Jia-Lu Sun

Chenggang Ci

Huanfeng Jiang

Pierre H Dixneuf

Min Zhang
email: minzhang@scut.edu.cn

J.-L Sun

Prof H.-F M Jiang

Zhang

Utilizing Nitroarenes and HCHO to Directly Construct Functional N-Heterocycles by Supported Cobalt/Amino Acid Relay Catalysis

Due to the generation of multiple intermediates during the nitroarene reduction, precise interception of single one to develop tandem reactions involving both C-C and C-N bond formations still remains a significant challenge. Herein, the relay catalysis of a supported bifunctional cobalt catalyst with L-proline has been successfully applied to establish a bran-new reductive annulation reaction of nitroarenes and formaldehyde, which enables direct and diverse construction of both symmetrical and unsymmetrical 1,3-diaryl imidazolines. It proceeds with operational simplicity, good substrate and functionality compatibility, and excellent step and atom-efficiency. Mechanistic studies reveal that the Co-catalyst exhibits a synergistic effect on the formation of key N-hydroxy imine, and the L-proline subsequently facilitates the key C-C bond formation. The current work opens a door to develop useful transformations with nitroarenes by reduction-interrupted strategy.

Introduction

Utilizing cheap and abundant feedstocks to step-and atomeconomically produce functional products is always a dream of scientific community and industry. As a class of vital and bulk chemical raw materials, nitroarenes has been extensively employed to produce various arylamines by catalytic hydrogenation. [1,2] In recent years, the construction of functional molecules via reductive functionalization of nitroarenes has attracted considerable attention. Advantageously, these transformations do not need pre-preparation of active anilines sensitive to many agents, thus avoiding tedious protection manipulations and improving the substrate and functionality compatibility. In this regard, except for the reductive Nformylation [3] and N-alkylation [4,5] of nitroarenes with carbonyl reagents (Figure 1a-1 and 1a-2), the construction of several types of N-heterocycles via reductive cyclization has also been nicely demonstrated, recently (Figure 1a-3). [6,7] In terms of these transformations, they all apply the in situ formed arylamines arising from full hydrogenation of nitroarenes to form new C-N bonds. In contrast, due to the generation of multiple intermediates during the nitroarene reduction (Figure 1a, nitrosoarenes, Nhydroxylamines, azoxybenzenes, azobenzenes, etc.), [8] precise in situ interception of single hydroxyamine to develop tandem reactions involving both C-C and C-N bond formations still remains a significant challenge (Figure 1a-4), whereas such a concept offers a promising new direction to construct functional molecules that are inaccessible or difficult to prepare with the conventional approaches.

Accepted Manuscript

Angewandte Chemie International Edition N-aryl imidazolines constitute a class of highly important Nheterocycles, and they have been found to exhibit diverse interesting biological and therapeutic activities. [9,10] Moreover, they are useful precursors [11] for the preparation of N-heterocyclic carbene (NHC) catalysts, [12][13][14] coordination ligands to transition metals, [15][16][17][18] and functional materials. [19][20][21] In general, N-aryl imidazolines are synthesized by the condensation of N 1 ,N 2diarylethane-1,2-diamines (DAEDAs) with formaldehyde or its surrogates (Figure 1b). [22,23] However, such a synthesis often encounters the following key issues: (i) Restricted synthetic diversity due to the poor availability of requisite DAEDAs. As the DAEDAs are generally prepared by reductive amination of arylamines with oxalaldehyde (Figure 1b-1), [24] nucleophilic substitution of 1,2-dihaloethanes with arylamines (Figure 1b-2), or Buchwald-Hartwig amination of aryl halides with ethane-1,2diamine (Figure 1b-3), [25] these protocols suffer from unavoidable generation of multiple by-products (e.g., 1,4-diphenylpiperazines, N-mono-, tri-, tetra-aryl substituted ethane-1,2-diamines) and easy occurrence of substrate decarbonylation or halogen elimination. (ii) Difficult catalyst reusability. In this context, the development of innovative strategies enabling direct and diverse construction of N-aryl imidazolines from cheap and abundant feedstocks, preferably with reusable base metal catalysts, would be highly desirable.

