Treatment Response In Oncology Research Articles

Low skeletal muscle mass predicts treatment response in oncology: a meta-analysis.

Low skeletal muscle mass (LSMM) predicts relevant clinical outcomes in oncologic patients. The purpose of this study was to perform a meta-analysis of data regarding associations between LSMM and treatment response (TR) in oncology. MEDLINE, Cochrane, and SCOPUS databases were screened for relationships between LSMM and TR in oncologic patients up to November 2022. Overall, 35 studies met the inclusion criteria. The meta-analysis was performed using RevMan 5.4 software. The collected 35 studies comprised 3858 patients. In 1682 patients (43.6%), LSMM was diagnosed. In the overall sample, LSMM predicted a negatively objective response rate (ORR), OR = 0.70, 95% CI = (0.54-0.91), p = 0.007, and disease control rate (DCR), OR = 0.69, 95% CI = (0.50-0.95), p = 0.02. In the curative setting, LSMM predicted a negatively ORR, OR = 0.24, 95% CI = (0.12-0.50), p = 0.0001, but not DCR, OR = 0.60, 95% CI = (0.31-1.18), p = 0.14. In palliative treatment with conventional chemotherapies, LSMM did not predict ORR: OR = 0.94, 95% CI (0.57-1.55), p = 0.81, and DCR: OR = 1.13, 95% CI (0.38-3.40), p = 0.82. In palliative treatment with tyrosine kinase inhibitors (TKI), LSMM did not predict TR: ORR, OR = 0.74, 95% CI (0.44-1.26), p = 0.27, and DCR, OR = 1.04, 95% CI (0.53-2.05), p = 0.90. In palliative immunotherapy, LSMM tended to predict ORR, OR = 0.74, 95% CI = (0.54-1.01), p = 0.06, and predicted DCR, OR = 0.53, 95% CI = (0.37-0.76), p = 0.0006. LSMM is a risk factor for poor TR in curative chemotherapy in the adjuvant and/or neoadjuvant setting. LSMM is a risk factor for treatment failure in treatment with immunotherapy. Finally, LSMM does not influence TR in palliative treatment with conventional chemotherapy and/or TKIs. • Low skeletal muscle mass (LSMM) predicts treatment response (TR) to chemotherapy in the adjuvant and/or neoadjuvant setting. • LSMM predicts TR in immunotherapy. • LSMM does not influence TR in palliative chemotherapy.

Real-world treatment response in Japanese patients with cancer using unstructured data from electronic health records

PurposeWe generated methods for evaluating clinical outcomes including treatment response in oncology using the unstructured data from electronic health records (EHR) in Japanese language.MethodsThis retrospective analysis used medical record database and administrative data of University of Miyazaki Hospital in Japan of patients with lung/breast cancer. Treatment response (objective response [OR], stable disease [SD] or progressive disease [PD]) was adjudicated by two evaluators using clinicians’ progress notes, radiology reports and pathological reports of 15 patients with lung cancer (training data set). For assessing key terms to describe treatment response, natural language processing (NLP) rules were created from the texts identified by the evaluators and broken down by morphological analysis. The NLP rules were applied for assessing data of other 70 lung cancer and 30 breast cancer patients, who were not adjudicated, to examine if any difference in using key terms exist between these patients.ResultsA total of 2,039 records in progress notes, 131 in radiology reports and 60 in pathological reports of 15 patients, were adjudicated. Progress notes were the most common primary source data for treatment assessment (60.7%), wherein, the most common key terms with high sensitivity and specificity to describe OR were “reduction/shrink”, for SD were “(no) remarkable change/(no) aggravation)” and for PD were “(limited) effect” and “enlargement/grow”. These key terms were also found in other larger cohorts of 70 patients with lung cancer and 30 patients with breast cancer.ConclusionThis study demonstrated that assessing response to anticancer therapy using Japanese EHRs is feasible by interpreting progress notes, radiology reports and Japanese key terms using NLP.

