# Cancer surgery 2. 0: guidance by real-time molecular technologies

Nina Ogrinc, Philippe Saudemont, Zoltan Takats, Michel Salzet, Isabelle Fournier

► To cite this version:

Nina Ogrinc, Philippe Saudemont, Zoltan Takats, Michel Salzet, Isabelle Fournier. Cancer surgery 2. 0: guidance by real-time molecular technologies. Trends in Molecular Medicine, 2021, Trends in Molecular Medicine, 27 (6), pp.602-615. 10.1016/j.molmed.2021.04.001. hal-05016823

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# 1 Cancer Surgery 2.0: Guidance by real time Molecular Technologies

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3 Nina Ogrinc¹, Philippe Saudemont¹, Zoltan Takats¹, Michel Salzet¹,²*, Isabelle Fournier¹,²*

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5 *Corresponding Author

6 1 Univ. Lille, Inserm, CHU Lille, U1192 - Protéomique Réponse Inflammatoire Spectrométrie de

7 Masse - PRISM, F-59000 Lille, France

8 2 Institut Universitaire de France (IUF), Paris, France

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13 ABSTRACT

14 *In vivo* cancer margin delineation during surgery remains a grand challenge. Despite the
15 availability of several image guidance techniques and intraoperative assessment, clear surgical
16 margins and debulking efficiency remain scarce. For this reason, there is particular interest in
17 developing rapid intraoperative tools with high sensitivity and specificity to help guide cancer
18 surgery *in vivo*. Recently, several emerging technologies including intraoperative mass
19 spectrometry have paved the way for molecular guidance in a clinical setting. We evaluate these
20 techniques and assess their relevance for intraoperative surgical guidance and how they can
21 transform the future of molecular cancer surgery, diagnostics, patient management and care.
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23 **Keywords:** *in vivo* real time analysis, cancer, diagnostics, excision margins, artificial intelligence
24 intraoperative mass spectrometry
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Importance of clear margin delineation and debulking efficiency

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For solid tumors, surgery still remains in nearly 80% diagnosed cases the first frontline treatment [1]. Many studies have shown that clear surgical tumor margins and debulking via surgery has drastic impact on patient survival rates. However, the surgeons are faced with difficult decision-making with regards to margin delineation and visualization of the actual extension of the tumor. Tumors are normally removed with surrounding healthy tissue “margins”, wide enough to remove the majority of cancer cells but minimized to prevent the functional loss of the diseased organ. In practice, the margin can vary from 2 mm up to 10 cm depending on the type of cancer. Pre-neoplastic lesions and isolated groups of cancer cells are difficult to detect and are generally missed during the surgery [2]. Depending on the site and stage of the primary tumor, forgotten residual cancer cells can lead to immediate re-excision surgery, or to metastasis and bad prognosis [3]. Indeed, after breast conserving surgery (BCS) it is estimated that around 25% of all patients need re-excision [4], while in colorectal cancer 17% of the patients experience recurrence after curative surgical resection with clear microscopic margins [5]. In general, it is estimated that the prevalence of positive margins in gastric cancer is up to 20% [6].

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It is thus critical to detect with high accuracy, the correct localization of the tumor and remaining cells. To that aim, surgeons use the information provided by conventional pre-operative and intraoperative guidance technologies e.g. **Magnetic Resonance Imaging (MRI)** (see **Glossary**), **Computed Tomography (CT)**, or **Ultrasound (US)** [7]. Yet, these technologies do not provide necessary information for complete tumor resection and, therefore, the surgeons still primarily rely on tactile information. However, this is not sufficient to address the problem of discriminating benign and malign, finding the locoregional extension of tumors and managing accurate margin delineation of tumors. Most often this is achieved by intraoperative pathological examination (either gross assessment or histopathological evaluation from frozen sections). Specimen sampling for intraoperative analysis is not trivial and done by the surgeon, which unfortunately already introduces a bias in the process and increases the duration of the surgery.

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Therefore, there is an unmet need for intraoperative techniques operating in real time and *in vivo* with sufficient spatial resolution (<1 mm), minimal daily practice interference, and offering operator-friendly instrumentation. To go beyond the anatomy, incorporating real time molecular information to intraoperative assessment would optimize surgical resection and cancer diagnosis
(see Clinician's Corner). In this review we focus on emerging technologies, particularly
intraoperative mass spectrometry (MS), which will help guide surgical resection *in vivo* and in
real time based on molecular profiles and pave the way of the new gold standards in cancer
surgery; Cancer surgery 2.0.

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## The conventional cancer surgery workflow; analysis and pitfalls

61 *Techniques for image guidance*

Several conventional methodologies are used pre-operatively and intraoperatively for evaluating
resection margins and extension of the tumor. A schematic of the tumor resection process and
analysis is shown in **Figure 1**.

In order to reduce the risk of re-excisions and to ensure the successful removal of cancerous
tissues specific technologies for image guidance can be used in the operating room. These are
normally 3D pre-operative MRI or CT scans projected onto a separate screen [8–11]. These
techniques offer surgeons visualization and detection of tumors in real time and improve the
accuracy of tumor resection without disrupting the normal surgical workflow. However, the
acquired images do not always correlate with eye observations due to tumor growth,
deformations of soft tissues, shifting of organs, or misalignment of the image display compared
to the surgical field. This has partially been resolved by intraoperative MRI (iMRI) and CT
systems, especially in neurosurgery [11–13]. However, these systems require a complex
infrastructure, specialized surgical suites and instruments due to the magnetic fields. Image
guidance techniques are often complemented with routine fluoroscopy and intraoperative US for
surgical navigation [14]. US was easily implemented as it provides real time information and
straightforward image interpretation with high sensitivity and specificity [15,16]. US is,
nevertheless, an iterative contact-based method, which is less useful for identifying tumor
boundaries or microscopic tumors during open surgeries [17].

