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Diagnostic algorithm for the imaging evaluation of a solid pancreatic mass: is it malignant?

Juan Carlos Spina¹, Mariano Lorea², Manuel Ignacio Rossi²

¹Department of Radiology, Hospital Italiano de Buenos Aires, Buenos Aires, Argentina. juan.spina@hospitalitaliano.org.ar.

Abdominal Radiology (New York)

|July 9, 2026

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Radiologists can now better characterize pancreatic masses using a new six-step algorithm. This approach aids in distinguishing malignant tumors from benign conditions, improving patient diagnosis and treatment.

Area of Science:

Radiology
Gastroenterology
Oncology

Background:

Pancreatic masses encompass a wide range of conditions, from benign to malignant, with significant prognostic and therapeutic differences.
Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal malignancy.
Inflammatory pancreatic conditions can mimic malignancy on imaging, potentially leading to misdiagnosis and unnecessary surgeries.

Purpose of the Study:

To present a structured, six-step diagnostic algorithm for evaluating pancreatic masses.
To guide radiologists in lesion characterization and risk stratification.
To assist in determining the likelihood of malignancy in pancreatic lesions.

Main Methods:

The review proposes a systematic, six-step diagnostic algorithm for pancreatic mass evaluation.

Keywords:

Autoimmune pancreatitis Decision-tree algorithm Differential diagnosis Mass-forming chronic pancreatitis Pancreatic ductal adenocarcinoma Pancreatic mass

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The algorithm is structured around the core question: 'Is it malignant?'.

Each step involves assessing explicit imaging and clinical criteria with cited accuracy data.

Main Results:

The algorithm provides a formal confidence level (high, moderate, or indeterminate) at each decision point.
It aims to improve the accuracy of pancreatic mass characterization.
The structured approach facilitates better guidance for further diagnostic steps.

Conclusions:

A systematic algorithmic approach can enhance the diagnostic accuracy of pancreatic mass evaluation.
This framework helps differentiate malignant from benign or inflammatory pancreatic lesions.
Improved characterization leads to appropriate patient management and reduces unnecessary interventions.