The techniques of proteomic analysis combined with tissue microarray provide us a dramatic tool for screening of LNM-associated proteins in cancer research. The increased expression of HSP-27, GST, and Annexin II, but decreased expression of L-FABP, suggests a significantly elevated incidence of LNM in CRC.

The lungs are the second most common site of metastasis for colorectal cancer (CRC) after the liver. Rectal cancer is associated with a higher incidence of lung metastases compared to colon cancer. In China, the proportion of rectal cancer cases is around 50%, much higher than that in Western countries (nearly 30%). However, there is no available consensus or guideline focusing on CRC with lung metastases. We conducted an extensive discussion and reached a consensus of management for lung metastases in CRC based on current research reports and the experts’ clinical experiences and knowledge. This consensus provided detailed approaches of diagnosis and differential diagnosis and provided general guidelines for multidisciplinary therapy (MDT) of lung metastases. We also focused on recommendations of MDT management of synchronous lung metastases and initial metachronous lung metastases. This consensus might improve clinical practice of CRC with lung metastases in China and will encourage oncologists to conduct more clinical trials to obtain high-level evidences about managing lung metastases. Electronic supplementary material The online version of this article (10.1186/s13045-019-0702-0) contains supplementary material, which is available to authorized users.

Background: Conventional methods for predicting treatment response to neoadjuvant chemoradiotherapy (nCRT) in patients with locally advanced rectal cancer (LARC) are limited. Methods: This study retrospectively recruited 134 LARC patients who underwent standard nCRT followed by total mesorectal excision surgery in our institution. Based on pre-operative axial T2-weighted images, machine learning radiomics was performed. A receiver operating characteristic (ROC) curve was performed to test the efficiencies of the predictive model. Results: Among the 134 patients, 32 (23.9%) achieved pathological complete response (pCR), 69 (51.5%) achieved a good response, and 91 (67.9%) achieved down-staging. For prediction of pCR, good-response, and down-staging, the predictive model demonstrated high classification efficiencies, with an AUC value of 0.91 (95% CI: 0.83–0.98), 0.90 (95% CI: 0.83–0.97), and 0.93 (95% CI: 0.87–0.98), respectively. Conclusion: Our machine learning radiomics model showed promise for predicting response to nCRT in patients with LARC. Our predictive model based on the commonly used T2-weighted images on pelvic Magnetic Resonance Imaging (MRI) scans has the potential to be adapted in clinical practice. Novelty and Impact Statements: Methods for predicting the response of the locally advanced rectal cancer (LARC, T3-4, or N+) to neoadjuvant chemoradiotherapy (nCRT) is lacking. In the present study, we developed a new machine learning radiomics method based on T2-weighted images. As a non-invasive tool, this method facilitates prediction performance effectively. It achieves a satisfactory overall diagnostic accuracy for predicting of pCR, good response, and down-staging show an AUC of 0.908, 0.902, and 0.930 in LARC patients, respectively.