Evaluation of F-18 FDG radiopharmaceuticals through Molecular Docking and radiation effects

Fluorodeoxyglucose (FDG), marked with the most used Positron Emission Tomography (PET) radiopharmaceutical Fluorine-18 (F-18), is a glucose analog and is taken to living cells through membrane glucose carriers. F-18 FDG involvement in tissue is proportional to glucose use. In many cancers, there is increased glucose use due to increased gluten expression and hexokinase activity. F-18 FDG PET is a proven method for diagnosis, staging, re-staging, and evaluation of treatment response in oncology. The purpose of this study is to find the effect of ionizing radiation on proteins in the mechanism of action of FDG and determine to Molecular mechanisms of F-18 FDG accumulation in metabolism. In the study, two different models were used together, the first method, the study was Molecular Docking method for modeling molecules deconstructed and the structure of FDG was energy minimized by utilizing the density functional theory, and the B3LYP functional was used with 6-311G basis set. The second method was the Monte Carlo method for modeling ionizing radiation interactive with the potential routes of FDG metabolism within the cell. It was determined that the Gibbs free energy (ΔG) change was compatible with the ionizing radiation factors for binding of FDG to the aphthous regions of Glucose-6-phosphate isomerase (G1), hexokinase (G2), and glucose transporter-1 (G3) were selected. In this study, the strong binding of FDG to protein influences the effect of radiation on the active site of enzymes. The G1 and G3 shown in the study interacted with only one charged amino acid FDG, and the absence of an aromatic residue around it can be considered among the results of this study as the cause of the low protective effect against ionizing radiation.

18F]FDG-PET Evaluation of Spinal Pathology in Patients in Oncology: Pearls and Pitfalls for the Neuroradiologist.

[18F]FDG-PET is a widely used technique for specific evaluation of disease and treatment response in oncology. However, the principles behind [18F]FDG-PET imaging allow a wide-ranging array of benign and malignant pathologies to be identified on both initial and routine surveillance imaging. This is important for clinicians and radiologists, alike, in that effective and accurate evaluation of malignancy and metastatic disease, specifically involving the spine and central nervous system, is crucial. In this article, we review the normal and posttherapy appearance of the spine on [18F]FDG-PET, the various types and patterns of metastatic disease that involve the spine and spinal cord, and, finally, important spinal pathologies that may mimic malignancy on [18F]FDG-PET.

A high-throughput imaging platform to characterize extracellular pH in organotypic three-dimensional in vitro models of liver cancer.

Given the extraordinary nature of tumor metabolism in hepatocellular carcinoma and its impact on oncologic treatment response, this study introduces a novel high-throughput extracellular pH (pHe ) mapping platform using magnetic resonance spectroscopic imaging in a three-dimensional (3D) in vitro model of liver cancer. pHe mapping was performed using biosensor imaging of redundant deviation in shifts (BIRDS) on 9.4 T and 11.7 T MR scanners for validation purposes. 3D cultures of four liver cancer (HepG2, Huh7, SNU475, VX2) and one hepatocyte (THLE2) cell line were simultaneously analyzed (a) without treatment, (b) supplemented with 4.5 g/L d-glucose, and (c) treated with anti-glycolytic 3-bromopyruvate (6.25, 25, 50, 75, and 100 μM). The MR results were correlated with immunohistochemistry (GLUT-1, LAMP-2) and luminescence-based viability assays. Statistics included the unpaired t-test and ANOVA test. High-throughput pHe imaging with BIRDS for in vitro 3D liver cancer models proved feasible. Compared with non-tumorous hepatocytes (pHe = 7.1 ± 0.1), acidic pHe was revealed in liver cancer (VX2, pHe = 6.7 ± 0.1; HuH7, pHe = 6.8 ± 0.1; HepG2, pHe = 6.9 ± 0.1; SNU475, pHe = 6.9 ± 0.1), in agreement with GLUT-1 upregulation. Glucose addition significantly further decreased pHe in hyperglycolytic cell lines (VX2, HepG2, and Huh7, by 0.28, 0.06, and 0.11, respectively, all p < 0.001), whereas 3-bromopyruvate normalized tumor pHe in a dose-dependent manner without affecting viability. In summary, this study introduces a non-invasive pHe imaging platform for high-yield screening using a translational 3D liver cancer model, which may help reveal and target mechanisms of therapy resistance and inform personalized treatment of patients with hepatocellular carcinoma.

