Tumor proportion in colon cancer: results from a semiautomatic image analysis approach.
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ABSTRACT:
SUBMITTER: Martin B
PROVIDER: S-EPMC7985049 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundMany modifiable lifestyle factors have been associated with colon cancer risk, but less is known about their effect on disease when considered together. Estimating the proportion of colon cancer cases that could be prevented by the adoption of combined modifiable lifestyle behaviors will provide important insights into disease prevention.MethodsIn the Nurses' Health Study, we defined a low-risk group according to a combination of six factors: body mass index < 25 kg/m(2), physical activity of ≥ 21 metabolic equivalent of task per week, alcohol consumption ≤ 30 g/day, cigarette smoking <10 pack-years before the age of 30, current use of multivitamins for ≥ 15 years, and total calcium intake ≥ 700 mg/day. A composite risk score index was created and the population attributable risk (PAR%) was calculated after accounting for other known risk or protective factors.ResultsWe documented 1,127 colon cancer cases among 81,092 over 24 years of follow-up. Compared with women in the lowest risk category, the women at all other exposure levels had a hazard ratio of colon cancer of 1.81 (95% confidence interval 1.15-2.85). The score index was significantly and linearly related to an increasing risk of colon cancer (p value for trend <0.0001). The PAR% of the six risk factors considered together in relation to colon cancer was 0.37 (95% CI 0.09-0.60). When regular aspirin use (two tablets/week for six or more years) was included with the other low-risk behaviors, the PAR% increased to 0.43 (95% CI 0.14-0.65).ConclusionsBeyond the known benefit from colonoscopy/sigmoidoscopy, key behavior modifications and adherence to a healthy lifestyle could avoid approximately 37% of colon cancer cases among women.
| S-EPMC4545459 | biostudies-literature
Project description:PurposeTo propose a deep learning network with subregion partition for predicting metastatic origins and EGFR/HER2 status in patients with brain metastasis.MethodsWe retrospectively enrolled 140 patients with clinico-pathologically confirmed brain metastasis originated from primary NSCLC (n = 60), breast cancer (BC, n = 60) and other tumor types (n = 20). All patients underwent contrast-enhanced brain MRI scans. The brain metastasis was subdivided into phenotypically consistent subregions using patient-level and population-level clustering. A residual network with a global average pooling layer (RN-GAP) was proposed to calculate deep learning-based features. Features from each subregion were selected with least absolute shrinkage and selection operator (LASSO) to build logistic regression models (LRs) for predicting primary tumor types (LR-NSCLC for the NSCLC origin and LR-BC for the BC origin), EGFR mutation status (LR-EGFR) and HER2 status (LR-HER2).ResultsThe brain metastasis can be partitioned into a marginal subregion (S1) and an inner subregion (S2) in the MRI image. The developed models showed good predictive performance in the training (AUCs, LR-NSCLC vs. LR-BC vs. LR-EGFR vs. LR-HER2, 0.860 vs. 0.909 vs. 0.850 vs. 0.900) and validation (AUCs, LR-NSCLC vs. LR-BC vs. LR-EGFR vs. LR-HER2, 0.819 vs. 0.872 vs. 0.750 vs. 0.830) set.ConclusionOur proposed deep learning network with subregion partitions can accurately predict metastatic origins and EGFR/HER2 status of brain metastasis, and hence may have the potential to be non-invasive and preoperative new markers for guiding personalized treatment plans in patients with brain metastasis.
| S-EPMC9382021 | biostudies-literature
Project description:Organotypic, three-dimensional (3D) cancer models have enabled investigations of complex microtissues in increasingly realistic conditions. However, a drawback of these advanced models remains the poor biological relevance of cancer cell lines, while higher clinical significance would be obtainable with patient-derived cell cultures. Here, we describe the generation and data analysis of 3D microtissue models from patient-derived xenografts (PDX) of non-small cell lung carcinoma (NSCLC). Standard of care anti-cancer drugs were applied and the altered multicellular morphologies were captured by confocal microscopy, followed by automated image analyses to quantitatively measure phenotypic features for high-content chemosensitivity tests. The obtained image data were thresholded using a local entropy filter after which the image foreground was split into local regions, for a supervised classification into tumor or fibroblast cell types. Robust statistical methods were applied to evaluate treatment effects on growth and morphology. Both novel and existing computational approaches were compared at each step, while prioritizing high experimental throughput. Docetaxel was found to be the most effective drug that blocked both tumor growth and invasion. These effects were also validated in PDX tumors in vivo. Our research opens new avenues for high-content drug screening based on patient-derived cell cultures, and for personalized chemosensitivity testing.