Given the above issues, catalytic reductive annulation of nitroarenes and formaldehyde via sequential C-N and C-C bond formations would provide a highly practical way to access 1,3diaryl imidazolines (Figure 1c), as both reactants are abundant and inexpensive, and water is generated as the by-product. However, the combination of nitroarenes with formaldehyde is seldom employed to construct N-heterocycles on account of the tendency to undergo more favorable reductive N-methylation reactions. [4,5] Enlightened by the capability of atomically dispersed cobalt catalysts in nitro reduction, [26][27][28] and the N-dopants in adsorbing HCHO and tuning the catalytic performance, [29][30][31][32] we believe that the design of a suitable cobalt catalyst would offer a solution to precisely intercept one of multiple intermediates during the nitroarene reduction and realize the above synthetic purpose. Based on this idea, we wish herein to report a N-doped carbonsupported bifunctional cobalt catalyst (Co-Nx/NC-900), featuring atomically dispersed Co-species and abundant electron-rich nitrogen sites, and describe, for the first time, the relay catalysis of such a reusable catalyst with L-proline is successfully applied to directly and diversely construct 1,3-diaryl imidazolines from nitroarenes and formaldehyde with renewable formic acid as the reductant. [33,34] This reaction proceeds with operational simplicity, good substrate and functionality compatibility, high step and atom efficiency, which offers a practical platform to access NHC precursors (Figure 1c).

Results and Discussion

At first, we prepared the catalyst (Co-Nx/NC-900) by pyrolysis of the bimetal-organic framework with a molar ratio of Co : Zn = 1: 20 (Co1Zn20-BMOF) under flowing argon at 900 °C for 3 h, and the detailed procedure was described in the Supporting Information (SI, Scheme S1). In the X-ray diffraction (XRD) pattern of catalyst (Figure S2a), no obvious Co signal was observed. The Co 2p X-ray photoelectron spectroscopy (XPS) spectrum only showed characteristic peaks of CoNx at 780.38 eV and 782.33 eV (Figure S4c). Both of which indicate that no Co nanoparticles are formed in Co-Nx/NC-900. The aberrationcorrected high-angle annular dark-field scanning transmission electron microscopy (AC HAADF-STEM) images confirmed that a small amount of Co atomic clusters and abundant cobalt single atoms (Co-SAs) arising from the pyrolysis of bimetallic MOF were highly dispersed in the catalyst (Figures 2a-b). The X-ray absorption fine structure (XAFS) measurements were applied to further validate the coordination environment of the Co-species. Two distinct peaks at ca. 1.47 Å and 2.30 Å in the FT k 3 -weighted extended XAFS (EXAFS) spectrum (Figure 2c) contribute similarly to the CoPc and Co foil, respectively, indicating there are two completely different coordination environment of Co-species in Co-Nx/NC-900. Meanwhile, the coordination number of Co-N is ca. 4.90 and Co-Co is ca. 1.37 with mean bond lengths of 1.97 Å and 2.53 Å, respectively (Figure 2d and Table S1). The wavelet transform (WT) was further conducted to analyze the Co (Figure 2e) K-edge EXAFS oscillations. The WT maximum at 3.8 Å -1 is assigned to the Co-N bonds, and the secondary signal at 7.4 Å -1 arises from Co-Co bonds. Moreover, the N 1s XPS results show the existence of abundant electron-rich pyridinic and pyrrolic nitrogen species, which is caused by the sublimation of zinc during the pyrolysis (Figure S5c and Table S2). Based on the above results, the abundant Co-SAs (CoN4) and a small number of atomic clusters are uniformly dispersed in the N-doped carbon support. Given this structure, we believe that a synergistic effect between atomically dispersed Co-species and N-dopants would exhibit promising catalytic performance for our synthetic purpose.