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81 *Intraoperative histology*
Although intraoperative histology assessment is not always routine practice, it remains the most frequently performed examination either by intraoperative frozen section (IFS) analysis or touch imprint cytology [18]. In an ideal clinical scenario, an accurate diagnosis should be rendered within 5-10 min. However, in most cases it requires 20-40 min depending on sampling procedures and care unit organization. The complete histopathological workup includes embedding in optimal cutting temperature medium, freezing and cutting into sections for best preservation of tissue morphology [18]. Direct freezing of the sample in liquid nitrogen [19] is also possible to reduce the time, but shown to reduce sensitivity by 65-78% during BCS [20]. Hematoxylin and Eosin (H&E) staining of tissues preserves important region-to-region morphology and allows microscopic evaluation of neighboring cell morphology, structure and organization to derive a diagnosis. Despite using simplified **algorithmic decision trees** inter-observer variation persists among experts [21,22]. In order to improve the accuracy and reduce discordance amongst specialists **artificial intelligence (AI)** algorithms based on deep neural networks (DNNs) have been recently developed and become a valuable tool for the generation and implementation of complex multi-parametric decision algorithms for pathological analysis [23] (Box 1).

## Ambient ionization mass spectrometry (AIMS): A newcomer in cancer diagnostics

MS is a label free, robust and highly sensitive technology used for biomolecular analysis. In MS the molecules are separated based on their molecular weight. Hence, in one single experiment thousands of biomolecules can be detected and identified. It has been widely implemented in the medical field for direct microbe identification (“biotyping”), newborn screening (NBS), and quantification of blood circulating drugs and metabolites.

The advent of AIMS (Box 2) progressively gave more flexibility to the field and broadened its applications. The number of AIMS technologies approved by the US Food and Drug Administration (FDA) has increased over the last years because of their ease of use, with minimal or no sample preparation. Many of these techniques show promise in clinical application for *in vitro* and *ex vivo* analysis including intraoperative analysis, however they cannot be used for *in vivo* MS-guided surgery. For this purpose, there has been an emergence of real time AIMS
probes, designed for rapid, sensitive, precise and accurate measurements *in vivo*. Each technique
promotes the analyte ions in a distinct way either by extraction of charged solvent droplets,
ultrasonic tissue ablation, laser ablation, laser excitation, laser cutting and accumulation of
smoke. To comply with the clinical settings, all of the probing systems are constituted of a
handheld device coupled to a transfer line for ion transfer [24–28] (Figure 2a-e). These probes
provide a wealth of chemical information from direct tissue analysis and give tissue specific
molecular profiles. The generated molecular barcodes can then be used to assess healthy vs.
cancer tissue, intra-tumor heterogeneity, tumor typing and grading as well as pinpointing
metastatic regions i.e. In direct tissue analysis with intraoperative probes, the most favorable is
the detection of small molecules such as metabolites and lipids. In general, abnormal lipid
metabolism is a common feature of cancer cells and can already be observed at early stages of
tumor development [29]. Several fatty acids and lipid species have been shown to be
discriminatory in breast [30,31], gastric [32], ovarian cancer [27,28,33,34], brain tumors [35–37]
and sarcomas [38]. The probes can be classified depending on the penetration depth, the type of
source or by usage either as a surgical cutting or diagnostic screening tool.

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Surgical tool-based techniques

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The first real time AIMS probes were introduced based on surgical tools serving as the AIMS
source.
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Stereotactic MS (Figure 2a), developed in 2011, introduced intraoperative DESI-MS as a way to
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validate in vivo molecular characterization in a near-real time intraoperative definition of brain
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tumor boundaries in surgery [37]. Using a combination of surgical forceps and Cavitron
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Ultrasonic Surgical Aspirator (CUSA) the specimen is dissected via ultrasonic tissue ablation.
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Each sampling location is co-registered with preoperative MRI imaging using the GE InstaTrak
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surgical navigation to correlate specific m/z values to the 3D imaging MRI system. The
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integrated approach was tested on a human glioma patient [37] and stratification of several
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glioma surgical cases [39]. The same principle was used to assess glioma subtype, grade, and
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tumor cell concentration [40], breast cancer margin assessment [41] and meningioma molecular
typing [42]. Although the concept is interesting, combining surgical tools with ex vivo mass
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spectrometry analysis still suffers from the same limitations and issues as intraoperative histology
with regards to the specimen sampling and response time.