| S-EPMC5529420 | biostudies-literature
Project description:BackgroundThe role of adjuvant chemotherapy in stage II colon cancer continues to be debated. The presence of circulating tumor DNA (ctDNA) after surgery predicts very poor recurrence-free survival, whereas its absence predicts a low risk of recurrence. The benefit of adjuvant chemotherapy for ctDNA-positive patients is not well understood.MethodsWe conducted a trial to assess whether a ctDNA-guided approach could reduce the use of adjuvant chemotherapy without compromising recurrence risk. Patients with stage II colon cancer were randomly assigned in a 2:1 ratio to have treatment decisions guided by either ctDNA results or standard clinicopathological features. For ctDNA-guided management, a ctDNA-positive result at 4 or 7 weeks after surgery prompted oxaliplatin-based or fluoropyrimidine chemotherapy. Patients who were ctDNA-negative were not treated. The primary efficacy end point was recurrence-free survival at 2 years. A key secondary end point was adjuvant chemotherapy use.ResultsOf the 455 patients who underwent randomization, 302 were assigned to ctDNA-guided management and 153 to standard management. The median follow-up was 37 months. A lower percentage of patients in the ctDNA-guided group than in the standard-management group received adjuvant chemotherapy (15% vs. 28%; relative risk, 1.82; 95% confidence interval [CI], 1.25 to 2.65). In the evaluation of 2-year recurrence-free survival, ctDNA-guided management was noninferior to standard management (93.5% and 92.4%, respectively; absolute difference, 1.1 percentage points; 95% CI, -4.1 to 6.2 [noninferiority margin, -8.5 percentage points]). Three-year recurrence-free survival was 86.4% among ctDNA-positive patients who received adjuvant chemotherapy and 92.5% among ctDNA-negative patients who did not.ConclusionsA ctDNA-guided approach to the treatment of stage II colon cancer reduced adjuvant chemotherapy use without compromising recurrence-free survival. (Supported by the Australian National Health and Medical Research Council and others; DYNAMIC Australian New Zealand Clinical Trials Registry number, ACTRN12615000381583.).
| S-EPMC9701133 | biostudies-literature
Project description:BACKGROUND:Achaete scute-like 2 (Ascl2), a basic helix-loop-helix (bHLH) transcription factor, controls the fate of intestinal stem cells. However, the role of Ascl2 in colon cancer progenitor cells remains unknown. The cell line HT-29 (47.5-95% of CD133(+) population) and LS174T (0.45% of CD133(+) population) were chosen for functional evaluation of Ascl2 in colon cancer progenitor cells after gene knockdown by RNA interference. METHODOLOGY/PRINCIPAL FINDINGS:Immunohistochemistry demonstrated that Ascl2 was significantly increased in colorectal adenocarcinomas. Downregulation of Ascl2 using RNA interference in cultured colonic adenocarcinoma HT-29 and LS174T cells reduced cellular proliferation, colony-forming ability, invasion and migration in vitro, and resulted in the growth arrest of tumor xenografts in vivo. The Ascl2 protein level in CD133(+) HT-29 cells was significantly higher than in CD133(-) HT-29 cells. Ascl2 blockade via shRNA interference in HT-29 cells (shRNA-Ascl2/HT-29 cells) resulted in 26.2% of cells staining CD133(+) compared with 54.7% in control shRNA-Ctr/HT-29 cells. The levels of 'stemness' associated genes, such as CD133, Sox2, Oct4, Lgr5, Bmi1, and C-myc, were significantly decreased in shRNA-Ascl2/HT-29 and shRNA-Ascl2/LS174T cells in vitro as well as in the corresponding tumor xenograft (CD133 was not performed in shRNA-Ascl2/LS174T cells). The shRNA-Ascl2/HT-29 cells had inhibited abilities to form tumorspheres compared with control. The microRNA (miRNAs) microarrays, identified 26 up-regulated miRNAs and 58 down-regulated miRNAs in shRNA-Ascl2/HT-29 cells. Expression levels of let-7b, miRNA-124, miRNA-125b, miRNA-17, miRNA-20a and miRNA-302b, involved in the regulation of 'stemness', were quantified with qPCR, which confirmed their identities. Restoration of miRNA-302b, via its mimic, led to the restoration of shRNA-Ascl2/HT-29 'stemness' characteristics, including tumorsphere formation and 'stemness' associated genes levels, and the recovery of cellular behaviors, including colony-forming ability, invasion and migration in vitro. CONCLUSIONS/SIGNIFICANCE:Ascl2 may be a potential target for the inhibition of colon cancer progenitor cells, and functions through a miR-302b-related mechanism.