With the availability of the Co-Nx/NC-900 material, we then employed it to test the reductive annulation reaction of nitroarene A1 and formaldehyde (Table S3, see Figure S13 for model reaction). Gratifyingly, an optimal isolated yield of 1,3-diphenyl 10.1002/anie.202303007

Accepted Manuscript

Angewandte Chemie International Edition imidazoline B1 (81%) was obtained when the reaction in 2 mL of pure tetrahydrofuran was conducted at 100 °C for 20 h by using 20 mg Co-Nx/NC-900 (2.82 wt%), 30 mol% of L-proline, and 5 equivalents of HCOOH (Table S3, entry 1).

After establishing the optimal reaction conditions, we then examined the substrate scope of the newly developed synthetic protocol. As illustrated in Figure 3, mono or di-substituted nitroarenes were initially employed to couple with formaldehyde. All the reactions proceeded smoothly and furnished the desired 1,3-diaryl imidazolines in moderate to excellent yields upon isolation (B1-B22). A vast range of functional groups (-Me, -Et, -OMe, -F, -Cl, -Br, -I, -CN, -CHO, ketyl, and ester) were well tolerated. Surprisingly, unsaturated cyano and carbonyl groups did not undergo reduction, and no hydrodehalogenation of the halogens was observed (B4-B10, B15-B16, and B18-B20). Importantly, the retention of these functionalities offers the potential for the elaboration of complex molecules via further chemical transformations. In comparison, nitroarenes with an electro-withdrawing group (eg., -CN, -CF3, and ester) afforded much higher product yields (B4, B6, B8, B11, and B14-B17) than those containing a strong electron-donating alkoxy group (B13, B19 and B22). Upon GC-MS analysis, this phenomenon is attributed to partial electron-rich nitroarenes undergoing reductive N-methylation to form the N-methyl and N,N-dimethylaniline byproducts. Nitro-substituted N-heteroarenes also underwent efficient reductive annulation, delivering the desired N-heteroaryl imidazolines in moderate to good isolated yields (B23-B31). Encouraged by the successful synthesis of symmetrical 1,3diarylimidazoles, we subsequently turned our attention to the transformation employing two different nitroarenes to synthesize unsymmetrical 1,3-diaryl imidazolines (Figure 4). In principle, the reaction generates one cross-coupling and two homocoupling products (B, B', and B''), and each one has the maximum yield of 33%. Gratifyingly, by utilizing the different electronic properties of the substituents, we were able to obtain the desired crosscoupling products with the isolated yields higher than 30% in most cases (B32-B35, B37, B39, and B41-B44). The results of Figures 34show that the developed chemistry offers a general approach to produce both symmetrical and unsymmetrical 1,3-diaryl imidazolines. To check the practicality of the synthetic method, the catalyst (Co-Nx/NC-900) recycling at full and half conversions of the model reaction was firstly examined (Figure S10). It was recycled to run the model reaction for 6 consecutive times. The activity and selectivity were very well retained, and no significant decrease of product yields was observed, which shows that the catalyst exhibits excellent cyclic durability. Meanwhile, the XRD, XPS and HAADF-STEM analysis of the reused catalyst unambiguously indicated that the catalyst structure has been maintained well after recycling (Figure S12).

As a class of promising compounds, thiourea derivatives are frequently utilized as synthetically useful building blocks and antiinflammatory agents. [35,36] We performed the model reaction by scaling up nitroarene A1 from 0.25 mmol to 2.5 mmol to produce B1 in a yield of 62%, and then prepared cyclic thiourea B45 in 78% yield by treating B1 with molecular sulfur S8 under solventfree conditions at 150 °C (Figure 5a). [37] Further, the gram-scale syntheses of compounds B6 and B8 were also achieved by scaling up the nitroarenes to 10 mmol (Figure 5b). Considering that imidazolinium salts are the precursors of NHCs extensively applied as organo-catalysts and coordination ligands to transition metals, we here were able to efficiently transform various imidazolines into the imidazolinium salts via an oxidation process with NBS, NCS, or DDQ (Figure 5c, C1-C13), [38] which 10.1002/anie.202303007

