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

Current Status and Emerging Trends in Colorectal Cancer Screening and Diagnostics

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.

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

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.

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

Purpose

To 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 Methods

In 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.

Results

We 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.

Conclusions

The CT radiomics-based XGBoost classifier provides a potentially valuable noninvasive tool to predict TSR in patients with PDAC and optimize risk stratification.

Developing a clinical decision support system based on the fuzzy logic and decision tree to predict colorectal cancer

Background: Colorectal Cancer (CRC) is the most prevalent digestive system- related cancer and has become one of the deadliest diseases worldwide. Given the poor prognosis of CRC, it is of great importance to make a more accurate prediction of this disease. Early CRC detection using computational technologies can significantly improve the overall survival possibility of patients. Hence this study was aimed to develop a fuzzy logic-based clinical decision support system (FL-based CDSS) for the detection of CRC patients. Methods: This study was conducted in 2020 using the data related to CRC and non-CRC patients, which included the 1162 cases in the Masoud internal clinic, Tehran, Iran. The chi-square method was used to determine the most important risk factors in predicting CRC. Furthermore, the C4.5 decision tree was used to extract the rules. Finally, the FL-based CDSS was designed in a MATLAB environment and its performance was evaluated by a confusion matrix. Results: Eleven features were selected as the most important factors. After fuzzification of the qualitative variables and evaluation of the decision support system (DSS) using the confusion matrix, the accuracy, specificity, and sensitivity of the system was yielded 0.96, 0.97, and 0.96, respectively. Conclusion: We concluded that developing the CDSS in this field can provide an earlier diagnosis of CRC, leading to a timely treatment, which could decrease the CRC mortality rate in the community.