| S-EPMC3285660 | biostudies-literature
Project description:This paper presents an integrated quantitative MR image analysis framework to include all necessary steps such as MRI inhomogeneity correction, feature extraction, multiclass feature selection and multimodality abnormal brain tissue segmentation respectively. We first obtain mathematical algorithm to compute a novel Generalized multifractional Brownian motion (GmBm) texture feature. We then demonstrate efficacy of multiple multiresolution texture features including regular fractal dimension (FD) texture, and stochastic texture such as multifractional Brownian motion (mBm) and GmBm features for robust tumor and other abnormal tissue segmentation in brain MRI. We evaluate these texture and associated intensity features to effectively delineate multiple abnormal tissues within and around the tumor core, and stroke lesions using large scale public and private datasets.
| S-EPMC5719874 | biostudies-literature
Project description:BackgroundGlutathione S-transferase mu 1 (GSTM1) is one of the major glutathione conjugation enzymes. Its expression and activity have been suggested to correlate with the occurrence of colon cancer; however, the role of GSTM1 in tumor immunity remains unclear.MethodsRelevant data downloaded from The Cancer Genome Atlas (TCGA), Clinical Proteomic Tumor Analysis Consortium (CPTAC), and Human Protein Atlas (HPA) was used to perform a multi-dimensional expression analysis of GSTM1 in colon adenocarcinoma (COAD). The correlation between GSTM1 and tumor immunity was analyzed with multiple online tools. Then protein-protein interaction (PPI) network and functional enrichment analyses of GSTM1-associated immunomodulators were performed. Further, we developed the Cox regression model based on the GSTM1-related immunomodulators. Finally, a GSTM1-based clinical nomogram and a calibration curve was established to predict the probability and accuracy of long-term survival.ResultGSTM1 was significantly downregulated in COAD versus normal tissues. Infiltration levels of B cells, CD8+ T cells, and dendritic cells were closely correlated to GSTM1 gene copy number deletion, and GSTM1 expression levels in COAD positively correlated with dendritic cell, B cell, neutrophil, and macrophage infiltration. Functional enrichment analysis indicated 36 GSTM1-related immunomodulators are involved in immune-related pathways of regulating T cell activation and lymphocytic activation. A 2-gene prognostic risk signature based on the 36 GSTM1-related immunomodulators was built using the Cox regression model, and the risk signature in combination with stage had an area under the curve (AUC) value of 0.747 by the receiver operating characteristic method. patients with higher risk scores-calculated based on 2 gene prognostic risk characteristics and further identified as an independent prognostic factor-were associated with worse survival using the Kaplan-Meier analysis. Together, the clinical nomogram and calibration curve based on GSTM1 suggested a good prediction accuracy for long-term survival probability.ConclusionsOur study provided evidence supporting the significant role of GSTM1 in COAD immunity and suggests GSTM1 as a potential novel target for COAD immunotherapy.
