Deep convolutional neural network for differentiating between sarcoidosis and lymphoma based on [18F]FDG maximum-intensity projection images

Enhancing Lymphoma Diagnosis, Treatment, and Follow-Up Using 18F-FDG PET/CT Imaging: Contribution of Artificial Intelligence and Radiomics Analysis

Lymphoma, encompassing a wide spectrum of immune system malignancies, presents significant complexities in its early detection, management, and prognosis assessment since it can mimic post-infectious/inflammatory diseases. The heterogeneous nature of lymphoma makes it challenging to definitively pinpoint valuable biomarkers for predicting tumor biology and selecting the most effective treatment strategies. Although molecular imaging modalities, such as positron emission tomography/computed tomography (PET/CT), specifically 18F-FDG PET/CT, hold significant importance in the diagnosis of lymphoma, prognostication, and assessment of treatment response, they still face significant challenges. Over the past few years, radiomics and artificial intelligence (AI) have surfaced as valuable tools for detecting subtle features within medical images that may not be easily discerned by visual assessment. The rapid expansion of AI and its application in medicine/radiomics is opening up new opportunities in the nuclear medicine field. Radiomics and AI capabilities seem to hold promise across various clinical scenarios related to lymphoma. Nevertheless, the need for more extensive prospective trials is evident to substantiate their reliability and standardize their applications. This review aims to provide a comprehensive perspective on the current literature regarding the application of AI and radiomics applied/extracted on/from 18F-FDG PET/CT in the management of lymphoma patients.

Sarcoidosis and Cancer: The Role of the Granulomatous Reaction as a Double-Edged Sword

Background/Objectives: The relationship between sarcoidosis and the occurrence of neoplasia deserves to be investigated, but this relation has been observed in different and heterogeneous populations, leading to conflicting data. To clarify the causal relationship between these two diseases, different risk factors (e.g., smoking), concurrent comorbidities, corticosteroid therapy, and metastasis development-as an expression of cancer aggressiveness-were investigated. Methods: In a retrospective study on 287 sarcoidosis outpatients at the Pneumological Department of the Gemelli Foundation (Rome, Italy) between 2000 and 2024, the diagnosis of cancer was recorded in 36 subjects (12.5%). Results: The reciprocal timeline of the diseases showed three different scenarios: (1) cancer preceding sarcoidosis or sarcoid-like reactions (63.8%); (2) cancer arising after sarcoidosis diagnosis (8.3%); and (3) sarcoidosis accompanying the onset of malignancy (27.8%). Only two subjects with sarcoidosis and cancer showed metastasis, and one of them was affected by lymphoma. Conclusions: These data suggest that granulomatous inflammation due to sarcoidosis may assume an ambivalent role as a "double-edged sword", according to the M1/M2 macrophage polarization model: it represents a protective shield, preventing the formation of metastasis through the induction of immune surveillance against cancer while, on the other hand, it can be a risk factor for carcinogenesis due to the persistence of a chronic active inflammatory status. Low-dose steroid treatment was administered in only 31.6% of the cancer-sarcoidosis subjects for less than six months to control inflammation activity, with no promotive effect on carcinogenesis observed.

Artificial intelligence performance in detecting lymphoma from medical imaging: a systematic review and meta-analysis

BACKGROUND

Accurate diagnosis and early treatment are essential in the fight against lymphatic cancer. The application of artificial intelligence (AI) in the field of medical imaging shows great potential, but the diagnostic accuracy of lymphoma is unclear. This study was done to systematically review and meta-analyse researches concerning the diagnostic performance of AI in detecting lymphoma using medical imaging for the first time.

METHODS

Searches were conducted in Medline, Embase, IEEE and Cochrane up to December 2023. Data extraction and assessment of the included study quality were independently conducted by two investigators. Studies that reported the diagnostic performance of an AI model/s for the early detection of lymphoma using medical imaging were included in the systemic review. We extracted the binary diagnostic accuracy data to obtain the outcomes of interest: sensitivity (SE), specificity (SP), and Area Under the Curve (AUC). The study was registered with the PROSPERO, CRD42022383386.

RESULTS

Thirty studies were included in the systematic review, sixteen of which were meta-analyzed with a pooled sensitivity of 87% (95%CI 83-91%), specificity of 94% (92-96%), and AUC of 97% (95-98%). Satisfactory diagnostic performance was observed in subgroup analyses based on algorithms types (machine learning versus deep learning, and whether transfer learning was applied), sample size (≤ 200 or >  200), clinicians versus AI models and geographical distribution of institutions (Asia versus non-Asia).

CONCLUSIONS

Even if possible overestimation and further studies with a better standards for application of AI algorithms in lymphoma detection are needed, we suggest the AI may be useful in lymphoma diagnosis.