The Intelligent Knife (iKnife) (Figure 2b) is a concept developed in 2009 for MS-guided surgery combining dissection with analysis [43]. Aerosols are generated by common surgical devices (electrocauterization), and subsequently turned into gas phase ions for MS analysis. They are then transmitted to the MS through plastic tubing. The instrument is equipped with a modified atmospheric interfaces featuring a heated capillary inlet and an orthogonal Venturi-pump aerosol transfer device [25]. The ions are created through the rapid evaporative ionization mass spectrometry (REIMS) interface via a droplet- collision on a heated surface [24,44]. The system has been tested in vivo, and allowed for confident identification of normal, borderline and malignant tissues derived from ovary cancer [34], breast cancer [30,31], cervical cancer patients [45] as well as endoscopic classification of intestinal wall, cancer and polyps [32]. The compatibility with common surgical devices is a key advantage of this technique but on the other hand low spatial resolution 0.5-2 mm, destruction of samples during analysis and in consequence, impossibility for direct cross-validation with histology are the major drawbacks [46].

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## Laser-based techniques

Lasers are powerful rays of monochromatic and coherent photons. Used in a variety of domains including in medicine, the CO₂ lasers in particular can be used during surgery for precise, fast, bleeding free cutting or vaporizing of surgical specimen with minimal postoperative pain and edema [47]. Their use has been exemplified in the field of neurosurgery [48], general cancer surgery, skin surgery and to this day eye surgery [47]. The first attempt to use laser probes for real time MS analysis of *ex vivo* biological tissues was reported in 2011 [49]. The study included divers lasers (CO₂, Nd:YAG, and nitrogen laser) linked to REIMS interface, previously described [43]. The systems were tested on food-grade porcine organs, *in vivo* canine carcinoma and *ex vivo* human colon carcinoma samples [49].

Developed in 2014, the SpiderMass (Figure 2c) is composed of a micro-sampling probe, transfer line and a MS analyzer [27]. An infrared laser with a pulse width of 5-7 ns is tuned to 2.94 µm to excite the most intense vibrational band (O-H) of water molecules, in a so-called WALDI
169 (Water-Assisted Laser Ionization/Desorption) process. The system exhibits low invasiveness: 170 about 0.1-0.3 mm³ is sampled in one experiment and as a result samples are reversibly 171 dehydrated. It is coupled to a laser fiber ending in a handpiece for contact-free screening of any 172 surface *ex vivo* or *in vivo* [26,27,38]. The most typical spectra of direct analysis in both positive 173 and negative ion mode consist of lipid and metabolite species [26]. The SpiderMass system has 174 been applied to analyze *ex vivo* ovarian cancer biopsies and *in vivo* human skin [27]. Moreover, 175 the SpiderMass system was also used to analyze *ex vivo* dog sarcoma samples, allowing for 97% 176 correct classification of tumor types and grades [38]. At present, the main disadvantage is the 177 fragility of the bare fiber in the used setup which is prone to breakage. 178

179 The Picosecond InfraRed Laser (PIRL) was first introduced as a new concept for laser surgery 180 with minimal postoperative scar formation [50]. Later in 2017, a handheld PIRL (Figure 2d) was 181 directly coupled to a MS (PIRL-MS), as for SpiderMass, through a heated inlet [51]. This 182 handheld device relies on a 300 picosecond impulsion duration which reduces the thermal 183 damage to the tissue providing a "cold" laser ablation. However, picosecond lasers yet remain 184 expensive and challenging regarding the fibering due to the peak energy for the injection into the 185 fiber. The system was applied to subcutaneous murine xenograft tumors from different 186 established human medulloblastoma cell lines [35]. A recent study demonstrated that the laser- 187 extracted lipids allow immediate grading of human medulloblastoma tumors into prognostically 188 important subgroups in 10 seconds. The robustness of the built classification model was tested 189 with the 20% leave out method resulting in 95.53% correct classification rate [36]. 190

191 *Extraction based techniques*

192 The MasSpec Pen (Figure 2e), developed in 2017, follows similar principles as liquid extraction 193 surface analysis and liquid microjunction surface sampling first introduced by Kertesz and Van 194 Berkel [52,53] in 2010. It is an automated, biocompatible, disposable handheld device which 195 allows sampling of tissues by liquid extraction [28]. It is composed of three parts: a syringe pump 196 delivering 4-10 µL of water to the sampling probe, PTFE tubing integrated with valves to 197 transport the water to and from the tissue after a 3s contact, and a pen-size device for probing the 198 biological tissues. The use of water as solvent allows damage-free extraction of hydrophilic and 199 amphiphilic compounds but requires the probe to be placed in tight contact with the tissue. The
MasSpec Pen was used to analyze *ex vivo* human biopsies of breast, thyroid, ovary, and lung cancer and healthy tissue or *in vivo* on mice model samples [28]. The performance of the MasSpec Pen was evaluated for rapid diagnosis of high-grade serous carcinoma where it showed 96.7% clinical sensitivity and 95.7 % specificity [33]. Recently, the system was used by 7 different surgeons *in vivo* and on excised tissues during 100 surgeries [54].

