBoost classifier performance based on its discriminative ability, calibration, and clinical utility.ResultsWe observed low and high TSR in 91 (40.09%) and 136 (59.91%) patients, respectively. A log-rank test revealed significantly longer survival for patients in the TSR-low group than those in the TSR-high group. The prediction model revealed good discrimination in the training (area under the curve [AUC]= 0.93) and moderate discrimination in the validation set (AUC= 0.63). While the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for the training set were 94.06%, 81.82%, 0.89, 0.89, and 0.90, respectively, those for the validation set were 85.71%, 48.00%, 0.70, 0.70, and 0.71, respectively.ConclusionsThe CT radiomics-based XGBoost classifier provides a potentially valuable noninvasive tool to predict TSR in patients with PDAC and optimize risk stratification.

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The role of artificial intelligence to quantify the tumour-stroma ratio for survival in colorectal cancer

Cited by 5 publications

References 7 publications

Standardization of the tumor-stroma ratio scoring method for breast cancer research

Standardization of the tumor-stroma ratio scoring method for breast cancer research

Current Status and Emerging Trends in Colorectal Cancer Screening and Diagnostics

CT Radiomics and Machine-Learning Models for Predicting Tumor-Stroma Ratio in Patients With Pancreatic Ductal Adenocarcinoma

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