Gait analysis comparison between manual marking, 2D pose estimation algorithms, and 3D marker-based system

IntroductionRecent advances in Artificial Intelligence (AI) and Computer Vision (CV) have led to automated pose estimation algorithms using simple 2D videos. This has created the potential to perform kinematic measurements without the need for specialized, and often expensive, equipment. Even though there's a growing body of literature on the development and validation of such algorithms for practical use, they haven't been adopted by health professionals. As a result, manual video annotation tools remain pretty common. Part of the reason is that the pose estimation modules can be erratic, producing errors that are difficult to rectify. Because of that, health professionals prefer the use of tried and true methods despite the time and cost savings pose estimation can offer.MethodsIn this work, the gait cycle of a sample of the elderly population on a split-belt treadmill is examined. The Openpose (OP) and Mediapipe (MP) AI pose estimation algorithms are compared to joint kinematics from a marker-based 3D motion capture system (Vicon), as well as from a video annotation tool designed for biomechanics (Kinovea). Bland-Altman (B-A) graphs and Statistical Parametric Mapping (SPM) are used to identify regions of statistically significant difference.ResultsResults showed that pose estimation can achieve motion tracking comparable to marker-based systems but struggle to identify joints that exhibit small, but crucial motion.DiscussionJoints such as the ankle, can suffer from misidentification of their anatomical landmarks. Manual tools don't have that problem, but the user will introduce a static offset across the measurements. It is proposed that an AI-powered video annotation tool that allows the user to correct errors would bring the benefits of pose estimation to professionals at a low cost

Human Body Motions Optimization for Able-Bodied Individuals and Prosthesis Users During Activities of Daily Living Using a Personalized Robot-Human Model

Current clinical practice regarding upper body prosthesis prescription and training is lacking a standarized, quantitative method to evaluate the impact of the prosthetic device. The amputee care team typically uses prior experiences to provide prescription and training customized for each individual. As a result, it is quite challenging to determine the right type and fit of a prosthesis and provide appropriate training to properly utilize it early in the process. It is also very difficult to anticipate expected and undesired compensatory motions due to reduced degrees of freedom of a prosthesis user. In an effort to address this, a tool was developed to predict and visualize the expected upper limb movements from a prescribed prosthesis and its suitability to the needs of the amputee. It is expected to help clinicians make decisions such as choosing between a body-powered or a myoelectric prosthesis, and whether to include a wrist joint. To generate the motions, a robotics-based model of the upper limbs and torso was created and a weighted least-norm (WLN) inverse kinematics algorithm was used. The WLN assigns a penalty (i.e. the weight) on each joint to create a priority between redundant joints. As a result, certain joints will contribute more to the total motion. Two main criteria were hypothesized to dictate the human motion. The first one was a joint prioritization criterion using a static weighting matrix. Since different joints can be used to move the hand in the same direction, joint priority will select between equivalent joints. The second criterion was to select a range of motion (ROM) for each joint specifically for a task. The assumption was that if the joints\u27 ROM is limited, then all the unnatural postures that still satisfy the task will be excluded from the available solutions solutions. Three sets of static joint prioritization weights were investigated: a set of optimized weights specifically for each task, a general set of static weights optimized for all tasks, and a set of joint absolute average velocity-based weights. Additionally, task