Accepted Manuscript

Angewandte Chemie International Edition demonstrates the potentials of the developed chemistry to be further applied in the field of organo and metal complex catalysis. It is important to note that, reasonable introduction of nitrogen atoms on the aromatic ring realized effective preparation of tridentate pyridine-NHC-pyridine pincer ligand precursors (C10-C13). Meanwhile, the synthetic protocol was successfully applied for the derivation of biomedically active molecules such as Ibuprofen derivatives B46 and Adapalene derivatives B47 (Figure 5d). Subsequently, we conducted the mechanistic investigations by means of control experiments and DFT calculations. As shown in Figure 6a-1, the interruption of the model reaction after 20 min generated trace amount of product B1 along with N-hydroxy imine int-3, imine int-5 and N-oxide compound int-6 (detected by HR-MS, Figure S16a-c). Noteworthy, these compounds were consumed up after completion of the reaction, indicating they serve as the reaction intermediates. Under the standard conditions, imine int-5 can react with m-chloronitrobenzene to afford the homo and cross-coupling products B5 and B37 (Figure 6a-2 and Figure S17), and the reaction employing nitrosobenzene int-1 or hydroxylamine int-2 as the reactant produced product B1 in 64% or 70% (Figure 6a-3 and Figure 6a-4). However, the reaction aniline and formaldehyde totally failed to B1 (Figure 6a-5). results reveal that aniline, int-1, and int-2 are also involved in the product formation, and they are the precursors of intermediates int-3 and int-5. According to the matching principle of electronic property, int-3 and int-5 are the precursors for the formation of intermediate int-6. Further, replacing formaldehyde with glyoxal failed to produce B1 (Figure 6a-6), implying that glyoxal as a reaction intermediate can be ruled out.

To figure out the role of key parameters on the formation of product B1, we conducted the comparison and formaldehydeadsorption experiments (Figure 6b, Table S4 and Figure S14). Table S4 reveals that HCOOH as a reductant and Co-Nx/NC-900 as the catalyst are equally essential for the generation of B1 (entries 1-4). Switching the reaction catalyst to Co(OAc)2 or Co1Zn20-BMOF precursor led to no product generation (Table S4, entries 5-6). The absence of L-proline significantly lowered the product yield to 37% (Figure 6b, entry 2), and N-methylaniline and N-dimethylaniline became the major products, indicating that Lproline plays a key role on promoting the C-C bond formation to afford requisite intermediate int-6. Then, the poisoning experiment by introducing excess KSCN was conducted under the standard conditions, and no product was observed at all (Figure 6b, entry 3), which indicate that the highly dispersed CoN4 species are the catalytic active sites. Meanwhile, the use of acidetched Co-Nx/NC-900 gave a similar product yield (Figure 6b, entry 4), showing that the cobalt clusters do not affect the product formation. Further, the UV-Vis spectra reveal the excellent HCHO-adsorption capability of the catalyst (Figure S14), assigning to the contribution of large specific surface area (Figure S3a) and electron-rich nitrogen dopants (Figure S5c and Table S2), [29,30] and the enriched formaldehyde ensures efficient formation of int-3 and int-5 to the greatest extent by timely capture the hydroxyamine int-2.

