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Radiomics in Pancreatic Cancer: Present and Future

  • Shuai Ren1,#,
  • Lina Song1,#,
  • Marcus J. Daniels2,
  • Ying Tian1,*  and
  • Zhongqiu Wang1,* 
Cancer Screening and Prevention   2024;3(2):130-132

doi: 10.14218/CSP.2024.00010

Received:

Revised:

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 Author information

Citation: Ren S, Song L, Daniels MJ, Tian Y, Wang Z. Radiomics in Pancreatic Cancer: Present and Future. Cancer Screen Prev. 2024;3(2):130-132. doi: 10.14218/CSP.2024.00010.

Dear editor,

Pancreatic cancer (PC) is the fourth most common cause of cancer-related mortality, with a meager 5-year survival rate of under 20%.1 Patients with PC often exhibit nonspecific symptoms such as abdominal pain and weight loss, leading to delayed diagnosis. However, even when promptly diagnosed after symptom onset, the majority of PC patients are found to have advanced-stage disease. Despite notable progress in surgical methods, chemotherapy, and radiation therapy, the 5-year survival rate remains dishearteningly low at 8.2%.2

While the exact causes of PC remain poorly understood, several risk factors have been identified. These include a family history of PC, obesity, chronic pancreatitis (CP), smoking, the presence of preneoplastic lesions, or certain hereditary syndromes associated with a high risk of developing PC.3

Cross-sectional imaging is a vital tool in initially assessing symptomatic individuals suspected of having PC. It is also crucial in screening asymptomatic individuals at a heightened risk of developing PC.4 Computed tomography (CT) is the predominant imaging diagnostic method, often complemented by endoscopic ultrasound with fine needle biopsy or aspiration for pinpointing small lesions and confirming diagnoses definitively.5 Additionally, magnetic resonance imaging (MRI) and positron emission tomography (PET) play significant roles in systematically staging the disease and determining whether the primary tumor is resectable, borderline resectable, or unresectable.5 Radiomics, a subset of medical imaging, involves extracting quantitative data from various medical images like CT scans, MRI scans, or PET scans. This data is then meticulously analyzed to glean valuable insights into tumor heterogeneity.6 Quantitative imaging facilitates integrating radiomics and dynamic imaging features, independently or together, enabling the construction of clinical prediction models. These models, based on radiomics signatures or imaging phenotypes, estimate clinical outcomes associated with tumor biology.7 Radiomics represents a promising non-invasive tool for various applications in PC, including early diagnosis, evaluating treatment response, predicting prognosis, and precise diagnosis.

One of the significant challenges physicians often face in managing PC is the early detection of high-risk individuals and the timely diagnosis of patients exhibiting suspected symptoms. Our recent exploration into radiomics unveiled a promising avenue for distinguishing early-stage from late-stage PCs.6 Our findings highlighted the remarkable performance of the radiomics model, with an impressive accuracy of 97.7%, alongside notable sensitivity of 97.6%, specificity of 97.8%, positive predictive value of 98.4%, and negative predictive value of 96.8%. Moreover, our rigorous validation process, employing a 10-fold LGOCV (leave-group-out cross-validation) method, demonstrated the model’s robustness and reproducibility. On average, the area under the curve stood at 0.75 across the 10 newly developed models, further enhancing the credibility of our radiomics approach.

In clinical practice, several neoplastic and inflammatory conditions can mimic PC, such as paraduodenal “groove” pancreatitis, autoimmune pancreatitis, focal acute and CP, neuroendocrine tumors, solid pseudopapillary neoplasms, metastases, and lymphoma. Differentiating these conditions from PC can be challenging due to overlapping CT and MRI features.8 Accurate diagnosis plays a crucial role in guiding therapeutic strategies and potential outcomes in PC, while also preventing unnecessary biopsy or surgical interventions for conditions that mimic it. Previous studies have illustrated the capacity of radiomics to effectively differentiate PC from its mimics, including autoimmune pancreatitis, CP, and neuroendocrine tumors, among others, demonstrating promising performance.9–11

Conventional methods encounter challenges in identifying changes following chemotherapy and/or radiotherapy treatments, prompting the investigation of radiomic features for improved detection. Changes in radiomic features over time in longitudinal images, referred to as delta radiomics, have the potential to serve as a biomarker for predicting treatment response.12 Nasief et al. developed a delta-radiomic process based on machine learning (ML). This process involves acquiring and registering longitudinal images, segmenting and populating regions of interest, extracting radiomic features, calculating their changes - delta-radiomic features (DRFs), reducing feature space, identifying candidate DRFs with treatment-induced changes, and finally, creating outcome prediction models using ML. Their results indicated that 13 DRFs successfully passed the tests, showing significant changes after two to four weeks of treatment. The most effective combination for distinguishing good responders from bad ones (cross-validated area under the curve = 0.94) comprised normalized entropy to standard deviation difference, kurtosis, and coarseness. These findings suggest that the radiomics approach could be valuable for evaluating treatment response.13,14 However, certain studies need validation of their findings.15,16 This necessity arises due to various factors influencing clinical outcomes, such as pre-and post-CRT effects, potentially insufficient sample size, and heterogeneous population characteristics. More substantial evidence is warranted.