joint limits were applied both independently and in conjunction with the static weights to assess the simulated motions they can produce. Using a generalized weighted inverse control scheme to resolve for redundancy, a human-like posture for each specific individual was created. Motion capture (MoCap) data were utilized to generate the weighting matrices required to resolve the kinematic redundancy of the upper limbs. Fourteen able-bodied individuals and eight prosthesis users with a transradial amputation on the left side participated in MoCap sessions. They performed ROM and activities of daily living (ADL) tasks. The methods proposed here incorporate patient\u27s anthropometrics, such as height, limb lengths, and degree of amputation, to create an upper body kinematic model. The model has 23 degrees-of-freedom (DoFs) to reflect a human upper body and it can be adjusted to reflect levels of amputation. The weighting factors resulted from this process showed how joints are prioritized during each task. The physical meaning of the weighting factors is to demonstrate which joints contribute more to the task. Since the motion is distributed differently between able-bodied individuals and prosthesis users, the weighting factors will shift accordingly. This shift highlights the compensatory motion that exist on prosthesis users. The results show that using a set of optimized joint prioritization weights for each specific task gave the least RMS error compared to common optimized weights. The velocity-based weights had a slightly higher RMS error than the task optimized weights but it was not statistically significant. The biggest benefit of that weight set is their simplicity to implement compared to the optimized weights. Another benefit of the velocity based weights is that they can explicitly show how mobile each joint is during a task and they can be used alongside the ROM to identify compensatory motion. The inclusion of task joint limits gave lower RMS error when the joint movements were similar across subjects and therefore the ROM of each joint for the task could be established more accurately. When the joint movements were too different among participants, the inclusion of task limits was detrimental to the simulation. Therefore, the static set of task specific optimized weights was found to be the most accurate and robust method. However, the velocity-based weights method was simpler with similar accuracy. The methods presented here were integrated in a previously developed graphical user interface (GUI) to allow the clinician to input the data of the prospective prosthesis users. The simulated motions can be presented as an animation that performs the requested task. Ultimately, the final animation can be used as a proposed kinematic strategy that a prosthesis user and a clinician can refer to, during the rehabilitation process as a guideline. This work has the potential to impact current prosthesis prescription and training by providing personalized proposed motions for a task

Βελτιστοποίηση κινήσεων ανθρώπινου σώματος για αρτιμελή άτομα και χρήστες προσθετικών κατά τη διάρκεια δραστηριοτήτων της καθημερινής ζωής χρησιμοποιώντας ένα εξατομικευμένο ρομπότικο μοντέλο του ανθρώπινου σώματος

Current clinical practice regarding upper body prosthesis prescription and training is lacking a standarized, quantitative method to evaluate the impact of the prosthetic device. The amputee care team typically uses prior experiences to provide prescription and training customized for each individual. As a result, it is quite challenging to determine the right type and fit of a prosthesis and provide appropriate training to properly utilize it early in the process. It is also very difficult to anticipate expected and undesired compensatory motions due to reduced degrees of freedom of a prosthesis user. In an effort to address this, a tool was developed to predict and visualize the expected upper limb movements from a prescribed prosthesis and its suitability to the needs oft he amputee. It is expected to help clinicians make decisions such as choosing between a body powered or a myoelectric prosthesis, and whether