Further investigations were focused on the new C-C bond formation between int-3 and int-5. We conducted the crossover reactions of N-hydroxyaniline and 4-cyano aniline (Figure 6c), two precursors allowing in situ formation of N-hydroxy imine int-3 and imine with formaldehyde. The results show that the presence of HCOOH is indispensable to acquire the cross-coupling product B48, and the addition of L-proline significantly increased the product yield. Meanwhile, the Co-Nx/NC-900 has no effect on the product formation, indicating that the Co-catalyst is not involved in the C-C bond formation (entries 1-5). Notably, the presence of Co-Nx/NC-900 mainly resulted in the homocoupling product B1 even the loading of 4-cyano aniline is two times of Nhydroxyaniline (Figure 6c, entries 6-8), indicating that the Cocatalyst in combination with HCOOH results in the reduction of Nhydroxyaniline to aniline and the more electron-rich aniline is favorable to condense with HCHO to afford requisite imine for the subsequent coupling with N-hydroxy imine int-3 than 4-cyano aniline. Noteworthy, as shown in entries 4-5, no product B1 formation indicates that the C-C bond formation from two Nhydroxy imines (int-3) is less likely. Further, pH-comparison experiments show that the acidity (grey polyline) of the reaction system plays a crucial role on the C-C bond formation (Figure S15), and there is no coordination between the cobalt catalyst and L-proline (Figures S18-20 To reveal the role of L-proline on the formation of key C-C bond, DFT [START_REF] Parr | Density-Functional Theory of Atoms and Molecules[END_REF] calculations were performed to compare the presumed pathways for the generation of int-6 from N-hydroxy imine int-3 and imine int-5 at the B3LYP-D3(BJ)/6-311G** level, implemented in the Gaussian 16 package. [START_REF] Frisch | [END_REF] Initiated with imine int-5, the potential energy surfaces (PESs) for the formation of int-6 are shown in Figure 6d. In the absence of L-proline, the C-C bond-formation between int-5 and int-3 proceeds via a sixmembered-ring transition state (TS1) with a barrier of 21.1 kcal/mol, and the reaction is exothermic by 2.6 kcal/mol. In contrast, the same PESs are analyzed under the assistance of Lproline (Figure 6d). Starting with the formation of complex int-5int-3-L-proline by intermolecular hydrogen bonding interactions, which is exothermic by 4.1 kcal/mol, and the process for TS2 is kinetically more favorable (ΔG ≠ = 9.9 kcal/mol) as compared to the formation of TS1 (ΔG ≠ = 21.1 kcal/mol). Herein, the C-C bond formation accompanies with the proton transfer from the -N=OH + unit to N-atom of L-proline, producing intermediate int-6'-Lproline. Then, int-6'-L-proline undergoes two consecutive proton transfer to form hydrogen-bonding complex int-6-Lproline, and this part of the PESs has low barrier of the entire path (9.9 kcal/mol for TS3, 1.6 kcal/mol for TS3'), corresponding to intermolecular proton transfer from the -COOH group of Lproline to the imino N-atom (int-6'-L-proline → int-6''-L-proline, ΔG = -4.6 kcal/mol) and intramolecular proton transfer from the Natom to the O-atom of L-proline (int-6''-L-proline → int-6-Lproline, ΔG = -1.3 kcal/mol), respectively. Further dissociation of proline from int-6-L-proline to int-6 undergoes a barrier of 10.9 kcal/mol.

Based on the above findings, a plausible reaction pathway for the formation of product B1 is depicted in Figure 7. Initially, the Co-Nx sites on the catalyst surface bind active H-atoms from the decomposition of HCOOH, [7,28,[33][34]

Conclusion

In summary, by developing a nitrogen-doped graphitic carbonsupported bifunctional cobalt catalyst with atomically dispersed Co-sites, the relay catalysis of such a reusable catalyst with Lproline has been successfully applied to establish a new reductive annulation reaction of nitroarenes and formaldehyde, which enables direct and diverse construction of both symmetrical and unsymmetrical 1,3-diaryl imidazolines with the features of 10.1002/anie.202303007

Accepted Manuscript

operational simplicity, high step-and atom-efficiency, and good substrate and functionality compatibility. Upon a simple oxidation, the obtained products can be transformed into the N-heterocyclic carbene precursors widely applied in organometallic chemistry and catalysis. Mechanistic studies reveal that the Co-catalyst plays a crucial role on the formation of key N-hydroxy imine by a synergistic effect of the nitroarene reduction and the enrichment of HCHO to timely trap the in situ formed hydroxyamines, and the L-proline subsequently facilitates the C-C bond formation to afford requisite N 1 ,N 2 -diarylethane-1,2-diamines for the generation of target imidazolines. The developed chemistry opens a door to further develop valuable transformations with nitroarenes by reduction-interrupted strategy.

