Welcome to MOP-C v0.3

MOP-C predicts risk of relapse in patients with CNS lymphoma. MOP-C includes clinical and molecular features assessed at baseline, after one cycle of therapy and at the end of induction therapy (Heger JM et al., Blood, 2023).

Important notes

Please note that all variables are required for prediction. For example, if 'ctDNA detected in plasma (at baseline)' is set to 'no', ctDNA levels are set to zero and PRD is set to negative both after 1 cycle of therapy and at the end of induction therapy. These values therefore influence the risk model and MOP-C cannot be used without the required variables.

Disclaimer

MOP-C is not validated in prospective clinical trials and does therefore not provide evidence to adjust, omit or intensify treatment of patients with CNS lymphoma. MOP-C should only be used by healthcare professionals and is provided without any warranty. All information contained in this system and produced by it are provided for educational purposes only. In particular, any information found here or generated from the use of this system should not be used for the diagnosis or treatment of any health problem or disease. MOP-C is not intended to replace any clinical judgement or guide individual patient care in any way.

MOP-C input variables


Age (in years)


ECOG PS (Eastern Cooperative Oncology Group Performance Status)


Deep brain structures involved (MRI at baseline)?


Total protein in cerebrospinal fluid elevated (at baseline)?


Serum LDH elevated (at baseline)?


ctDNA detected in plasma (at baseline)?

ctDNA level baseline in log hGE/mL plasma (haploid genome equivalents per mL plasma)

PRD (peripheral residual disease) after 1 cycle of therapy detected?

Log10 change from baseline


PRD (peripheral residual disease) at the end of induction therapy detected?

Log10 change from baseline


Complete remission achieved (MRI at the end of induction therapy)?



        

        

        
        

          

            MOP-C Shiny app implementation by Teodora Bucaciuc and Roland Schwarz, (c) Schwarzlab 2023, Institute for Computational Cancer Biology (ICCB, https://iccb-cologne.org), Center for Integrated Oncology (CIO), 
         Cancer Research Center Cologne Essen (CCCE), Faculty of Medicine and University Hospital Cologne, University of Cologne, Germany. 

Code available on https://bitbucket.org/schwarzlab/mopc under MIT license. Technical contact: roland.schwarz@iccb-cologne.org.
          

          

            Predictive model and clinical interpretation by Jan-Michel Heger and Sven Borchman, Borchmann Lab, Department of Internal Medicine I, Center for Integrated Oncology (CIO), University Hospital Cologne, Germany. Contact: sven.borchmann@uk-koeln.de.
          

          

            Publication: Heger JM et al. Entirely noninvasive outcome prediction in central nervous system lymphomas using circulating tumor DNA. Blood 2023.