Despite the numerous treatment options available, the prognosis for patients with PC remains poor. However, the prediction of tumor phenotype, treatment response, and patient prognosis is becoming increasingly feasible through the use of comprehensive and integrated radiomics models.2 A recent study demonstrated that prognostic radiomics models, incorporating MRI features and clinical data, are effective in predicting progression-free survival, overall survival, and objective response rate in PC patients with hepatic metastasis undergoing chemoimmunotherapy.17 These models hold promise for evaluating patient prognosis.

Despite significant advancements, challenges persist in applying radiomics to PC.18,19 Firstly, the effectiveness of any radiomics model depends on the quality of the training data. The predictive performance of automated tools is hindered by the absence of optimal thresholds needed to balance sensitivity and specificity during data acquisition and curation. Secondly, the heterogeneity of patient data, influenced by factors such as age, sex, race, and demographics, requires future algorithms and ML technologies to accommodate such variations. Validating the robustness of radiomics tools using both prospective and retrospective real-life populations is essential for their successful integration into clinical practice. Thirdly, integrating multi-omics data represents an essential advancement in enhancing the clinical adoption of radiomics.20 This approach involves a multifaceted workflow, employing various software and expertise. Substantial technological investments are imperative to develop integrated, user-friendly tools for broad implementation in clinical settings. Additionally, segmentation, a pivotal but time-intensive process, is prone to variability among observers.20 Automating or semi-automating segmentation, especially through deep learning techniques, is crucial to streamline this critical stage.

In conclusion, radiomics, as an emerging quantitative technique, is rapidly gaining momentum in the management of PC, with its methodology continually evolving. The primary obstacles hindering the application of radiomics in cancer diseases include the limited availability of high-quality data and a lack of biological mechanistic explanations. Bridging this gap could be achieved through data sharing and collaborations among institutions, focusing on tasks such as data cleaning and labeling.

Declarations

Acknowledgement

None.

Funding

This study was funded by the National Natural Science Foundation of China (82202135, 82371919, 82372017, 82171925), Jiangsu Provincial Key Research and Development Program (BE2023789), China Postdoctoral Science Foundation (2023M741808), Young Elite Scientists Sponsorship Program by Jiangsu Association for Science and Technology (JSTJ-2023-WJ027), Foundation of Excellent Young Doctor of Jiangsu Province Hospital of Chinese Medicine (2023QB0112), Nanjing Postdoctoral Science Foundation, Natural Science Foundation of Nanjing University of Chinese Medicine (XZR2023036, XZR2021003, XZR2021050), and Medical Imaging Artificial Intelligence Special Research Fund Project, Nanjing Medical Association Radiology Branch.

Conflict of interest

The authors have no conflict of interests related to this publication.

Authors’ contributions

Study concept and design (SR), funding acquisition (SR, YT, ZQW), drafting of the manuscript (SR, LNS, MJD), critical revision of the manuscript for important intellectual content (MJD, YT, ZQW), and study supervision (YT, ZQW). All authors have made significant contributions to this study and have approved the final manuscript.

