Radiomics for Response and Outcome Assessment for Non-Small Cell Lung Cancer.

Routine follow-up visits and radiographic imaging are required for outcome evaluation and tumor recurrence monitoring. Yet more personalized surveillance is required in order to sufficiently address the nature of heterogeneity in nonsmall cell lung cancer and possible recurrences upon completion of treatment. Radiomics, an emerging noninvasive technology using medical imaging analysis and data mining methodology, has been adopted to the area of cancer diagnostics in recent years. Its potential application in response assessment for cancer treatment has also drawn considerable attention. Radiomics seeks to extract a large amount of valuable information from patients' medical images (both pretreatment and follow-up images) and quantitatively correlate image features with diagnostic and therapeutic outcomes. Radiomics relies on computers to identify and analyze vast amounts of quantitative image features that were previously overlooked, unmanageable, or failed to be identified (and recorded) by human eyes. The research area has been focusing on the predictive accuracy of pretreatment features for outcome and response and the early discovery of signs of tumor response, recurrence, distant metastasis, radiation-induced lung injury, death, and other outcomes, respectively. This review summarized the application of radiomics in response assessments in radiotherapy and chemotherapy for non-small cell lung cancer, including image acquisition/reconstruction, region of interest definition/segmentation, feature extraction, and feature selection and classification. The literature search for references of this article includes PubMed peer-reviewed publications over the last 10 years on the topics of radiomics, textural features, radiotherapy, chemotherapy, lung cancer, and response assessment. Summary tables of radiomics in response assessment and treatment outcome prediction in radiation oncology have been developed based on the comprehensive review of the literature

Exploration of PET and MRI radiomic features for decoding breast cancer phenotypes and prognosis.

Radiomics is an emerging technology for imaging biomarker discovery and disease-specific personalized treatment management. This paper aims to determine the benefit of using multi-modality radiomics data from PET and MR images in the characterization breast cancer phenotype and prognosis. Eighty-four features were extracted from PET and MR images of 113 breast cancer patients. Unsupervised clustering based on PET and MRI radiomic features created three subgroups. These derived subgroups were statistically significantly associated with tumor grade (p = 2.0 × 10-6), tumor overall stage (p = 0.037), breast cancer subtypes (p = 0.0085), and disease recurrence status (p = 0.0053). The PET-derived first-order statistics and gray level co-occurrence matrix (GLCM) textural features were discriminative of breast cancer tumor grade, which was confirmed by the results of L2-regularization logistic regression (with repeated nested cross-validation) with an estimated area under the receiver operating characteristic curve (AUC) of 0.76 (95% confidence interval (CI) = [0.62, 0.83]). The results of ElasticNet logistic regression indicated that PET and MR radiomics distinguished recurrence-free survival, with a mean AUC of 0.75 (95% CI = [0.62, 0.88]) and 0.68 (95% CI = [0.58, 0.81]) for 1 and 2 years, respectively. The MRI-derived GLCM inverse difference moment normalized (IDMN) and the PET-derived GLCM cluster prominence were among the key features in the predictive models for recurrence-free survival. In conclusion, radiomic features from PET and MR images could be helpful in deciphering breast cancer phenotypes and may have potential as imaging biomarkers for prediction of breast cancer recurrence-free survival

Predicting lung cancer recurrence from circulating tumour DNA. Commentary on 'Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution'

No abstract available

RAS mutation status predicts survival and patterns of recurrence in patients undergoing hepatectomy for colorectal liver metastases.

ObjectiveTo determine the impact of RAS mutation status on survival and patterns of recurrence in patients undergoing curative resection of colorectal liver metastases (CLM) after preoperative modern chemotherapy.BackgroundRAS mutation has been reported to be associated with aggressive tumor biology. However, the effect of RAS mutation on survival and patterns of recurrence after resection of CLM remains unclear.MethodsSomatic mutations were analyzed using mass spectroscopy in 193 patients who underwent single-regimen modern chemotherapy before resection of CLM. The relationship between RAS mutation status and survival outcomes was investigated.ResultsDetected somatic mutations included RAS (KRAS/NRAS) in 34 (18%), PIK3CA in 13 (7%), and BRAF in 2 (1%) patients. At a median follow-up of 33 months, 3-year overall survival (OS) rates were 81% in patients with wild-type versus 52.2% in patients with mutant RAS (P = 0.002); 3-year recurrence-free survival (RFS) rates were 33.5% with wild-type versus 13.5% with mutant RAS (P = 0.001). Liver and lung recurrences were observed in 89 and 83 patients, respectively. Patients with RAS mutation had a lower 3-year lung RFS rate (34.6% vs 59.3%, P < 0.001) but not a lower 3-year liver RFS rate (43.8% vs 50.2%, P = 0.181). In multivariate analyses, RAS mutation predicted worse OS [hazard ratio (HR) = 2.3, P = 0.002), overall RFS (HR = 1.9, P = 0.005), and lung RFS (HR = 2.0, P = 0.01), but not liver RFS (P = 0.181).ConclusionsRAS mutation predicts early lung recurrence and worse survival after curative resection of CLM. This information may be used to individualize systemic and local tumor-directed therapies and follow-up strategies

Emerging evidence for CHFR as a cancer biomarker : from tumor biology to precision medicine

Novel insights in the biology of cancer have switched the paradigm of a "one-size-fits-all" cancer treatment to an individualized biology-driven treatment approach. In recent years, a diversity of biomarkers and targeted therapies has been discovered. Although these examples accentuate the promise of personalized cancer treatment, for most cancers and cancer subgroups no biomarkers and effective targeted therapy are available. The great majority of patients still receive unselected standard therapies with no use of their individual molecular characteristics. Better knowledge about the underlying tumor biology will lead the way toward personalized cancer treatment. In this review, we summarize the evidence for a promising cancer biomarker: checkpoint with forkhead and ring finger domains (CHFR). CHFR is a mitotic checkpoint and tumor suppressor gene, which is inactivated in a diverse group of solid malignancies, mostly by promoter CpG island methylation. CHFR inactivation has shown to be an indicator of poor prognosis and sensitivity to taxane-based chemotherapy. Here we summarize the current knowledge of altered CHFR expression in cancer, the impact on tumor biology and implications for personalized cancer treatment

BIOINFORMATIC ANALYSIS OF PROTEOMIC AND GENOMIC DATA FROM NSCLC TUMORS ON PROGNOSTIC AND PREDICTIVE FACTORS OF IMMUNOTHERAPY TREATMENT

Recent lung cancer research has led to advancements in molecular immunology, resulting in development of small molecule inhibitors, or immune checkpoint inhibitors, that propagate an anti-tumor T cell response. Despite increased overall and progression-free survival with reduced adverse effects compared to traditional chemotherapy, treating advanced stage lung adenocarcinoma patients remains non-curative, and evidence of non-responders or tumor recurrence to immune checkpoint inhibitor therapy is growing. Also, compared to traditional chemotherapy, there is a lower percentage of patients who respond to small molecule inhibitors. In this analysis of proteomic and genomic data from The Cancer Proteome Atlas and Global Data Commons cancer databases, as well as clinical outcomes data from Phase II POPLAR and Phase III OAK clinical trials, we discuss possible prognostic and predictive factors of immunotherapy in the treatment of advanced non-small-cell lung carcinoma