Perfusion CT can predict the response to chemoradiation therapy and survival in esophageal squamous cell carcinoma: initial clinical results

Identification of Tumor-Specific MRI Biomarkers Using Machine Learning (ML)

The identification of reliable and non-invasive oncology biomarkers remains a main priority in healthcare. There are only a few biomarkers that have been approved as diagnostic for cancer. The most frequently used cancer biomarkers are derived from either biological materials or imaging data. Most cancer biomarkers suffer from a lack of high specificity. However, the latest advancements in machine learning (ML) and artificial intelligence (AI) have enabled the identification of highly predictive, disease-specific biomarkers. Such biomarkers can be used to diagnose cancer patients, to predict cancer prognosis, or even to predict treatment efficacy. Herein, we provide a summary of the current status of developing and applying Magnetic resonance imaging (MRI) biomarkers in cancer care. We focus on all aspects of MRI biomarkers, starting from MRI data collection, preprocessing and machine learning methods, and ending with summarizing the types of existing biomarkers and their clinical applications in different cancer types.

Imaging biomarkers for the treatment of esophageal cancer

Esophageal cancer is known as one of the malignant cancers with poor prognosis. To improve the outcome, combined multimodality treatment is attempted. On the other hand, advances in genomics and other “omic” technologies are paving way to the patient-oriented treatment called “personalized” or “precision” medicine. Recent advancements of imaging techniques such as functional imaging make it possible to use imaging features as biomarker for diagnosis, treatment response, and prognosis in cancer treatment. In this review, we will discuss how we can use imaging derived tumor features as biomarker for the treatment of esophageal cancer.

The Comparison of Computed Tomography Perfusion, Contrast-Enhanced Computed Tomography and Positron-Emission Tomography/Computed Tomography for the Detection of Primary Esophageal Carcinoma

OBJECTIVE

The purpose of this study was to investigate the efficiency of computed tomography perfusion (CTP), contrast-enhanced computed tomography (CECT) and 18F-fluoro-2-deoxy-D-glucose (18F-FDG) positron-emission tomography (PET/CT) in the diagnosis of esophageal cancer.

SUBJECTS AND METHODS

This prospective study consisted of 33 patients with pathologically confirmed esophageal cancer, 2 of whom had an esophageal abscess. All the patients underwent CTP, CECT and PET/CT imaging and the imaging findings were evaluated. Sensitivity, specificity and positive and negative predictive values were calculated for each of the 3 imaging modalities relative to the histological diagnosis.

RESULTS

Thirty-three tumors were visualized on CTP, 29 on CECT and 27 on PET/CT. Six tumors were stage 1, and 2 and 4 of these tumors were missed on CECT and PET/CT, respectively. Significant differences between CTP and CECT (p = 0.02), and between CTP and PET/CT (p = 0.04) were found for stage 1 tumors. Values for the sensitivity, specificity and positive and negative predictive values on CTP were 100, 100, 100 and 100%, respectively. Corresponding values on CECT were 93.94, 0, 93.94 and 0%, respectively, and those on PET/CT were 87.88, 0, 93.55 and 0%, respectively. Hence, the sensitivity, specificity and positive and negative predictive values of CTP were better than those of CECT and PET/CT.

CONCLUSION

CTP had an advantage over CECT and PET/CT in detecting small lesions. CTP was valuable, especially in detecting stage 1 tumors.

Diffusion-weighted magnetic resonance imaging for predicting and detecting early response to chemoradiation therapy of squamous cell carcinomas of the head and neck

PURPOSE

The aim of this study was to investigate the utility of apparent diffusion coefficient (ADC) for prediction and early detection of treatment response in head and neck squamous cell carcinomas (HNSCC).

EXPERIMENTAL DESIGN

Diffusion-weighted magnetic resonance imaging studies were performed on 40 patients with newly diagnosed HNSCC before, during, and after the end of chemoradiation therapy. Analysis was done on data from 33 patients after exclusion of 7 patients that had incomplete data.

RESULTS

Pretreatment ADC value of complete responders (1.04 +/- 0.19 x 10(-3) mm2/s) was significantly lower (P < 0.05) than that from partial responders (1.35 +/- 0.30 x 10(-3) mm2/s). A significant increase in ADC was observed in complete responders within 1 week of treatment (P < 0.01), which remained high until the end of the treatment. The complete responders also showed significantly higher increase in ADC than the partial responders by the first week of chemoradiation (P < 0.01). When pretreatment ADC value was used for predicting treatment response, the area under the receiver operating characteristic curve was 0.80 with a sensitivity of 65% and a specificity of 86%. However, change in ADC within the first week of chemoradiation therapy resulted in an area under the receiver operating characteristic curve of 0.88 with 86% sensitivity and 83% specificity for prediction of treatment response.

CONCLUSIONS

These results suggest that ADC can be used as a marker for prediction and early detection of response to concurrent chemoradiation therapy in HNSCC.