Tumor Response to Treatment: Prediction and Assessment

Extracellular vesicles and particles impact the systemic landscape of cancer

Intercellular cross talk between cancer cells and stromal and immune cells is essential for tumor progression and metastasis. Extracellular vesicles and particles (EVPs) are a heterogeneous class of secreted messengers that carry bioactive molecules and that have been shown to be crucial for this cell-cell communication. Here, we highlight the multifaceted roles of EVPs in cancer. Functionally, transfer of EVP cargo between cells influences tumor cell growth and invasion, alters immune cell composition and function, and contributes to stromal cell activation. These EVP-mediated changes impact local tumor progression, foster cultivation of pre-metastatic niches at distant organ-specific sites, and mediate systemic effects of cancer. Furthermore, we discuss how exploiting the highly selective enrichment of molecules within EVPs has profound implications for advancing diagnostic and prognostic biomarker development and for improving therapy delivery in cancer patients. Altogether, these investigations into the role of EVPs in cancer have led to discoveries that hold great promise for improving cancer patient care and outcome.

Medical imaging and nuclear medicine: a Lancet Oncology Commission

The diagnosis and treatment of patients with cancer requires access to imaging to ensure accurate management decisions and optimal outcomes. Our global assessment of imaging and nuclear medicine resources identified substantial shortages in equipment and workforce, particularly in low-income and middle-income countries (LMICs). A microsimulation model of 11 cancers showed that the scale-up of imaging would avert 3·2% (2·46 million) of all 76·0 million deaths caused by the modelled cancers worldwide between 2020 and 2030, saving 54·92 million life-years. A comprehensive scale-up of imaging, treatment, and care quality would avert 9·55 million (12·5%) of all cancer deaths caused by the modelled cancers worldwide, saving 232·30 million life-years. Scale-up of imaging would cost US$6·84 billion in 2020-30 but yield lifetime productivity gains of $1·23 trillion worldwide, a net return of $179·19 per $1 invested. Combining the scale-up of imaging, treatment, and quality of care would provide a net benefit of $2·66 trillion and a net return of $12·43 per $1 invested. With the use of a conservative approach regarding human capital, the scale-up of imaging alone would provide a net benefit of $209·46 billion and net return of $31·61 per $1 invested. With comprehensive scale-up, the worldwide net benefit using the human capital approach is $340·42 billion and the return per dollar invested is $2·46. These improved health and economic outcomes hold true across all geographical regions. We propose actions and investments that would enhance access to imaging equipment, workforce capacity, digital technology, radiopharmaceuticals, and research and training programmes in LMICs, to produce massive health and economic benefits and reduce the burden of cancer globally.

Neo-adjuvant chemoradiotherapy response prediction using MRI based ensemble learning method in rectal cancer patients

OBJECTIVES

The aim of this study was to investigate and validate the performance of individual and ensemble machine learning models (EMLMs) based on magnetic resonance imaging (MRI) to predict neo-adjuvant chemoradiation therapy (nCRT) response in rectal cancer patients. We also aimed to study the effect of Laplacian of Gaussian (LOG) filter on EMLMs predictive performance.

METHODS

98 rectal cancer patients were divided into a training (n = 53) and a validation set (n = 45). All patients underwent MRI a week before nCRT. Several features from intensity, shape and texture feature sets were extracted from MR images. SVM, Bayesian network, neural network and KNN classifiers were used individually and together for response prediction. Predictive performance was evaluated using the area under the receiver operator characteristic (ROC) curve (AUC).

RESULTS

Patients' nCRT responses included 17 patients with Grade 0, 28 with Grade 1, 34 with Grade 2, and 19 with Grade 3 according to AJCC/CAP pathologic grading. In without preprocessing MR Image the best result was for Bayesian network classifier with AUC and accuracy of 75.2% and 80.9% respectively, which was confirmed in the validation set with an AUC and accuracy of 74% and 79% respectively. In EMLMs the best result was for 4 (SVM.NN.BN.KNN) classifier EMLM with AUC and accuracy of 97.8% and 92.8% in testing and 95% and 90% in validation set respectively.

CONCLUSIONS

In conclusion, we observed that machine learning methods can used to predict nCRT response in patients with rectal cancer. Preprocessing LOG filters and EL models can improve the prediction process.

Clinical and Pre-clinical Methods for Quantifying Tumor Hypoxia

Hypoxia, a prevalent characteristic of most solid malignant tumors, contributes to diminished therapeutic responses and more aggressive phenotypes. The term hypoxia has two definitions. One definition would be a physiologic state where the oxygen partial pressure is below the normal physiologic range. For most normal tissues, the normal physiologic range is between 10 and 20 mmHg. Hypoxic regions develop when there is an imbalance between oxygen supply and demand. The impact of hypoxia on cancer therapeutics is significant: hypoxic tissue is 3× less radiosensitive than normoxic tissue, the impaired blood flow found in hypoxic tumor regions influences chemotherapy delivery, and the immune system is dependent on oxygen for functionality. Despite the clinical implications of hypoxia, there is not a universal, ideal method for quantifying hypoxia, particularly cycling hypoxia because of its complexity and heterogeneity across tumor types and individuals. Most standard imaging techniques can be modified and applied to measuring hypoxia and quantifying its effects; however, the benefits and challenges of each imaging modality makes imaging hypoxia case-dependent. In this chapter, a comprehensive overview of the preclinical and clinical methods for quantifying hypoxia is presented along with the advantages and disadvantages of each.