Pancreatic cancer diagnosis: radiologists meet AI (PANORAMA25)
Creators: name: Alves, Natália
name: Schuurmans, Megan
name: Rutkowski, Dawid
name: Löhr, Matthias
name: Yakar, Derya
name: Haldorsen, Ingfried
name: Liedenbaum, Marjolein
name: Molven, Anders
name: Vendittelli, Pierpaolo
name: Litjens, Geert
name: Hermans, John
name: Huisman, Henkjan
Publisher: name: Zenodo
Year: 2024
Subjects: Pancreatic cancer
artificial intelligence
computed tomography
radiologists
computer-aided detection and diagnosis
opportunistic screening
MICCAI 2025 challenge

description: * Natália Alves and Megan Schuurmans contributed equally.

Pancreatic ductal adenocarcinoma (PDAC) is the deadliest cancer worldwide (median survival=4.8 months) and is estimated to become the second leading cause of cancer-related deaths in Western countries by 2030 (He, 2024). Due to the lack of early, disease-specific symptoms, 80–85% of patients are diagnosed in advanced disease stages(Park, 2021). However, early-stage patients have a significantly more favourable prognosis (median survival= 26 months), making early detection the current most effective approach to improving outcome (Schwartz, 2021). Contrast-enhanced computed tomography (CECT) is considered the first line of diagnosis for PDAC (Grossberg, 2020). Recent research has shown that cancer signs (such as pancreatic duct cutoff/dilatation and pancreatic atrophy) can be retrospectively seen on pre-diagnostic CECT scans (3–36 months before clinical diagnosis) in 16%-84% of patients (Singh, 2020; Toshima, 2021). These secondary imaging signs and early subtle focal lesions are often overlooked in clinical practice, where most abdominal CECT scans are acquired for non-pancreatic-specific indications, leading to delayed diagnosis and reduced survival.

Implementing image-based screening programs for PDAC detection is still cost-prohibitive in the absence of validated tumor biomarkers or disease-specific risk factors. CECT scans are the most common type of imaging examination to diagnose and evaluate abdominal diseases, accounting for 40%-52% of all CT scans performed worldwide (Prokop, 2022; Schöckel, 2020; Gonzales, 2015). With the ongoing global increase in CECT image acquisition in routine clinical practice, incidental or opportunistic screening for PDAC is the most promising avenue to boost early detection and improve patient outcomes. However, this would impose a significant burdenon radiologists interpreting CECT, making implementation impractical given the global shortage of professionals in this field.

Artificial intelligence (AI) is starting to achieve expert performances in cancer diagnosis across many domains (Esteva, 2017; McKinney, 2020), and an increasing number of publications study AI for PDAC diagnosis on CECT (Cao, 2023; Korfiati, 2023; Chen, 2023; Park, 2023). AI has the potential to analyse a high volume of routine CECTscans acquired in clinical practice, looking for PDAC lesions as well as early secondary signs and alerting clinicians to any suspicious findings. However, currently, research does not focus on early detection/incidental screening, considers private data sets and algorithms, and largely lacks clinical comparison to radiologists.

In recent years significant attention has been drawn to AI-powered opportunistic screening, specifically regarding osteoporosis and body composition analysis (Pickhardt, 2022). Still, AI for incidental cancer screening on medical images remains underexplored due to several unresolved technical challenges. First, there is an absence ofhigh-quality, publicly accessible data sets considering a wide enough inclusion criteria for opportunistic screening AI development. Second, opportunistic screening software needs to be exceptionally able to adapt to different vendors and centres, and generalizable across various scan acquisition parameters (such as reconstructionkernels, section collimation, and thickness) and other technical variations. Third, AI for opportunistic cancer screening requires a high sensitivity to be clinically useful, shifting the focus to the true positive rate maximization at the lowest possible false positive rate, in contrast with standard diagnostic cancer AI built to minimize falsepositives at the highest possible true positive rate. Novel AI algorithms and/or training strategies are then required to achieve high precision at false positive rates as low as 1 in 10,000 or 1 in 100,000 patients. Finally, given the variance of input seen by an AI in this application, it is paramount that models incorporate techniques that allow them to communicate the uncertainty associated with their predictions to the user.

The PANORAMA25 challenge is a new prospectively designed, multi-centre challenge to establish the state-of-the-art AI performance at PDAC detection on CECT at a low false positive rate for opportunistic screening. PANORAMA25 will be the first Grand Challenge addressing opportunistic cancer screening on medical imaging.

PANORAMA25 also introduces a new community-based approach for AI scientific development. Specifically, we have established a scientific advisory board consisting of 14 experts in pancreas AI, pathology, radiology, and surgery, and a patient representative to unify and standardize present-day guidelines. Key aspects of the PANORAMA25 challenge were established in conjunction with this scientific advisory board, to ensure meaningful development and validation of AI towards clinical translation.