to include a wrist joint. To generate the motions, a robotics-based model of the upper limbs and torso was created and a weighted least-norm (WLN) inverse kinematics algorithm was used. The WLN assigns a penalty (i.e. the weight) on each joint to create a priority between redundant joints. As a result, certain joints will contribute more to the total motion. Two main criteria were hypothesized to dictate the human motion. The first one was a joint prioritization criterion using a static weighting matrix. Since different joints can be used to move the hand in the same direction, joint priority will select between equivalent joints. The second criterion was to select a range of motion (ROM)for each joint specifically for a task. The assumption was that if the joints’ ROM is limited, then all the unnatural postures that still satisfy the task will be excluded from the available solutions. Three sets of static joint prioritization weights were investigated: a set of optimized weights specifically for each task, a general set of static weights optimized for all tasks, and a set of joint absolute average velocity-based weights. Additionally, task joint limits were applied both independently and in conjunction with the static weights to assess the simulated motions they can produce. Using a generalized weighted inverse control scheme to resolve for redundancy, a human-like posture for each specific individual was created. Motion capture (MoCap) data were utilized to generate the weighting matrices required to resolve the kinematic redundancy of the upper limbs. Fourteen able-bodied individuals and eight prosthesis users with a transradial amputation on the left side participated in MoCap sessions. They performed ROM and activities of daily living (ADL) tasks. The methods proposed here incorporate patient’s anthropometrics, such as height, limb lengths, and degree of amputation, to create an upper body kinematic model. The model has 23 degrees-of-freedom (DoFs) to reflect a human upper body and it can be adjusted to reflect levels of amputation. The weighting factors resulted from this process showed how joints are prioritized during each task. The physical meaning of the weighting factors is to demonstrate which joints contribute more to the task. Since the motion is distributed differently between able-bodied individuals and prosthesis users, the weighting factors will shift accordingly. This shift highlights the compensatory motion that exist on prosthesis users. The results show that using a set of optimized joint prioritization weights for each specific task gave the least RMS error compared to common optimized weights. The velocity-based weights had a slightly higher RMS error than the task optimized weights but it was not statistically significant. The biggest benefit of that weight set is their simplicity to implement compared to the optimized weights. Another benefit of the velocity based weights is that they can explicitly show how mobile each joint is during a task and they can be used alongside the ROM to identify compensatory motion. The inclusion of task joint limits gave lower RMS error when the joint movements were similar across subjects and therefore the ROM of each joint for the task could be established more accurately. When the joint movements were too different among participants, the inclusion of task limits was detrimental to the simulation. Therefore, the static set of task specific optimized weights was found to be the most accurate and robust method. However, the velocity-based weights method was simpler with similar accuracy. The methods presented here were integrated in a previously developed graphical user interface(GUI) to allow the clinician to input the data of the prospective prosthesis users. The simulated motions can be presented as an animation that performs the requested task. Ultimately, the final animation can be used as a proposed kinematic strategy that a prosthesis user and a clinician can refer to, during the rehabilitation process as a guideline. This work has the potential to impact current prosthesis prescription and training by providing personalized proposed motions for a task.