Integrating Artificial Intelligence and PET Imaging for Drug Discovery: A Paradigm Shift in Immunotherapy.

The integration of artificial intelligence (AI) and positron emission tomography (PET) imaging has the potential to become a powerful tool in drug discovery. This review aims to provide an overview of the current state of research and highlight the potential for this alliance to advance pharmaceutical innovation by accelerating the development and deployment of novel therapeutics. We previously performed a scoping review of three databases (Embase, MEDLINE, and CENTRAL), identifying 87 studies published between 2018 and 2022 relevant to medical imaging (e.g., CT, PET, MRI), immunotherapy, artificial intelligence, and radiomics. Herein, we reexamine the previously identified studies, performing a subgroup analysis on articles specifically utilizing AI and PET imaging for drug discovery purposes in immunotherapy-treated oncology patients. Of the 87 original studies identified, 15 met our updated search criteria. In these studies, radiomics features were primarily extracted from PET/CT images in combination (n = 9, 60.0%) rather than PET imaging alone (n = 6, 40.0%), and patient cohorts were mostly recruited retrospectively and from single institutions (n = 10, 66.7%). AI models were used primarily for prognostication (n = 6, 40.0%) or for assisting in tumor phenotyping (n = 4, 26.7%). About half of the studies stress-tested their models using validation sets (n = 4, 26.7%) or both validation sets and test sets (n = 4, 26.7%), while the remaining six studies (40.0%) either performed no validation at all or used less stringent methods such as cross-validation on the training set. Overall, the integration of AI and PET imaging represents a paradigm shift in drug discovery, offering new avenues for more efficient development of therapeutics. By leveraging AI algorithms and PET imaging analysis, researchers could gain deeper insights into disease mechanisms, identify new drug targets, or optimize treatment regimens. However, further research is needed to validate these findings and address challenges such as data standardization and algorithm robustness.

Artificial intelligence in immunotherapy PET/SPECT imaging.

OBJECTIVE: Immunotherapy has dramatically altered the therapeutic landscape for oncology, but more research is needed to identify patients who are likely to achieve durable clinical benefit and those who may develop unacceptable side effects. We investigated the role of artificial intelligence in PET/SPECT-guided approaches for immunotherapy-treated patients. METHODS: We performed a scoping review of MEDLINE, CENTRAL, and Embase databases using key terms related to immunotherapy, PET/SPECT imaging, and AI/radiomics through October 12, 2022. RESULTS: Of the 217 studies identified in our literature search, 24 relevant articles were selected. The median (interquartile range) sample size of included patient cohorts was 63 (157). Primary tumors of interest were lung (n = 14/24, 58.3%), lymphoma (n = 4/24, 16.7%), or melanoma (n = 4/24, 16.7%). A total of 28 treatment regimens were employed, including anti-PD-(L)1 (n = 13/28, 46.4%) and anti-CTLA-4 (n = 4/28, 14.3%) monoclonal antibodies. Predictive models were built from imaging features using univariate radiomics (n = 7/24, 29.2%), radiomics (n = 12/24, 50.0%), or deep learning (n = 5/24, 20.8%) and were most often used to prognosticate (n = 6/24, 25.0%) or describe tumor phenotype (n = 5/24, 20.8%). Eighteen studies (75.0%) performed AI model validation. CONCLUSION: Preliminary results suggest broad potential for the application of AI-guided immunotherapy management after further validation of models on large, prospective, multicenter cohorts. CLINICAL RELEVANCE STATEMENT: This scoping review describes how artificial intelligence models are built to make predictions based on medical imaging and explores their application specifically in the PET and SPECT examination of immunotherapy-treated cancers. KEY POINTS: • Immunotherapy has drastically altered the cancer treatment landscape but is known to precipitate response patterns that are not accurately accounted for by traditional imaging methods. • There is an unmet need for better tools to not only facilitate in-treatment evaluation but also to predict, a priori, which patients are likely to achieve a good response with a certain treatment as well as those who are likely to develop side effects. • Artificial intelligence applied to PET/SPECT imaging of immunotherapy-treated patients is mainly used to make predictions about prognosis or tumor phenotype and is built from baseline, pre-treatment images. Further testing is required before a true transition to clinical application can be realized.

Artificial Intelligence and Radiomics: Clinical Applications for Patients with Advanced Melanoma Treated with Immunotherapy.