## Statistical treatment of the obtained molecular data

AIMS probes have been developed to bring MS into the operating room and in the hands of medical personnel. The majority of the described probes are designed for two clinical settings: the first one as an “online” *in vivo* approach used directly during tumor resection surgeries and the second one as an “offline” *ex vivo* approach on the bench as a diagnostic tool for clinicians and pathologists [55]. In both *in vivo* and *ex vivo* intraoperative settings the instrument needs to be trained to recognize cancer cells, types or grades before used in the clinical setting (Box 1). Then intraoperatively, the device uses its training data to provide an instant feedback to the surgeon. The first (training) step is realized upstream and can be done in the research or clinical lab. It requires collection of data from *ex vivo* analysis of tissue sections [26] or whole excised fresh/frozen tissues [25,28] with known diagnostic to enable building a molecular database based on pre-defined histopathological classes. Once the mass spectra are collected, the molecular profiles can be subjected to different machine learning algorithms. For example, principal component analysis in combination with linear discriminant analysis (PCA-LDA) has been used to create molecular databanks and to discriminate normal tissue from cancerous tissues [25,32,46], to perform sarcoma subtyping and grading [38] and glioma patient stratification [36]. Lasso regression has been used to build classification models on MasSpec Pen data to classify lung cancer, thyroid tumor [28], as well as high-grade serous ovarian cancer vs. normal tissue [33]. Recently, machine learning approaches have been challenged by new end-to-end deep learning algorithms, such as convolutional neural networks (CNNs) where feature extraction and classification can be learned in one step (Box 1). In the intraoperative context, transfer learning approaches have been explored to improve classification rates of several biological samples including sarcoma subtyping analyzed by the SpiderMass [56].
Once the classification models are built, they can be tested through different cross-validation techniques and on blind samples. Hence, in a second step, the classification models will be re-interrogated in real time during the surgery. Specific software (i.e “Recognition”, MediMass Ltd, LiveID) [26] has been developed to correlate real time molecular profiles to the pre-built classification models which will allow for *in vivo* classification during the surgery and prompt a simple readout to the surgeon. The readout is simplified for the clinician; for example, a color-code readout.

## Pros and cons of AIMS probes compared to other emerging *in vivo* guidance technologies

Concomitantly with AIMS probes, other new technologies based on Fluorescence, **Optical Coherence Tomography (OCT)** and **Raman** spectroscopy have emerged for real time analysis that also hold several interesting features for intraoperative margin evaluation. A comparison of the various hand-held or goggled *in vivo* guidance technologies, in terms of their appropriateness in the intraoperative context, is shown in **Table 1**.

**Fluorescence guided surgery (FGS)** encompasses of the broadest range of clinically approved and commercially available imaging systems and fluorophores [57]. Particularly, near-infrared (NIR) and auto-fluorescence imaging handheld systems such as the photodynamic eye (PDE) (Hamamatsu Photonics), Fluobeam (Fluoptics) and Artemis (Quest Medical Imaging BV) offer real time, *in vivo* tumor and surgical margin visualization. The models are operator friendly with low intra-operator variability. Contrary to AIMS probes, fluorescence requires the use of targeted fluorophores to indicate the precise location of primary tumors [58], lymph nodes [59], and aid in surgical resection [60,61]. The use of intrinsic autofluorescence can also indicate specific tissue properties, for example, to identify parathyroid glands [62,63]. An interesting experimental system in its early stages of clinical studies is “fluorescence-goggle” assisted imaging and navigation system which allows hands-free operation. The device was first validated in animal studies and later for hepatocellular carcinoma (HCC) detection in human patients [64,65]. It is affordable, portable, user-friendly, and equipped with telemedicine capability (Figure 3a). High-resolution fluorescence imaging has several limitations due to absorption and scattering but offers
greater penetration depth than other techniques. The goggled imaging devices show great promise and even permit a hand-free approach opposed to other handheld devices.

OCT is a high-resolution optical approach with similar functionality and use as US. OCT scanners provide iterative quantitative imaging information for intraoperative use [66-68]. A handheld OCT device was used in a multicenter study for *ex vivo* intraoperative assessment of final surgical margin during BCS [69] (Figure 3b). In a translational study, *in vivo* label-free video-based imaging was introduced by a unique custom-built portable OCT system [70] to assess the BCS margins and the resection bed with over 90% mean sensitivity and specificity. The principal difficulties of OCT scanners are sterility and operator stability, which limits the resolution and reproducibility. Indeed, the OCT probe and attached cord need to be wrapped in sterile drapes before being used in the operative field. Sterilization is also mandatory for AIMS probes and except for the laser-based probes, the surgeons are also required to use disposable tips or device to avoid cross-contamination.

Raman and AIMS probes are more closely related in regard to functionality, data acquisition and processing for interpretation. They both provide the ability to detect molecular information for tissue specific features and hence, distinguish healthy and cancer tissue. Spontaneous and **Stimulated Raman Scattering (SRS)** have been progressively used in intraoperative cancer diagnosis [71-77]. Though spontaneous Raman is less sensitive than SRS, it has been used for *in vivo* analysis in brain cancer [78,79], real time analysis of skin and nasopharyngeal carcinoma [80,81], while NIR confocal Raman was used for the investigation of cervical cancer [82]. Particularly, the handheld devices for surgical guidance and *in vivo* fingerprinting were able to distinguish between normal, tumor tissue and necrosis with accuracies over 90% in patients with grade 2-4 gliomas or metastasis associated with lung, colon and skin cancer [79,83] (Figure 3c). However, Raman requires emission enhancement and proper illumination setup to limit the external light source interference. It also requires sophisticated micrometer-scale light filtering components to extract tissue-specific signatures. Compared to AIMS probes, Raman is not invasive and also offers 2D imaging capabilities or 3D by stacking images or measuring at different depths.