References

  1. Ren S, Song L, Tian Y, Zhu L, Guo K, Zhang H, et al. Emodin-Conjugated PEGylation of Fe3O4 Nanoparticles for FI/MRI Dual-Modal Imaging and Therapy in Pancreatic Cancer. Int J Nanomedicine 2021;16:7463-7478 View Article PubMed/NCBI
  2. Marti-Bonmati L, Cerdá-Alberich L, Pérez-Girbés A, Díaz Beveridge R, Montalvá Orón E, Pérez Rojas J, et al. Pancreatic cancer, radiomics and artificial intelligence. Br J Radiol 2022;95(1137):20220072 View Article PubMed/NCBI
  3. de la Pinta C. Radiomics in pancreatic cancer for oncologist: Present and future. Hepatobiliary Pancreat Dis Int 2022;21(4):356-361 View Article PubMed/NCBI
  4. Chu LC, Fishman EK. Pancreatic ductal adenocarcinoma staging: A narrative review of radiologic techniques and advances. Int J Surg 2023 View Article PubMed/NCBI
  5. Casà C, Piras A, D’Aviero A, Preziosi F, Mariani S, Cusumano D, et al. The impact of radiomics in diagnosis and staging of pancreatic cancer. Ther Adv Gastrointest Endosc 2022;15:26317745221081596 View Article PubMed/NCBI
  6. Ren S, Qian LC, Cao YY, Daniels MJ, Song LN, Tian Y, et al. Computed tomography-based radiomics diagnostic approach for differential diagnosis between early- and late-stage pancreatic ductal adenocarcinoma. World J Gastrointest Oncol 2024;16(4):1256-1267 View Article PubMed/NCBI
  7. Faur AC, Lazar DC, Ghenciu LA. Artificial intelligence as a noninvasive tool for pancreatic cancer prediction and diagnosis. World J Gastroenterol 2023;29(12):1811-1823 View Article PubMed/NCBI
  8. Miller FH, Lopes Vendrami C, Hammond NA, Mittal PK, Nikolaidis P, Jawahar A. Pancreatic Cancer and Its Mimics. Radiographics 2023;43(11):e230054 View Article PubMed/NCBI
  9. Ren S, Zhang J, Chen J, Cui W, Zhao R, Qiu W, et al. Evaluation of Texture Analysis for the Differential Diagnosis of Mass-Forming Pancreatitis From Pancreatic Ductal Adenocarcinoma on Contrast-Enhanced CT Images. Front Oncol 2019;9:1171 View Article PubMed/NCBI
  10. Li J, Liu F, Fang X, Cao K, Meng Y, Zhang H, et al. CT Radiomics Features in Differentiation of Focal-Type Autoimmune Pancreatitis from Pancreatic Ductal Adenocarcinoma: A Propensity Score Analysis. Acad Radiol 2022;29(3):358-366 View Article PubMed/NCBI
  11. Shiraishi M, Igarashi T, Hiroaki F, Oe R, Ohki K, Ojiri H. Radiomics based on diffusion-weighted imaging for differentiation between focal-type autoimmune pancreatitis and pancreatic carcinoma. Br J Radiol 2022;95(1140):20210456 View Article PubMed/NCBI
  12. Nasief H, Zheng C, Schott D, Hall W, Tsai S, Erickson B, et al. A machine learning based delta-radiomics process for early prediction of treatment response of pancreatic cancer. NPJ Precis Oncol 2019;3:25 View Article PubMed/NCBI
  13. Khasawneh H, Ferreira Dalla Pria HR, Miranda J, Nevin R, Chhabra S, Hamdan D, et al. CT Imaging Assessment of Pancreatic Adenocarcinoma Resectability after Neoadjuvant Therapy: Current Status and Perspective on the Use of Radiomics. J Clin Med 2023;12(21):6821 View Article PubMed/NCBI
  14. Li J, Du J, Li Y, Meng M, Hang J, Shi H. A nomogram based on CT texture features to predict the response of patients with advanced pancreatic cancer treated with chemotherapy. BMC Gastroenterol 2023;23(1):274 View Article PubMed/NCBI
  15. Chen X, Oshima K, Schott D, Wu H, Hall W, Song Y, et al. Assessment of treatment response during chemoradiation therapy for pancreatic cancer based on quantitative radiomic analysis of daily CTs: An exploratory study. PLoS One 2017;12(6):e0178961 View Article PubMed/NCBI
  16. Ciaravino V, Cardobi N, DE Robertis R, Capelli P, Melisi D, Simionato F, et al. CT Texture Analysis of Ductal Adenocarcinoma Downstaged After Chemotherapy. Anticancer Res 2018;38(8):4889-4895 View Article PubMed/NCBI
  17. Lu W, Wu G, Miao X, Ma J, Wang Y, Xu H, et al. The radiomics nomogram predicts the prognosis of pancreatic cancer patients with hepatic metastasis after chemoimmunotherapy. Cancer Immunol Immunother 2024;73(5):87 View Article PubMed/NCBI
  18. Kolla L, Parikh RB. Uses and limitations of artificial intelligence for oncology. Cancer 2024 View Article PubMed/NCBI
  19. Berbís MÁ, Godino FP, Rodríguez-Comas J, Nava E, García-Figueiras R, Baleato-González S, et al. Radiomics in CT and MR imaging of the liver and pancreas: tools with potential for clinical application. Abdom Radiol (NY) 2024;49(1):322-340 View Article PubMed/NCBI
  20. Miranda J, Horvat N, Fonseca GM, Araujo-Filho JAB, Fernandes MC, Charbel C, et al. Current status and future perspectives of radiomics in hepatocellular carcinoma. World J Gastroenterol 2023;29(1):43-60 View Article PubMed/NCBI