Immunotherapy has greatly improved the outcomes of patients with metastatic melanoma. However, it has also led to new patterns of response and progression, creating an unmet need for better biomarkers to identify patients likely to achieve a lasting clinical benefit or experience immune-related adverse events. In this study, we performed a focused literature survey covering the application of artificial intelligence (AI; in the form of radiomics, machine learning, and deep learning) to patients diagnosed with melanoma and treated with immunotherapy, reviewing 12 studies relevant to the topic published up to early 2022. The most commonly investigated imaging modality was CT imaging in isolation (n = 9, 75.0%), while patient cohorts were most frequently recruited retrospectively and from single institutions (n = 7, 58.3%). Most studies concerned the development of AI tools to assist in prognostication (n = 5, 41.7%) or the prediction of treatment response (n = 6, 50.0%). Validation methods were disparate, with two studies (16.7%) performing no validation and equal numbers using cross-validation (n = 3, 25%), a validation set (n = 3, 25%), or a test set (n = 3, 25%). Only one study used both validation and test sets (n = 1, 8.3%). Overall, promising results have been observed for the application of AI to immunotherapy-treated melanoma. Further improvement and eventual integration into clinical practice may be achieved through the implementation of rigorous validation using heterogeneous, prospective patient cohorts.

PET/CT and SPECT/CT Imaging of HER2-Positive Breast Cancer.

HER2 (Human Epidermal Growth Factor Receptor 2)-positive breast cancer is characterized by amplification of the HER2 gene and is associated with more aggressive tumor growth, increased risk of metastasis, and poorer prognosis when compared to other subtypes of breast cancer. HER2 expression is therefore a critical tumor feature that can be used to diagnose and treat breast cancer. Moving forward, advances in HER2 in vivo imaging, involving the use of techniques such as positron emission tomography (PET) and single-photon emission computed tomography (SPECT), may allow for a greater role for HER2 status in guiding the management of breast cancer patients. This will apply both to patients who are HER2-positive and those who have limited-to-minimal immunohistochemical HER2 expression (HER2-low), with imaging ultimately helping clinicians determine the size and location of tumors. Additionally, PET and SPECT could help evaluate effectiveness of HER2-targeted therapies, such as trastuzumab or pertuzumab for HER2-positive cancers, and specially modified antibody drug conjugates (ADC), such as trastuzumab-deruxtecan, for HER2-low variants. This review will explore the current and future role of HER2 imaging in personalizing the care of patients diagnosed with breast cancer.

Artificial intelligence and radiomics: fundamentals, applications, and challenges in immunotherapy.

Immunotherapy offers the potential for durable clinical benefit but calls into question the association between tumor size and outcome that currently forms the basis for imaging-guided treatment. Artificial intelligence (AI) and radiomics allow for discovery of novel patterns in medical images that can increase radiology's role in management of patients with cancer, although methodological issues in the literature limit its clinical application. Using keywords related to immunotherapy and radiomics, we performed a literature review of MEDLINE, CENTRAL, and Embase from database inception through February 2022. We removed all duplicates, non-English language reports, abstracts, reviews, editorials, perspectives, case reports, book chapters, and non-relevant studies. From the remaining articles, the following information was extracted: publication information, sample size, primary tumor site, imaging modality, primary and secondary study objectives, data collection strategy (retrospective vs prospective, single center vs multicenter), radiomic signature validation strategy, signature performance, and metrics for calculation of a Radiomics Quality Score (RQS). We identified 351 studies, of which 87 were unique reports relevant to our research question. The median (IQR) of cohort sizes was 101 (57-180). Primary stated goals for radiomics model development were prognostication (n=29, 33.3%), treatment response prediction (n=24, 27.6%), and characterization of tumor phenotype (n=14, 16.1%) or immune environment (n=13, 14.9%). Most studies were retrospective (n=75, 86.2%) and recruited patients from a single center (n=57, 65.5%). For studies with available information on model testing, most (n=54, 65.9%) used a validation set or better. Performance metrics were generally highest for radiomics signatures predicting treatment response or tumor phenotype, as opposed to immune environment and overall prognosis. Out of a possible maximum of 36 points, the median (IQR) of RQS was 12 (10-16). While a rapidly increasing number of promising results offer proof of concept that AI and radiomics could drive precision medicine approaches for a wide range of indications, standardizing the data collection as well as optimizing the methodological quality and rigor are necessary before these results can be translated into clinical practice.

Investigating of the role of CT scan for cancer patients during the first wave of COVID-19 pandemic.