Apart from FGS, most of the Raman and OCT studies were performed *ex vivo* with only one *in vivo* study currently published for both techniques. In contrast, three AIMS systems were
evaluated during *in vivo*, real time analysis in surgery. The iKnife system was first used *in vivo* during canine oncological surgery [24]. The second system, the SpiderMass, was implemented in an veterinary operating theater and tested on a canine patient in order to evaluate the low invasiveness of the system [38]. The MasSpec pen device was recently used by 7 different surgeons and demonstrated *in vivo* during 100 surgeries [54]. Still the AIMS probes are facing technical challenges to be addressed like the portability of such systems and their reproducibility/robustness.

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## Concluding Remarks

Many efforts have been made in the last decade to improve intraoperative cancer margin delineation and diagnosis. New emerging technologies have shown great potential to pave the way for intraoperative guidance, particularly Raman and MS. For example, intraoperative MS has been shown to provide minute *ex vivo* and *in vivo* analysis of cancer tissue with high specificity and sensitivity. There are, however, still many challenges to be addressed for these technologies to become the next gold standard in cancer surgery. It will include the miniaturization and design of the probes, multicentric studies, the development of imaging, the refinement of bioinformatic solutions and the design of autonomous devices (see **Outstanding Questions**). We also foresee development of the devices for surgical interventions as add-ons in robotic-assisted tools such as the Da Vinci Robot (Intuitive Surgical) [84] or RoSA (Zimmer Biomet) [85] and combined with AI screening tools for minimally invasive and guided surgery. Some efforts have already been made in that direction with the development of the endoscopic REIMS system [32] and the coupling of the MasSpec Pen with the Da Vinci Robot on a porcine model simulation [86]. Autonomous surgical robots are also a clear direction enabled by SpiderMass-guided surgery [87]. Finally, the future vision for surgery 2.0 is certainly based on autonomous robotic devices for guidance of the MS-probe in minimally invasive surgery. Such systems are foreseen to be coupled to Augmented Reality (AR), as the PIRL-MS in preliminary *ex vivo* experiments with the optical surgical tracking system [88], to provide the surgeon with the right decision making. While there are still many roadblocks ahead before the probes can be widely accepted as medical tools in routine clinical use, the recently developed MS probes are moving towards rapid, ease-of-use and cost-effective solutions for improved patient care.

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531 105
546 **Figure Legends**

547 **Figure 1.** Schematic representation of cancer surgery and the diagnostic modalities involved in
548 each step of the process. Optical imaging techniques are preferred in the pre-operative context
549 like MRI and CT-scans. In an intraoperative context, the removal of the tumor can be guided by
550 imaging with intraoperative MRI but usually, the tumor piece or biopsies are removed for
551 extemporaneous assessment by the pathologist. Gross examination or sectioning and fast staining
552 / observation are conducted. Then, after the surgery, the classical Formalin Fixed Paraffin
553 Embedded (FFPE) histological workflow is unfolded. Emerging modalities in the intraoperative
554 time course are now being used like ultrasound, OCT, Fluorescence, RAMAN, or Mass
555 spectrometry. The tumor drawing and histological slides were adapted from Servier Medical Art
556 (https://smart.servier.com/) and are licensed under a Creative Commons Attribution 3.0 license
557 (CC BY 3.0).

558

559 **Figure 2.** Schematic of AIMS handheld probes and their invasiveness along with some examples
560 of applications. The diverse probes are arranged by their principle of operation: in green are the
561 techniques based on existing surgical tools like CUSA (A) and iKnife (B). In light red represents
562 the IR laser-based techniques such as the SpiderMass (C) and PIRL-MS (D). Blue is the solvent
563 extraction with the MasSpec Pen (E). A close view on the probe and tissue being analyzed is
564 shown with comparable size to visualize the systems invasiveness alongside typical spectra and
565 3D visualization of the classification models obtained with the technologies. **A.** CUSA negative
566 ion mode spectra of human brain gray matter (top) and white matter (bottom) and plot of first
567 three LDA parameters of different human brain tissue samples. Adapted with permission from
568 [89] **B.** iKnife negative ion mode spectra from benign (top) and malignant (bottom) cervical
569 biopsies and associated LDA model. Adapted with permission from [45] (licensed under [CC CY-
570 NC-ND 4.0](https://creativecommons.org/licenses/by-nc-nd/4.0/)). **C.** SpiderMass Positive ion mode mass spectra obtained from necrotic, cancerous,
571 and normal dog sarcoma biopsies and the related LDA score plot representation. Adapted from
572 [38]. **D.** PIRL-MS spectra collected in the negative ion mode from DAOY and UW228 cell lines
573 xenograft and its associated Low complexity Partial Least Squares Discriminant Analysis.
574 Adapted with permission from [35]. **E.** MasSpec Pen negative ion mode mass spectra obtained
575 from a normal human thyroid tissue section (top) and a Papillary Thyroid Carcinoma tissue
section (bottom) alongside a PCA score plots representation for this tissue type. Adapted with permission from [28].

