Colorectal cancer (CRC) is a leading cause of cancer deaths worldwide. Although polypectomy at early stage reduces CRC incidence, 90% of the polyps are small and diminutive, where removal of them poses risks to patients that may outweigh the benefits. Correctly detecting and predicting polyp type during colonoscopy allows endoscopists to resect and discard the tissue without submitting it for histology, saving time, and costs. Nevertheless, human visual observation of early stage polyps varies. Therefore, this paper aims at developing a fully automatic algorithm to detect and classify hyperplastic and adenomatous colorectal polyps. Adenomatous polyps should be removed, whereas distal diminutive hyperplastic polyps are considered clinically insignificant and may be left in situ . A novel transfer learning application is proposed utilizing features learned from big nonmedical datasets with 1.4-2.5 million images using deep convolutional neural network. The endoscopic images we collected for experiment were taken under random lighting conditions, zooming and optical magnification, including 1104 endoscopic nonpolyp images taken under both white-light and narrowband imaging (NBI) endoscopy and 826 NBI endoscopic polyp images, of which 263 images were hyperplasia and 563 were adenoma as confirmed by histology. The proposed method identified polyp images from nonpolyp images in the beginning followed by predicting the polyp histology. When compared with visual inspection by endoscopists, the results of this study show that the proposed method has similar precision (87.3% versus 86.4%) but a higher recall rate (87.6% versus 77.0%) and a higher accuracy (85.9% versus 74.3%). In conclusion, automatic algorithms can assist endoscopists in identifying polyps that are adenomatous but have been incorrectly judged as hyperplasia and, therefore, enable timely resection of these polyps at an early stage before they develop into invasive cancer.

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Purpose HER2 amplification has been implicated in resistance to therapy with anti–epidermal growth factor receptor antibodies (anti-EGFRabs) in metastatic colorectal cancer (mCRC). The purpose of the study was to validate the predictive impact of HER2 amplification in mCRC. Patients and Methods We analyzed patients with RAS/BRAF wild-type mCRC across two distinct cohorts. In cohort 1 (n = 98), HER2 amplification was tested in tumor tissue using dual in situ hybridization ( HER2 amplification: HER2/CEP17 ratio, 2.0 or greater). Cohort 2 (n = 70) included 16 patients with HER2 amplification and 54 HER2 nonamplified controls identified by next-generation sequencing ( HER2 amplification: four or more copies) who had received prior anti-EGFRabs. The primary end point was progression-free survival (PFS) on treatment with anti-EGFRab therapy, which was estimated and compared using the Kaplan-Meier method and log-rank test. Results Median PFS in cohort 1 on anti-EGFRab–based therapy was significantly shorter in patients with HER2 amplification compared with HER2 nonamplified patients (2.8 v 8.1 months, respectively; hazard ratio [HR], 7.05; 95% CI, 3.4 to 14.9; P < .001). These findings were validated in cohort 2 (median PFS for HER2 amplified v nonamplified: 2.8 v 9.3 months, respectively; HR, 10.66; 95% CI, 4.5 to 25.1; P < .001). The median PFS on therapy without anti-EGFRabs was similar among HER2-amplified and nonamplified patients in both cohort 1 (9.7 v 11.1 months, respectively; HR, 1.01; 95% CI, 0.4 to 2.4; P = .97) and cohort 2 (9.6 v 11.3 months, respectively; HR, 1.21; 95% CI, 0.5 to 3.1; P = .66). In multivariable analyses, HER2 amplification emerged as a single independent predictor of poor PFS on anti-EGFRab therapy in both cohort 1 (HR, 6.48; 95% CI, 3.1 to 13.6; P < .001) and cohort 2 (HR, 10.1; 95% CI, 4.3 to 23.9; P < .001). Conclusion HER2 amplification in RAS/RAF wild-type mCRC seems to be a predictive biomarker for lack of efficacy of anti-EGFRab therapy. Screening patients with RAS/BRAF wild-type mCRC for HER2 amplification should be considered before anti-EGFRab treatment to guide therapy and to identify patients for early referral to clinical trials.