Introduction: Amidst this current COVID-19 pandemic, we undertook this systematic review to determine the role of medical imaging, with a special emphasis on computed tomography (CT), on guiding the care and management of oncologic patients. Material and Methods: Study selection focused on articles from 01/02/2020 to 04/23/2020. After removal of irrelevant articles, all systematic or non-systematic reviews, comments, correspondence, editorials, guidelines and meta-analysis and case reports with less than 5 patients were also excluded. Full-text articles of eligible publications were reviewed to select all imaging-based publications, and the existence or not of an oncologic population was reported for each publication. Two independent reviewers collected the following information: ( 1) General publication data; (2) Study design characteristics; (3) Demographic, clinical and pathological variables with percentage of cancer patients if available; (4) Imaging performances. The sensitivity and specificity of chest CT (C-CT) were pooled separately using a random-effects model. The positive predictive value (PPV) and negative predictive value (NPV) of C-CT as a test was estimated for a wide range of disease prevalence rates. Results: A total of 106 publications were fully reviewed. Among them, 96 were identified to have extractable data for a two-by-two contingency table for CT performance. At the end, 53 studies (including 6 that used two different populations) were included in diagnosis accuracy analysis (N = 59). We identified 53 studies totaling 11,352 patients for whom the sensitivity (95CI) was 0.886 (0.880; 0.894), while specificity remained low: in 93% of cases (55/59), specificity was ≤ 0.5. Among all the 106 reviewed studies, only 7 studies included oncologic patients and were included in the final analysis for C-CT performances. The percentage of patients with cancer in these studies was 0.3% (34/11352 patients), lower than the global prevalence of cancer. Among all these studies, only 1 (0.9%, 1/106) reported performance specifically in a cohort of cancer patients, but it however only reported true positives. Discussion: There is a concerning lack of COVID-19 studies involving oncologic patients, showing there is a real need for further investigation and evaluation of the performance of the different medical imaging modalities in this specific patient population.

Proton-Coupled Electron-Transfer Reactivity Controls Iron versus Sulfur Oxidation in Nonheme Iron-Thiolate Complexes.

Reaction of the five-coordinate FeII(N4S) complexes, [FeII(iPr3TACN)(abtX)](OTf) (abt = aminobenzenethiolate, X = H, CF3), with a one-electron oxidant and an appropriate base leads to net H atom loss, generating new FeIII(iminobenzenethiolate) complexes that were characterized by single-crystal X-ray diffraction (XRD), as well as UV-vis, EPR, and Mössbauer spectroscopies. The spectroscopic data indicate that the iminobenzenethiolate complexes have S = 3/2 ground states. In the absence of a base, oxidation of the FeII(abt) complexes leads to disulfide formation instead of oxidation at the metal center. Bracketing studies with separated proton-coupled electron-transfer (PCET) reagents show that the FeII(aminobenzenethiolate) and FeIII(iminobenzenethiolate) forms are readily interconvertible by H+/e- transfer and provide a measure of the bond dissociation free energy (BDFE) for the coordinated N-H bond between 64 and 69 kcal mol-1. This work shows that coordination to the iron center causes a dramatic weakening of the N-H bond and that Fe- versus S-oxidation in a nonheme iron complex can be controlled by the protonation state of an ancillary amino donor.

Probing Physical Oxidation State by Resonant X-ray Emission Spectroscopy: Applications to Iron Model Complexes and Nitrogenase.

The ability of resonant X-ray emission spectroscopy (XES) to recover physical oxidation state information, which may often be ambiguous in conventional X-ray spectroscopy, is demonstrated. By combining Kß XES with resonant excitation in the XAS pre-edge region, resonant Kß XES (or 1s3p RXES) data are obtained, which probe the 3dn+1 final-state configuration. Comparison of the non-resonant and resonant XES for a series of high-spin ferrous and ferric complexes shows that oxidation state assignments that were previously unclear are now easily made. The present study spans iron tetrachlorides, iron sulfur clusters, and the MoFe protein of nitrogenase. While 1s3p RXES studies have previously been reported, to our knowledge, 1s3p RXES has not been previously utilized to resolve questions of metal valency in highly covalent systems. As such, the approach presented herein provides chemists with means to more rigorously and quantitatively address challenging electronic-structure questions.

Kß X-Ray Emission Spectroscopic Study of a Second-Row Transition Metal (Mo) and Its Application to Nitrogenase-Related Model Complexes.

In recent years, X-ray emission spectroscopy (XES) in the Kß (3p-1s) and valence-to-core (valence-1s) regions has been increasingly used to study metal active sites in (bio)inorganic chemistry and catalysis, providing information about the metal spin state, oxidation state and the identity of coordinated ligands. However, to date this technique has been limited almost exclusively to first-row transition metals. In this work, we present an extension of Kß XES (in both the 4p-1s and valence-to-1s [or VtC] regions) to the second transition row by performing a detailed experimental and theoretical analysis of the molybdenum emission lines. It is demonstrated in this work that Kß2 lines are dominated by spin state effects, while VtC XES of a 4d transition metal provides access to metal oxidation state and ligand identity. An extension of Mo Kß XES to nitrogenase-relevant model complexes shows that the method is sufficiently sensitive to act as a spectator probe for redox events that are localized at the Fe atoms. Mo VtC XES thus has promise for future applications to nitrogenase, as well as a range of other Mo-containing biological cofactors. Further, the clear assignment of the origins of Mo VtC XES features opens up the possibility of applying this method to a wide range of second-row transition metals, thus providing chemists with a site-specific tool for the elucidation of 4d transition metal electronic structure.