Figure 1 .

 1 Figure 1. a) The advances and the challenge on reductive functionalization of nitroarenes. b) Conventional approaches for access to 1,3-diarylimidazolidines. c) Using nitroarenes and formaldehyde to construct 1,3-diaryl imidazolidines by supported CoNx/N with amino acid relay catalysis and the application to prepare N-heterocyclic carbene precursors (This work).

  10.1002/anie.202303007

Figure 2 .

 2 Figure 2. a-b) AC HAADF-STEM images of Co-Nx/NC-900. c) FT-EXAFS spectra in the R-space of Co3O4, CoO, CoPc, Co foil and Co-Nx/NC-900. d) Corresponding Co K-edge EXAFS fitting curve of Co-Nx/NC-900 in R space. e) Wavelet-transformed k 3 -weighted EXAFS spectra of Co3O4, CoO, CoPc, Co foil and Co-Nx/NC-900.

Figure 3 .

 3 Figure 3. Synthesis of symmetrical 1,3-diaryl imidazolidines from nitroarenes and formaldehyde.[a]Reaction conditions: nitroarene A (0.25 mmol), HCHO (2 eq.), catalyst (20 mg, 3.83 mol%), L-proline (30 mol%), HCOOH (5 eq.), and THF (2 mL), 100 °C , N2, 20 h.

Figure 4 .

 4 Figure 4. Synthesis of unsymmetrical 1,3-diaryl imidazolidines from two different nitroarenes and formaldehyde.[a]Reaction conditions: nitroarenes A' (0.125 mmol), A'' (0.125 mmol), HCHO (4 eq.), catalyst (20 mg, 7.66 mol%), Lproline (60 mol%), HCOOH (10 eq.), and THF (2 mL), 100 °C , N2, 20 h.

Figure 5 .

 5 Figure 5. Synthetic applications. The detailed reaction conditions are indicated in "synthetic utility" in Supporting Information.

  ).

Figure 6 .

 6 Figure 6. a) Control experiments. b) Verification experiments. [a] Reaction conditions: A1 (0.25 mmol), HCHO (2 eq.), catalyst (20 mg), L-proline (30 mol %), HCOOH (5 eq.), and THF (2 mL), 100 °C , 20 h. [b] Determined by gas chromatography (GC) using n-hexadecane as an internal standard. [c] Co-Nx/NC-900 was etched as far as possible with 0.5 M H2SO4 aqueous solution at 80 °C for 12 h. c) Crossover reactions of N-hydroxyaniline and 4-cyano aniline. d) DFT calculations. (The detailed reaction conditions are indicated in "experimental procedure" in Supporting Information)

  which results in catalytic transfer hydrogenation (CTH) of nitroarene A1 to generate nitrosobenzene int-1 and hydroxylamine int-2. Then, the catalystenriched formaldehyde timely traps int-2 to afford N-hydroxy imine int-3 in situ. Noteworthy, being different from the conventional nitrone int-4 possessing a nucleophilic oxygen-site, int-3 formed in acidic conditions undergoes carbo-nucleophilic addition to imine int-5 resulting from CTH of int-3 under the catalysis of L-proline, affording intermediate int-6 through a new C-C bond formation. Finally, the CTH of int-6 followed by dehydrative cyclization of DAEDA int-7 with formaldehyde gives rise to imidazoline B1.

Figure 7 .

 7 Figure 7. Plausible reaction pathway.

Acknowledgements

The authors are grateful to the financial support of State Key Laboratory of Pulp and Paper Engineering (2022C03), the National Natural Science Foundation of China (21971071, 22163007), the Natural Science Foundation of Guangdong Province (2023A1515012318 and 2021A1515010155), and Guizhou Province Science Foundation ([2020]1Y050).