**Figure 3.** Emerging *in vivo* hand-held devices for intraoperative guidance based on optical and spectroscopic analysis **A.** Fluorescence goggle-assisted imaging and navigation system (top). Optical image (bottom left) and fluorescence information (bottom right) displayed in the goggle eyepiece during HCC resection. Adapted with permission from [65] ; copyright 2020, Elsevier **B.** Hand-held optical coherence tomography (OCT) system (**i.**) and ex vivo breast shaving margin assessment (**ii.**). Adapted with permission from [69] ; copyright 2020, Springer Nature. **C.** Hand-held contact Raman spectroscopy probe for *in vivo* assessment during glioma surgery. Adapted from [79]; The figure is licensed under a [Creative Commons Attribution 4.0 International License](https://creativecommons.org/licenses/by/4.0/)

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<table><caption>Table 1. Overview and comparison of detection, in vivo/ex vivo use, penetration depth, spatial resolution, speed, sensitivity and specificity of the emerging handheld techniques</caption><thead><tr><th></th><th>Emerging handheld probes</th><th>Detection</th><th>In vivo/ex vivo</th><th>Penetration depth</th><th>Spatial resolution</th><th>Speed</th><th>Sensitivity /specificity</th><th>Ref</th></tr></thead><tbody><tr><td rowspan="4">Fluorescence guided surgery</td><td>Photodynamic eye (PDE)*</td><td>ICG fluorescence</td><td>In vivo</td><td>1 cm</td><td>~10 µm</td><td>millisecond range</td><td rowspan="4">Superficial tissue high sensitivity but decrease with depth</td><td>[60-61]</td></tr><tr><td>Fluobeam*</td><td>ICG or autoflourescence</td><td>In vivo</td><td>1 cm</td><td>~10 µm</td><td>millisecond range</td><td>[62]</td></tr><tr><td>Artemis*</td><td>ICG or autoflourescence</td><td>In vivo</td><td>1 cm</td><td>~10 µm</td><td>millisecond range</td><td>[63]</td></tr><tr><td>Goggle-assisted imaging</td><td>ICG fluorescence</td><td>In vivo</td><td>5-15mm</td><td>FOV 12cm x 10cm at 5 m working distance</td><td>12 frames/s</td><td>[64-65]</td></tr><tr><td rowspan="2">OCT</td><td>Handheld surgical OCT probe</td><td>optical imaging</td><td>In vivo/ex vivo</td><td>1-2mm</td><td>2-10 µm</td><td>11.5 frames/sec</td><td>91.7% sensitivity and 92.1% specificity ex vivo</td><td>[70]</td></tr><tr><td>handheld surgical OCT probe</td><td>optical imaging</td><td>ex vivo</td><td>2-3mm</td><td>&lt;15 µm</td><td>&gt;2.3 cm/sec scan speed</td><td>80% sensitivity and 69% specificity</td><td>[69]</td></tr><tr><td rowspan="2">Raman</td><td>Intraoperative Raman probe</td><td>Raman lipid and protein spectra</td><td>In vivo,</td><td>1mm</td><td>0.5 mm</td><td>0.2s/three acquisitions</td><td>Sensitivity and specificity &gt; 90%</td><td>[78]</td></tr><tr><td>Optical biopsy needle</td><td>Raman lipid and protein spectra</td><td>In situ during biopsy collection</td><td>1mm</td><td>0.5mm</td><td>0.5-2s</td><td>Sensitivity 80% and specificity 90%</td><td>[79]</td></tr><tr><td rowspan="4">AIMS handheld probes</td><td>i-Knife*</td><td>Mass lipid and metabolite spectra</td><td>In vivo/ex vivo</td><td>~3.65 mm</td><td>0.5-2mm</td><td>3s</td><td>Sensitivity and specificity &gt; 90%</td><td>[25, 26, 30-32, 44-46]</td></tr><tr><td>SpiderMass</td><td>Mass lipid and metabolite spectra</td><td>In vivo/ex vivo</td><td>2-4µm/ laser shot</td><td>400-500µm</td><td>3s</td><td>Sensitivity and specificity &gt; 90%</td><td>[26, 27, 38]</td></tr><tr><td>PIRL</td><td>Mass lipid and metabolite spectra</td><td>Ex vivo</td><td>300 µm with 2-10 mm/sec movement</td><td>~500 µm</td><td>10s</td><td>Sensitivity and specificity &gt; 90%</td><td>[35,36, 51]</td></tr><tr><td>MassSpec Pen</td><td>Mass lipid and metabolite spectra</td><td>In vivo/ex vivo</td><td>Chemical-extraction based penetration</td><td>0.5 to 5 mm</td><td>3s</td><td>Sensitivity and specificity &gt; 90%</td><td>[28, 33, 54]</td></tr></tbody></table>

<footnote>*Commercially available and clinically tested probes. Note that the iKnife is commercially available solely for research purposes.</footnote>
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**Glossary**

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**Artificial intelligence (AI)** is intelligence demonstrated by machines (e.g. computers) in contrast to natural intelligence demonstrated by animals and humans.

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**Algorithmic decision trees** are decision support tools that use a tree-like model of decisions and their possible consequences. In pathology decision trees are used to sufficiently stratify patients into reproducible groups based on tumor phenotypes.

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**Computed Tomography Scan**: Several X-ray (wavelength from 10 nm to 5 pm) scans Computed to reconstruct a Tomography are called CT-scan and are used in a pre-operative context for diagnosis and staging of cancer.

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**Electrospray Ionization (ESI)**: a flow of solvent in which the analyte is present is transferred into a mist of droplets that slowly evaporate to free the molecules in the gas phase. Currents are applied to charge those droplets and transfer them to the analytes.

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**Magnetic resonance (MRI)** employs a strong external magnetic field that changes the spin of certain nuclei (mostly hydrogen). During the relaxation it emits radiofrequency (RF) which can be measured and imaged.