Spectroscopic X-ray and Mössbauer Characterization of M6 and M5 Iron(Molybdenum)-Carbonyl Carbide Clusters: High Carbide-Iron Covalency Enhances Local Iron Site Electron Density Despite Cluster Oxidation.

The present study employs a suite of spectroscopic techniques to evaluate the electronic and bonding characteristics of the interstitial carbide in a set of iron-carbonyl-carbide clusters, one of which is substituted with a molybdenum atom. The M6C and M5C clusters are the dianions (Et4N)2[Fe6(µ6-C)(µ2-CO)2(CO)14] (1), [K(benzo-18-crown-6)]2[Fe5(µ5-C)(µ2-CO)1(CO)13] (2), and [K(benzo-18-crown-6)]2[Fe5Mo(µ6-C)(µ2-CO)2(CO)15] (3). Because 1 and 2 have the same overall cluster charge (2-) but different numbers of iron sites (1: 6 sites → 2: 5 sites), the metal atoms of 2 are formally oxidized compared to those in 1. Despite this, Mössbauer studies indicate that the iron sites in 2 possess significantly greater electron density (lower spectroscopic oxidation state) compared with those in 1. Iron K-edge X-ray absorption and valence-to-core X-ray emission spectroscopy measurements, paired with density functional theory spectral calculations, revealed the presence of significant metal-to-metal and carbide 2p-based character in the filled valence and low-lying unfilled electronic manifolds. In all of the above experiments, the presence of the molybdenum atom in 3 (Fe5Mo) results in somewhat unremarkable spectroscopic properties that are essentially a "hybrid" of 1 (Fe6) and 2 (Fe5). The overall electronic portrait that emerges illustrates that the central inorganic carbide ligand is essential for distributing charge and maximizing electronic communication throughout the cluster. It is evident that the carbide coordination environment is quite flexible and adaptive: it can drastically modify the covalency of individual Fe-C bonds based on local structural changes and redox manipulation of the clusters. In light of these findings, our data and calculations suggest a potential role for the central carbon atom in FeMoco, which likely performs a similar function in order to maintain cluster integrity through multiple redox and ligand binding events.

Structures, Spectroscopic Properties, and Dioxygen Reactivity of 5- and 6-Coordinate Nonheme Iron(II) Complexes: A Combined Enzyme/Model Study of Thiol Dioxygenases.

The synthesis of four new FeII(N4S(thiolate)) complexes as models of the thiol dioxygenases are described. They are composed of derivatives of the neutral, tridentate ligand triazacyclononane (R3TACN; R = Me, iPr) and 2-aminobenzenethiolate (abtx; X = H, CF3), a non-native substrate for thiol dioxygenases. The coordination number of these complexes depends on the identity of the TACN derivative, giving 6-coordinate (6-coord) complexes for FeII(Me3TACN)(abtx)(OTf) (1: X = H; 2: X = CF3) and 5-coordinate (5-coord) complexes for [FeII(iPr3TACN)(abtx)](OTf) (3: X = H; 4: X = CF3). Complexes 1-4 were examined by UV-vis, 1H/19F NMR, and Mössbauer spectroscopies, and density functional theory (DFT) calculations were employed to support the data. Mössbauer spectroscopy reveals that the 6-coord 1-2 and 5-coord 3- 4 exhibit distinct spectra, and these data are compared with that for cysteine-bound CDO, helping to clarify the coordination environment of the cys-bound FeII active site. Reaction of 1 or 2 with O2 at -95 °C leads to S-oxygenation of the abt ligand, and in the case of 2, a rare di(sulfinato)-bridged complex, [Fe2III(µ-O)((2-NH2) p-CF3C6H3SO2)2](OTf)2 ( 5), was obtained. Parallel enzymatic studies on the CDO variant C93G were carried out with the abt substrate and show that reaction with O2 leads to disulfide formation, as opposed to S-oxygenation. The combined model and enzyme studies show that the thiol dioxygenases can operate via a 6-coord FeII center, in contrast to the accepted mechanism for nonheme iron dioxygenases, and that proper substrate chelation to Fe appears to be critical for S-oxygenation.