Reimagining T Staging Through Artificial Intelligence and Machine Learning Image Processing Approaches in Digital Pathology.

Tumor stage and grade, visually assessed by pathologists from evaluation of pathology images in conjunction with radiographic imaging techniques, have been linked to outcome, progression, and survival for a number of cancers. The gold standard of staging in oncology has been the TNM (tumor-node-metastasis) staging system. Though histopathological grading has shown prognostic significance, it is subjective and limited by interobserver variability even among experienced surgical pathologists. Recently, artificial intelligence (AI) approaches have been applied to pathology images toward diagnostic-, prognostic-, and treatment prediction-related tasks in cancer. AI approaches have the potential to overcome the limitations of conventional TNM staging and tumor grading approaches, providing a direct prognostic prediction of disease outcome independent of tumor stage and grade. Broadly speaking, these AI approaches involve extracting patterns from images that are then compared against previously defined disease signatures. These patterns are typically categorized as either (1) handcrafted, which involve domain-inspired attributes, such as nuclear shape, or (2) deep learning (DL)-based representations, which tend to be more abstract. DL approaches have particularly gained considerable popularity because of the minimal domain knowledge needed for training, mostly only requiring annotated examples corresponding to the categories of interest. In this article, we discuss AI approaches for digital pathology, especially as they relate to disease prognosis, prediction of genomic and molecular alterations in the tumor, and prediction of treatment response in oncology. We also discuss some of the potential challenges with validation, interpretability, and reimbursement that must be addressed before widespread clinical deployment. The article concludes with a brief discussion of potential future opportunities in the field of AI for digital pathology and oncology.

Evaluation of the Use of Radiomics in Predicting Treatment Response in Oncology: (Review Artical)

Evaluation of the Use of Radiomics in Predicting Treatment Response in Oncology: (Review Artical)

Measurement Repeatability of 18F-FDG PET/CT Versus 18F-FDG PET/MRI in Solid Tumors of the Pelvis

Knowledge of the within-subject variability of 18F-FDG PET/MRI measurements is necessary for proper interpretation of quantitative PET or MRI metrics in the context of therapeutic efficacy assessments with integrated PET/MRI scanners. The goal of this study was to determine the test–retest repeatability of these metrics on PET/MRI, with comparison to similar metrics acquired by PET/CT. Methods: This prospective study enrolled subjects with pathology-proven pelvic malignancies. Baseline imaging consisted of PET/CT immediately followed by PET/MRI, using a single 370-MBq 18F-FDG dose. Repeat imaging was performed within 7 d using an identical imaging protocol, with no oncologic therapy between sessions. PET imaging on both scanners consisted of a list-mode acquisition at a single pelvic station. The MRI consisted of 2-point Dixon imaging for attenuation correction, standard sequences for anatomic correlation, and diffusion-weighted imaging. PET data were statically reconstructed using various frame durations and minimizing uptake time differences between sessions. SUV metrics were extracted for both PET/CT and PET/MRI in each imaging session. Apparent diffusion coefficient (ADC) metrics were extracted for both PET/MRI sessions. Results: The study cohort consisted of 14 subjects (13 female, 1 male) with various pelvic cancers (11 cervical, 2 rectal, 1 endometrial). For SUVmax, the within-subject coefficient of variation (wCV) appeared higher for PET/CT (8.5%–12.8%) than PET/MRI (6.6%–8.7%) across all PET reconstructions, though with no significant repeatability differences (all P values ≥ 0.08) between modalities. For lean body mass-adjusted SUVpeak, the wCVs appeared similar for PET/CT (9.9%–11.5%) and PET/MRI (9.2%–11.3%) across all PET reconstructions, again with no significant repeatability differences (all P values ≥ 0.14) between modalities. For PET/MRI, the wCV for ADCmedian of 3.5% appeared lower than the wCVs for SUVmax (6.6%–8.7%) and SULpeak (9.2%–11.3%), though without significant repeatability differences (all P values ≥ 0.23). Conclusion: For solid tumors of the pelvis, the repeatability of the evaluated SUV and ADC metrics on 18F-FDG PET/MRI is both acceptably high and similar to previously published values for 18F-FDG PET/CT and MRI, supporting the use of 18F-FDG PET/MRI for quantitative oncologic treatment response assessments.