Keywords: Nitroarenes and formaldehyde • Reductive coupling • Imidazolines • N-heterocyclic carbene ligands • Relay catalysis • Precise interception

RESEARCH ARTICLE

Utilizing Nitroarenes and HCHO to Directly Construct Functional

N-Heterocycles by Supported Cobalt/Amino Acid Relay Catalysis

Jia-Lu Sun + , Chenggang Ci + , Huanfeng Jiang, Pierre. H. Dixneuf, and Min Zhang*

The relay catalysis of a supported bifunctional cobalt catalyst and L-proline has been applied to establish a new reductive annulation reaction of nitroarenes and formaldehyde, which enables direct and diverse construction of 1,3-diaryl imidazolines. Mechanistic studies reveal that the Co-catalyst exhibits a synergistic effect on the formation of requisite N-hydroxy imines, and the L-proline subsequently facilitates the key C-C bond formation.

Accepted Manuscript

Angewandte Chemie International Edition


Riccardo Campi

Campi Riccardo

Zine-Eddine Eddine Khene
email: zineddine.khene@gmail.com

Alexander Kutikov

Machine learning in renal cell carcinoma research: the promise and pitfalls of 'renal-izing' the potential of artificial intelligence

Keywords: Renal cell carcinoma, Kidney cancer, Artificial intelligence, Machine learning

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Artificial intelligence and Machine learning (ML) are increasingly applied for study of patients with renal cell carcinoma (RCC) [START_REF] Lee | Kidney cancer management 3.0: can artificial intelligence make us better?[END_REF]. Advanced techniques, such as neural networks or random forests can analyze vast amount of clinical data to uncover specific prognostic features that may not be detectable with traditional statistical methods. However, despite some promising results, recent studies showcase that benefits of ML are not ubiquitous, especially when deployed on imperfect and nongranular datasets [START_REF] Khene | Application of Machine Learning Models to Predict Recurrence After Surgical Resection of Nonmetastatic Renal Cell Carcinoma[END_REF].

In this issue of BJUI, Boulenger de Hauteclocque employ data from the French multiinstitutional kidney cancer database UroCCR (ClinicalTrials.gov Identifier: NCT03293563), to investigate the ability of seven ML algorithms at predicting pT3a upstaging in a cohort of patients who underwent surgery for cT1/cT2a RCC. Using supervised ML algorithms, they reported a prediction accuracy (measured by the area under the receiver operating curve) of 0.77 for their best model [START_REF] Boulenger De Hauteclocque | Machine learning approach for prediction of pT3a upstaging and outcomes of localized RCC (UroCCR-15)[END_REF]. While the results are intriguing, the design of the study raises questions that should be considered by clinicians when interpreting the results.

First, the accuracy of the model is heavily dependent on the fidelity and granularity of the dataset used for training. For instance, missing data have a significant noxious impact on the performance of the model. For their study, Boulenger et al. worked with a database with a high rate (205) of missing observations in the variables used to construct the prognostic model, such as the R.E.N.A.L. nephrometry score. Thus, limitations of the presented models stemmed, at least in part, from the nature and handicap of the dataset. Indeed, deep neural network approach is particularly effective when deployed for analyzing large, complex, and high-dimensional datasets, such as cross-sectional imaging, that are highly granular and are of high fidelity [START_REF] Heller | Nephrometry Scores Yield Comparable Predictive Results to Those of Human-Expert Scores in Predicting Oncologic and Perioperative Outcomes[END_REF].

Second, ML algorithms have several adjustable hyperparameters. Hyperparameters can be compared to knobs of an amplifier that allow to fine-tune the bass and treble of an audio track. They are usually set before the training process begins. A search for optimal hyperparameters must be done using the training set to achieve a high performance (there are usually two strategies for hyperparameter tuning: GridSearchCV or RandomizedSearchCV). A detailed description of the hyperparameters tuning process is lacking and would strengthen this report [START_REF] Liu | How to Read Articles That Use Machine Learning: Users' Guides to the Medical Literature[END_REF]. Similarly, decision curve analysis is also missing. While the authors do report receiver operating characteristic curves and calibration measurements of the models, details of decision curve analysis are important since they provide a probability of certainty for the decisions that can be made in daily practice based on the results of the algorithm [START_REF] Vickers | Decision Curve Analysis: A Novel Method for Evaluating Prediction Models[END_REF].

