Liver Cancer Recurrence Prediction

The only potentially curative option for patients with colorectal liver metastases (CRLM) or hepatocellular carcinoma (HCC) is surgical resection. However, 80–85% of these patients are not eligible for liver surgery because of extensive intrahepatic metastatic lesions or the presence of extrahepatic disease. Neoadjuvant chemotherapy (NAC) is increasingly applied with the aim to downsize tumors in patients with initially unresectable disease to attain a resectable situation.

Accurate imaging of the liver following neoadjuvant chemotherapy is crucial for optimal selection of patients eligible for surgical resection and preparation of a surgical plan. MRI is the most appropriate imaging modality for preoperative assessment of patients with CRLM or HCC.

However, NAC may impair lesion detection and underestimate lesion size. As a result, patients whose tumors were considered resectable on preoperative imaging may turn out to have unresectable tumors during surgery. Or the underestimation may result in insufficient surgery, resulting in positive margins and re-excisions.

The incidence of recurrence after liver resection is very high. In different series between 43% and 65% of the patients had recurrences within 2 years of removal of the first tumor, and up to 85% within 5 years.  Without any form of treatment, most patients with recurrent cancer will die within one year.

Following surgical treatments,  doctors will frequently use MRI to check for residual tumors and will look at the risk that cancer will come back (recur) to decide if the patient should be offered additional treatments (called adjuvant therapy) or repeat the hepatectomy.

Project goal

The aim of this study is to design and develop a deep learning-based algorithm to predict five-year liver cancer recurrence using series of liver MRI exams. Patients have serial liver MRI exams: pre-treatment baseline MRI, at follow-up MRI exams during the course of therapy or surgery, and a final MRI after completing the therapy protocol.


  • Enthusiastic Master student in electrical engineering, biomedical engineering, computer science, or a related field
  • Interest in the intersection of machine learning and deep learning
  • Understanding of basic machine learning concepts and image analysis
  • Programming experiences in MATLAB and Python
  • A good team player with excellent communication skills
  • A creative solution-finder

Duration: 9 months (BME or ME or MWT)

Start date: a.s.a.p.

Collaboration:  Netherlands Cancer Institute (NKI)

Location: TU/e (Eindhoven) and NKI (Amsterdam)

Contact: For project details, please contact Dr. Behdad Dasht Bozorg, email:

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