Evaluation of [18F]CP18 as a Substrate-Based Apoptosis Imaging Agent for the Assessment of Early Treatment Response in Oncology.

The substrate-based positron emission tomography (PET) tracer [18F]CP18 is capable of detecting the activity of caspase-3/7, two key executioner proteases in the apoptosis pathway, through selective cleavage of the ligand by the activated proteases and subsequent accumulation in apoptotic cells. Using an in vitro and in vivo model of colorectal cancer (CRC), we investigated whether [18F]CP18 tracer accumulation provides a measure for apoptosis and reliably reflects early treatment response to chemotherapeutics. [18F]CP18 cell uptake was assessed in treated Colo205 cells (saline, 5-fluorouracil (5-FU), irinotecan or their combination) and correlated with caspase-3/7 activity. [18F]CP18 imaging was performed in Colo205 xenografts, starting with a baseline μPET/microX-ray computed tomography (​μCT) scan, followed by a 3-day treatment with saline (n=5), 5-FU (low sensitivity, n=4), irinotecan (high sensitivity, n=5), or a combination of both (n=7). The study was concluded with a second [18F]CP18 scan, 24h after final treatment administration, followed by tumor removal for gamma counting (%ID/g) and for cleaved caspase-3 immunohistochemistry (apoptotic index/necrosis). Tumors were delineated on μCT images and, using the obtained volumes of interest, average percentage injected dose per cubic centimeter (%ID/cm3) was calculated from every μPET image. In vitro, [18F]CP18 cell uptake was positively correlated with caspase-3/7 activity (r=0.59, p=0.003). A drug-dependent increase in [18F]CP18 tumor uptake compared to baseline was observed in animals treated with 5-FU (+14±25%), irinotecan (+56±54%), and their combination (+158±69%, p=0.002). %ID/cm3 showed a positive relationship with both %ID/g (r=0.83, p<0.0001) and the apoptotic index (r=0.60, p=0.004), but not with tumor necrosis (r=0.22, p=0.36). Both our in vitro and in vivo findings have shown the ability of [18F]CP18-PET to visualize therapy-induced cancer cell apoptosis and possibly serve as a biomarker for early therapy response.

Perfusion CT imaging of treatment response in oncology

Perfusion CT was first described in the 1970s but has become accepted as a clinical technique in recent years. In oncological practice Perfusion CT allows the downstream effects of therapies on the tumour vasculature to be monitored. From the dynamic changes in tumour and vascular enhancement following intravenous iodinated contrast agent administration, qualitative and quantitative parameters may be derived that reflect tumour perfusion, blood volume, and microcirculatory changes with treatment. This review outlines the mechanisms of action of available therapies and state-of-the-art imaging practice.

Diffusion-weighted magnetic resonance imaging: biomarker for treatment response in oncology

The authors report a case where a quantitative assessment of the apparent diffusion coefficient (ADC) of liver metastasis in a patient undergoing chemotherapy has shown to be an effective early marker for predicting therapeutic response, anticipating changes in tumor size. A lesion with lower initial ADC value and early increase in such value in the course of the treatment tends to present a better therapeutic response.