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**MALDI (Matrix-Assisted Laser Desorption/Ionisation)**: A laser beam, UV or IR, is fired against a prepared solution of the analytes together with an absorbing molecule so-called “matrix” to create a cascade of reactions leading to the transfer of charges to the analytes.

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**Raman** uses spectral analysis to identify the unique wavelength shift from a monochromatic beam on a tissue backscattered light to identify the molecular signatures (amino acids, lipids proteins and nucleic acids) of tissue that has undergone disease transformation.

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**Ultrasound (US)**: Sound waves with frequencies over 20 kHz (mainly between 1 and 18 MHz) are used to create images of internal structures of the body by reconstructing the echo coming from tissues bearing various reflection properties.

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**Fluorescence guided surgery (FGS)** visualizes tumors in real time either through autofluorescence, conventional fluorescent techniques using probes in the visible light spectrum or near-infrared spectrum. The most common FGSs use targeted probes in the near-infrared
634 (NIR) window (approximately 700-900 nm) with largest penetration depth of optical light in
635 tissue (5-15 mm).

636 **Optical Coherence Tomography (OCT)** is a high-resolution optical approach using low-
637 coherence light and interferometry to generate cross-sectional depth resolved in two-dimensional
638 and three-dimensional (3D) images.

639 **Stimulated Raman (SRS)** uses a second light source that induces a stimulated and enhanced
640 scattering of light. It has been used to monitor dynamic changes, alterations in tissue cellularity,
641 axonal density and protein/lipid ratios.
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Machine learning: In the field of AI, machine learning (ML) represents the development of algorithms that learn from a massive amount of data and experience to improve their predictive values (or accuracy) over time. It is not solely dedicated for classification purposes but in this case, they are conducted in two phases: first is the learning by a training dataset and second, the application of the system on a test dataset and on "real" cases as shown in Figure I.

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Usual ML algorithm relies on a human intervention to be presented with annotated datasets for "supervised" analysis with the goal of understanding the rules linking the variables to the annotation in a classification manner. Alternatively, it can rely only on the data without any annotation, in an "unsupervised" analysis, in which the algorithm needs to determine the underlying structures of the inputs by itself in a clustering manner. The most popular ML approaches for MS cancer-related diagnostics include PCA/LDA models, "LASSO" for Least Absolute Shrinkage and Selection Operator [90], Partial Least Squares Discriminant Analysis (PLS-DA) [35], Support Vector Machine (SVM) [91] and Random Forest [92]. In general, these methods aim to reduce the complexity / dimensionality of the data itself to make it understandable to human minds.

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Deep Learning: Amongst the ML techniques are the deep learning (DL) algorithms which are characterized by the ability to extract useful representation of the data directly from the raw data with reduced preprocessing or human intervention. The most represented methods in DL are the ones derived from neural networks and mostly Convolutional Neural Networks (CNN) where the feature discovery and the final task of the model is done in the same training process, making it more powerful. These methods compute several convolutional and non-linear transformations for the final classification stage of the network [93]. Neural networks are starting to be used in medical imaging because they are well-suited for image-oriented data. In clinical mass spectrometry, the volume of data available is not yet high enough. Indeed, the power of CNN evolves exponentially with the increasing volume of data, so they work best with large datasets.

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**Figure I.** Classification model construction, analysis, and validation workflow (from right to left). The two-fold workflow begins with *ex vivo* instrument teaching with analysis of fresh or
frozen biopsies or excised tissue (right panel). The samples have pre-assigned classes given by
the pathologists. Each spectrum serves as a molecular barcode. Once an adequate number of
samples is analyzed they are subjected to multivariate statistical analysis to build the
classification models and perform cross-validation. Once the model has been built it is used on
validation cohorts and on test cohorts then on *in vivo* real subjects (left panel). The model is
interrogated in real time and displays user feedback.

677
678 **Box 2 – Guide to Ambient ionization mass spectrometry (AIMS) techniques**

679 A timeline of important MS and AIMS developments is shown in Figure II. They derive from the
680 two main ways of generating ions that are needed for the mass spectrometric analysis; the
681 **Electrospray Ionization - ESI** and **Matrix-Assisted Laser Desorption Ionization - MALDI**
682 (See Glossary). For the benchtop laboratory analysis, desorption and ionization can be achieved
683 by means of:

684 - A flow of charged solvent droplets applied onto the surface to be analyzed allowing
685 chemicals to be dissolved, ionized and teared by the gas flow to the MS inlet (Desorption
686 Electrospray Ionization; DESI) [94] or by a flow of charged gas (Direct analysis in real
687 time: DART) [95]

688 - Photons alone: Atmospheric Pressure MALDI (AP-MALDI) [96]

689 - Combination of photons and a ESI: Laser-Ablation and ESI (LAESI) [97] or MALDI
690 Electrospray Ionization (MALDESI) [98]

691 - Physical probing and ESI: Probe Electrospray Ionization analysis (PESI) [99], needle
692 biopsy and spray ionization [100]

693 The techniques show great potential and are ideal for benchtop routine analysis of patient
694 samples but cannot be used *in vivo* due to safety, biocompatibility, invasiveness issues or
695 incompatibility with the size of the sample. Spatial tissue analysis has been possible by the
696 development of several imaging methodologies for example DESI-MSI, LAESI, MALDESI, etc.
697 Out of many, DESI is the most used for chemical imaging and intraoperative diagnosis of tissue
698 samples [101–103]. In the majority of analyzed clinical samples, lipid species encompassing
699 different classes, such as fatty acids and glycerophospholipids, were observed as the
700 discriminating molecules in cancer vs. normal tissue comparison. The demonstration of DESI and
701 DESI-MSI for cancer diagnosis and characterization was conducted on several pathologies from
702 metastatic liver adenocarcinoma, breast, brain, prostate and more.