| S-EPMC9830352 | biostudies-literature
Project description:Our aim was to introduce and validate qPSMA, a semiautomatic software package for whole-body tumor burden assessment in prostate cancer patients using 68Ga-prostate-specific membrane antigen (PSMA) 11 PET/CT. Methods: qPSMA reads hybrid PET/CT images in DICOM format. Its pipeline was written using Python and C++ languages. A bone mask based on CT and a normal-uptake mask including organs with physiologic 68Ga-PSMA11 uptake are automatically computed. An SUV threshold of 3 and a liver-based threshold are used to segment bone and soft-tissue lesions, respectively. Manual corrections can be applied using different tools. Multiple output parameters are computed, that is, PSMA ligand-positive tumor volume (PSMA-TV), PSMA ligand-positive total lesion (PSMA-TL), PSMA SUVmean, and PSMA SUVmax Twenty 68Ga-PSMA11 PET/CT data sets were used to validate and evaluate the performance characteristics of qPSMA. Four analyses were performed: validation of the semiautomatic algorithm for liver background activity determination, assessment of intra- and interobserver variability, validation of data from qPSMA by comparison with Syngo.via, and assessment of computational time and comparison of PSMA PET-derived parameters with serum prostate-specific antigen. Results: Automatic liver background calculation resulted in a mean relative difference of 0.74% (intraclass correlation coefficient [ICC], 0.996; 95%CI, 0.989;0.998) compared with METAVOL. Intra- and interobserver variability analyses showed high agreement (all ICCs > 0.990). Quantitative output parameters were compared for 68 lesions. Paired t testing showed no significant differences between the values obtained with the 2 software packages. The ICC estimates obtained for PSMA-TV, PSMA-TL, SUVmean, and SUVmax were 1.000 (95%CI, 1.000;1.000), 1.000 (95%CI, 1.000;1.000), 0.995 (95%CI, 0.992;0.997), and 0.999 (95%CI, 0.999;1.000), respectively. The first and second reads for intraobserver variability resulted in mean computational times of 13.63 min (range, 8.22-25.45 min) and 9.27 min (range, 8.10-12.15 min), respectively (P = 0.001). Highly significant correlations were found between serum prostate-specific antigen value and both PSMA-TV (r = 0.72, P < 0.001) and PSMA-TL (r = 0.66, P = 0.002). Conclusion: Semiautomatic analyses of whole-body tumor burden in 68Ga-PSMA11 PET/CT is feasible. qPSMA is a robust software package that can help physicians quantify tumor load in heavily metastasized prostate cancer patients.
| S-EPMC6735278 | biostudies-literature
Project description:RATIONALE AND OBJECTIVES:The objective of this study was to develop and validate a predictive magnetic resonance imaging (MRI) activity score for ileocolonic Crohn disease activity based on both subjective and semiautomatic MRI features. MATERIALS AND METHODS:An MRI activity score (the "virtual gastrointestinal tract [VIGOR]" score) was developed from 27 validated magnetic resonance enterography datasets, including subjective radiologist observation of mural T2 signal and semiautomatic measurements of bowel wall thickness, excess volume, and dynamic contrast enhancement (initial slope of increase). A second subjective score was developed based on only radiologist observations. For validation, two observers applied both scores and three existing scores to a prospective dataset of 106 patients (59 women, median age 33) with known Crohn disease, using the endoscopic Crohn's Disease Endoscopic Index of Severity (CDEIS) as a reference standard. RESULTS:The VIGOR score (17.1?×?initial slope of increase?+?0.2?×?excess volume?+?2.3?×?mural T2) and other activity scores all had comparable correlation to the CDEIS scores (observer 1: r?=?0.58 and 0.59, and observer 2: r?=?0.34-0.40 and 0.43-0.51, respectively). The VIGOR score, however, improved interobserver agreement compared to the other activity scores (intraclass correlation coefficient?=?0.81 vs 0.44-0.59). A diagnostic accuracy of 80%-81% was seen for the VIGOR score, similar to the other scores. CONCLUSIONS:The VIGOR score achieves comparable accuracy to conventional MRI activity scores, but with significantly improved reproducibility, favoring its use for disease monitoring and therapy evaluation.
| S-EPMC6760840 | biostudies-literature
Project description:The complexity of tumor histomorphology reflects underlying tumor biology impacting on natural course and response to treatment. This study presents a method of computer-aided analysis of tissue sections, relying on multifractal (MF) analyses, of cytokeratin-stained tumor sections which quantitatively evaluates of the morphological complexity of the tumor-stroma interface. This approach was applied to colon cancer collection, from an adjuvant treatment randomized study. Metrics obtained with the method acted as independent markers for natural course of the disease, and for benefit of adjuvant treatment. Comparative analyses demonstrated that MF metrics out-performed standard histomorphological features such as tumor grade, budding and configuration of invasive front. Notably, the MF analyses-derived "?max" -metric constitutes the first response-predictive biomarker in stage II-III colon cancer showing significant interactions with treatment in analyses using a randomized trial-derived study population. Based on these results the method appears as an attractive and easy-to-implement tool for biomarker identification.
| S-EPMC5095346 | biostudies-literature