Third, ML approaches have a "shadow zone", referred to as a "black box", which results in challenges of understanding the model estimates. Boulenger de Hauteclocque and colleagues acknowledge this limitation and use the shapley additive explanations (SHAP) values to explain each patient's probability of being upstaged. SHAP is a popular method used to understand how ML models make predictions, but it has limitations. One issue is that SHAP assumes the included variables are independent from one another, while in reality, there may be interdependencies among the variables. In that case, the approximations made by SHAP can be skewed (for instance tumor size and RENAL score, both of which are used in the ML model, are intimately linked) [START_REF] Fryer | Shapley Values for Feature Selection: The Good, the Bad, and the Axioms[END_REF].

The Standardized Reporting of Machine Learning Applications in Urology (STREAM-URO) framework was developed to provide a set of recommendations to standardize the way ML studies are reported [START_REF] Kwong | Standardized Reporting of Machine Learning Applications in Urology: The STREAM-URO Framework[END_REF]. It includes providing a detailed report of the model, along with the code, especially in case of complex ML systems. The authors of the study largely adhered to the STREAM-URO guidelines. However, the manuscript omits the models themselves, which prohibits the readers from utilizing them to predict outcomes on new populations and to validate the model with external data.

Finally, from a clinical and surgical standpoint, the ability to predict pT3a upstaging among patients with localized renal masses who are candidates for elective surgery is not always actionable. On one hand, decision-making in these patients is complex and nuanced; as such, beyond tumor-related characteristics, the treatment decisions should also rely on careful assessment of patient-, kidney-and provider-related factors [START_REF] Chandrasekar | Collaborative Review: Factors Influencing Treatment Decisions for Patients with a Localized Solid Renal Mass[END_REF]. On the other hand, PN and RN appear to achieve similar cancer control even if the final histology shows unexpected pT3a RCC upstaging [START_REF] Capitanio | Does the Unexpected Presence of Non-organ-confined Disease at Final Pathology Undermine Cancer Control in Patients with Clinical T1N0M0 Renal Cell Carcinoma Who Underwent Partial Nephrectomy[END_REF]. In addition, in a recent systematic review, age, tumor size and RENAL score were the three predictors of upstaging [START_REF] Veccia | Upstaging to pT3a in Patients Undergoing Partial or Radical Nephrectomy for cT1 Renal Tumors: A Systematic Review and Meta-analysis of Outcomes and Predictive Factors[END_REF]. In this regard, the added value of ML methods as compared to conventional logistic regression models for prediction of pathological features of RCC is unclear. Indeed, several reports indicate that logistic regression is at least as good as ML techniques in predict such outcomes [START_REF]A systematic review shows no performance benefit of machine learning over logistic regression for clinical prediction models[END_REF]. Of note, an AUC of 0.77 (as reported in the study by Boulenger de Hauteclocque et al) is close to halfway between a random coin flip (AUC of 0.5) and a perfect prediction (AUC of 1.0), being comparable to previously reported "standard" modes [START_REF] Kutikov | Anatomic features of enhancing renal masses predict malignant and high-grade pathology: a preoperative nomogram using the RENAL Nephrometry score[END_REF].

In conclusion, the report by the authors is novel and noteworthy; however, the available evidence does not entirely support the claim that prediction models obtained using ML algorithms on standard clinical datasets are superior and more clinically useful when compared to conventional predictive tools. While ML techniques harbor new opportunities in the field of RCC prognostication, it is imperative to standardize and improve the quality of reporting of these novel AI algorithms, as well as to thoroughly validate them before integration into shared decision-making in daily clinical practice.

grant from any funding agency in the public, commercial, or not-for-profit sectors.