703
**Figure II.** General timeline of significant events in the evolution of MS toward clinical application and FDA clearance and approval along with technical developments leading to AIMS and intraoperative techniques. It begins with the Nimitz plane accident in which marijuana consumption were detected in the pilots. It led to a zero tolerance against drug uses in the US Army and the increase of drug testing by immunoassays which showed a high rate of false positive. The MS showed promising results and replaced immunoassays in drug testing or newborn disease screening with GC-MS on Dried Blood Spots (DBS) and rapid identification of intact bacteria using MALDI. Meanwhile, the ESI ionization source started to be used and implemented to lower the processing time, the cost per sample and improve the technique turnover. Since 2013, the identification of microbes using MALDI-TOF was approved by the FDA. The second aspect of MS analysis in a clinical environment has been possible by the development of AIMS techniques derived from ESI like DESI, PESI, and LAESI, or from other modalities with the DART, REIMS and the other intraoperative techniques like SpiderMass, PIRL and the MasSpec Pen that lead to the in situ analysis of tumor lipid profiles, tumor subtyping and metastatic status prediction. Partially adapted from https://www.clinicallabmanager.com/trends/clinical-mass-spectrometry/milestones-in-clinical-mass-spectrometry-279.

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<figure><img src="image_2.png" /></figure>

New tools are emerging for intraoperative assessment, which are not traditional medical technologies, such as Raman or MS. Interestingly, these technologies are mini-invasive and could even be operated *in vivo* while avoiding the use of labeled tracers.

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<figure><img src="image_3.png" /></figure>

MS has demonstrated unprecedented sensitivity and specificity compared to other tools particularly in distinguishing cancer tissues and even providing access to cancer grade, type or sub-type.

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<figure><img src="image_4.png" /></figure>

New MS devices operating *in vivo* and in real time have recently been developed and were successfully showcased in the operating theater. MS systems provide instant feedback to surgeons and are foreseen as a novel tool for surgery guidance, to accurately delineate the tumor margins and find the locoregional extension of the cancer.

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<figure><img src="image_5.png" /></figure>

MS systems are based on AI and are trained to recognize cancer cells. Their performances will continue to improve through, in particular, the development of novel deep learning solutions.

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<figure><img src="image_6.png" /></figure>

Robotics associated with MS devices is the next step for these new tools. They will open the door to new surgical instruments based on a molecular eye for real time diagnosis. These new devices will help surgeons in their decision making and will allow personalized patient therapies.
Pre-operative

<figure><img src="image_7.png" /></figure>

MRI / CT

<figure><img src="image_8.png" /></figure>

Intraoperative
Examination

<figure><img src="image_9.png" /></figure>

**In vivo**
MRI / Ultrasound / OCT
Fluorescence / Raman
**Mass Spectrometry**

Intra-operative

**TO**

Tissue
Resection

Or biopsies

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

Gross
Examination

**20-40 min**

Cryo-sectioning

Fast staining

<figure><img src="image_12.png" /></figure>

<figure><img src="image_13.png" /></figure>

Microscopic
Examination

Post-operative

**48h & ++**

<figure><img src="image_14.png" /></figure>

Staining

<figure><img src="image_15.png" /></figure>

Microscopic examination
Molecular analysis

<figure><img src="image_16.png" /></figure>

**Ex vivo**
Immunohistochemistry
Genetic sequencing
**Mass Spectrometry**

<figure><img src="image_17.png" /></figure>

**Ex vivo**
OCT / Raman / MRI
**Mass Spectrometry**
A.

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

B.

<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>

<figure><img src="image_24.png" /></figure>

C.

<figure><img src="image_25.png" /></figure>

<figure><img src="image_26.png" /></figure>

<figure><img src="image_27.png" /></figure>

<figure><img src="image_28.png" /></figure>

<figure><img src="image_29.png" /></figure>

D.

<figure><img src="image_30.png" /></figure>

E.

Thyroid
56 patients

<figure><img src="image_31.png" /></figure>

<figure><img src="image_32.png" /></figure>

<figure><img src="image_33.png" /></figure>

m/z

<figure><img src="image_34.png" /></figure>

PLS-DA Scores Plot

<figure><img src="image_35.png" /></figure>

SHH MB
(DAOY xenograft)

Necrosis

<figure><img src="image_36.png" /></figure>

<figure><img src="image_37.png" /></figure>
A.

<figure><img src="image_38.png" /></figure>

<figure><img src="image_39.png" /></figure>

<figure><img src="image_40.png" /></figure>

<figure><img src="image_41.png" /></figure>

<figure><img src="image_42.png" /></figure>

B.

<figure><img src="image_43.png" /></figure>

i.

<figure><img src="image_44.png" /></figure>

ii.

C.

<figure><img src="image_45.png" /></figure>