Highlights of This Issue

The unique antigenic nature of chemically induced tumors is well established. In murine models, vaccination with 1 tumor provides protection from challenge with the same, but not other histologically identical tumors. Twitty and colleagues report that tumor-derived autophagosome vaccines, in contrast with whole-tumor vaccines, prime T cells with broader tumor-specific reactivity and provide significant cross-protection to challenge with similar tumors. Further, they show that ubiquitinated short-lived proteins, sequestered in autophagosomes in a p62-dependent fashion, contain the key antigenic components required for cross-protection. This strategy to broaden anticancer immunity is being explored in a clinical trial.Several recent clinical trials have shown successful results in the treatment of advanced thyroid cancer. All of the agents being evaluated in these trials are multikinase inhibitors targeting VEGF receptor 2 (VEGFR2) plus other receptor tyrosine kinases. Jin and colleagues report here on a preclinical study of a RAF inhibitor and a dual phosphoinositide 3-kinase (PI3) kinase/mTOR inhibitor that successfully inhibits 2 of the major downstream pathways of ras. Addition of the PI3 kinase inhibitor was effective even in BRAF mutant settings. This strategy may be a viable alternative to multikinase inhibitors in thyroid cancer.X-ray repair complementing defective repair in Chinese hamster cell 1 (XRCC1) protein is involved in DNA base excision and single strand break repair and may modulate chemoradiation outcomes by affecting efficient DNA damage repair. Ang and colleagues explored the relationship of XRCC1 protein expression with clinical outcomes in head and neck squamous cell carcinoma by immunohistochemistry. High XRCC1 expression was associated with poorer survival, particularly in patients undergoing chemoradiation. Furthermore, this association was independent of human papilloma virus status, clinical stage, and tumor primary site. Thus, patients with high XRCC1 expression before treatment may have poor outcomes from chemoradiation and may benefit from alternative management strategies.In vivo imaging of cellular proliferation with the biomarker [18F]fluorothymidine (FLT) and positron emission tomography has tremendous potential for monitoring treatment response in oncology. Wardak and colleagues investigated whether changes in tumor FLT kinetic parameters, assessed early after the start of therapy, could predict overall survival and progression-free survival in patients with recurrent high-grade glioma undergoing treatment with bevacizumab and irinotecan. They found that discriminant analysis of kinetic parameter changes could accurately classify patients into their known survival groups. This method could help physicians provide a personalized treatment regimen and thus improve patient outcomes.

Diffusion Magnetic Resonance Imaging: A Biomarker for Treatment Response in Oncology

Imaging of response to oncology treatments, either on clinical protocol or as part of standard practice, is a complicated process that has evolved during the last 10 years due to the improvement of existing imaging technologies and the introduction of newer modalities. Diffusion magnetic resonance imaging is a technique that measures the mobility of water within tissues and, as such, may function as a surrogate marker for both tissue cellularity and response to treatment that occur earlier than usual measures of tumor response. This review highlights the development of this technique and the state of current clinical understanding of its utility.

Évaluation de la réponse au traitement par imagerie fonctionnelle

Imaging in cancer plays a capital role to guide the clinician in his choice of therapies. We will discuss the new techniques available to predict and evaluate treatment response in oncology. The method of reference to evaluate treatment response is based on the measure of lesion size. Functional imaging doesn't evaluate size, but rather a physiological or molecular feature, which is probably modified earlier in response to treatment. Dynamic contrast-enhanced functional imaging of microcirculation follows the biodistribution of a contrast agent and analyses tumour vascularization. Diffusion-weighted Magnetic Resonance Imaging differentiates free and restrained water molecules in tissues, reflecting tumor cellularity. Nuclear Magnetic Resonance spectroscopy is an application of MRI that yields information on the metabolic content of a tissue. It detects relative quantities of various molecules which differ in tumour compared to normal tissue. Positon-emission tomography using 18FDG is a nuclear medicine technique which gives information on tissue metabolism. Captation of FDG is proportional to the proliferative activity and the number of viable cells in a tumour. Human studies concerning these techniques are still quite preliminary, and the medical community must determine their potential in clinical practice to evaluate treatment response in oncology.

Radiolabeled annexin-V for monitoring treatment response in oncology.

Because of its potential to allow for noninvasive, repetitive, and selective in vivo identification of the site and extent of apoptotic cell death and for monitoring cell death kinetics without the need for invasive biopsy, radiolabeled annexin-V is of major clinical relevance. This paper reviews available preclinical and clinical data on radiolabeled annexin-V pertaining to the domain of monitoring response to radiotherapy and chemotherapy, focusing especially on advantages and drawbacks of the different labeling procedures for the radiolabeling of annexin-V.

Radiological evaluation of oncologic treatment response: current update

Radiological evaluation of oncologic treatment response: current update