Η τρέχουσα κλινική πρακτική όσον αφορά τη συνταγογράφηση και την εκπαίδευση για την πρόθεση του άνω μέρους του σώματος είναι ελλιπής μια τυποποιημένη, ποσοτική μέθοδος για την αξιολόγηση της επίδρασης της προσθετικής συσκευής. Ο ακρωτηριασμένος ομάδα φροντίδας χρησιμοποιεί συνήθως προηγούμενες εμπειρίες για να παρέχει συνταγογράφηση και εκπαίδευση προσαρμοσμένη για κάθε άτομο. Ως αποτέλεσμα, είναι αρκετά δύσκολο να προσδιοριστεί ο σωστός τύπος και η εφαρμογή μιας πρόθεσης και η παροχή της κατάλληλης εκπαίδευσης για τη σωστή χρήση του σε πρώιμο στάδιο της διαδικασίας. Είναι επίσης πολύ δύσκολο να προβλεφθούν οι αναμενόμενες και ανεπιθύμητες αντισταθμιστικές κινήσεις λόγω των μειωμένων βαθμών ελευθερίας του ενός χρήστη πρόθεσης. Σε μια προσπάθεια να αντιμετωπιστεί αυτό, αναπτύχθηκε ένα εργαλείο για την πρόβλεψη και την οπτικοποίηση των αναμενόμενες κινήσεις των άνω άκρων από μια συνταγογραφούμενη πρόθεση και την καταλληλότητά της για τις ανάγκες του ακρωτηριασμένου. Αναμένεται να βοηθήσει τους κλινικούς γιατρούς να λαμβάνουν αποφάσεις, όπως η επιλογή μεταξύ ενός σωματικού ή μιας μυοηλεκτρικής πρόθεσης και αν θα συμπεριλάβουν άρθρωση καρπού. Για τη δημιουργία των κινήσεων, δημιουργήθηκε ένα ρομποτικό μοντέλο των άνω άκρων και του κορμού. και χρησιμοποιήθηκε ένας αλγόριθμος αντίστροφης κινηματικής σταθμισμένης ελάχιστης κανονικότητας (WLN). Ο WLN αποδίδει ένα ποινή (δηλ. το βάρος) σε κάθε άρθρωση για τη δημιουργία μιας προτεραιότητας μεταξύ περιττών αρθρώσεων. Ως αποτέλεσμα, ορισμένες αρθρώσεις θα συνεισφέρουν περισσότερο στη συνολική κίνηση. Δύο βασικά κριτήρια θεωρήθηκαν ως εξής υπαγορεύουν την ανθρώπινη κίνηση. Το πρώτο ήταν ένα κριτήριο ιεράρχησης των αρθρώσεων που χρησιμοποιεί μια στατική στάθμιση matrix. Δεδομένου ότι διαφορετικές αρθρώσεις μπορούν να χρησιμοποιηθούν για την κίνηση του χεριού προς την ίδια κατεύθυνση, η προτεραιότητα των αρθρώσεων θα επιλέγει μεταξύ ισοδύναμων αρθρώσεων. Το δεύτερο κριτήριο ήταν η επιλογή ενός εύρους κίνησης (ROM) για κάθε άρθρωση ειδικά για μια εργασία. Η υπόθεση ήταν ότι εάν το ROM των αρθρώσεων είναι περιορισμένο, τότε όλες οι αφύσικες στάσεις που εξακολουθούν να ικανοποιούν την εργασία θα αποκλειστούν από τις διαθέσιμες λύσεις. Διερευνήθηκαν τρία σύνολα στατικών βαρών ιεράρχησης των αρθρώσεων: ένα σύνολο βελτιστοποιημένων βαρών ειδικά για κάθε εργασία, ένα γενικό σύνολο στατικών βαρών βελτιστοποιημένο για όλες τις εργασίες και ένα σύνολο βαρών με βάση την απόλυτη μέση ταχύτητα των αρθρώσεων. Επιπλέον, τα όρια των αρθρώσεων της εργασίας εφαρμόστηκαν τόσο ανεξάρτητα όσο και σε συνδυασμό με τα στατικά βάρη για την αξιολόγηση των προσομοιωμένων κινήσεων που μπορούν να παράγουν. Χρησιμοποιώντας ένα γενικευμένο σύστημα σταθμισμένου αντίστροφου ελέγχου για την επίλυση του πλεονασμού, ένα δημιουργήθηκε μια ανθρώπινη στάση για κάθε συγκεκριμένο άτομο. Τα δεδομένα καταγραφής κίνησης (MoCap) χρησιμοποιήθηκαν για τη δημιουργία των πινάκων στάθμισης που απαιτούνται για την επίλυση του κινηματικού πλεονασμού των άνω άκρων. Δεκατέσσερα υγιή άτομα και οκτώ χρήστες προθέσεων με διακρατικό ακρωτηριασμό στην αριστερή πλευρά συμμετείχαν σε συνεδρίες MoCap. Εκτέλεσαν εργασίες ROM και δραστηριότητες καθημερινής διαβίωσης (ADL). Οι μέθοδοι που προτείνονται εδώ ενσωματώνουν τα ανθρωπομετρικά στοιχεία του ασθενούς, όπως το ύψος, τα μήκη των άκρων και ο βαθμός ακρωτηριασμού, για τη δημιουργία ενός κινηματικού μοντέλου του άνω μέρους του σώματος. Το μοντέλο διαθέτει 23 βαθμούς ελευθερίας (DoF) για να αντικατοπτρίζει ένα ανθρώπινο άνω σώμα και μπορεί να προσαρμοστεί ώστε να αντικατοπτρίζει τα επίπεδα ακρωτηριασμού. Οι συντελεστές στάθμισης που προέκυψαν από αυτή τη διαδικασία έδειξαν πώς οι αρθρώσεις ιεραρχούνται κατά τη διάρκεια σε κάθε εργασία. Το φυσικό νόημα των συντελεστών στάθμισης είναι να καταδείξουν ποιες αρθρώσεις συμβάλλουν περισσότερο στην εργασία. Δεδομένου ότι η κίνηση κατανέμεται διαφορετικά μεταξύ των αρτιμελών ατόμων και των χρήστες πρόθεσης, οι συντελεστές στάθμισης θα μετατοπιστούν ανάλογα. Αυτή η μετατόπιση αναδεικνύει την αντισταθμιστική κίνηση που υπάρχει στους χρήστες προσθετικών μελών. Τα αποτελέσματα δείχνουν ότι η χρήση ενός συνόλου βελτιστοποιημένων κοινών βαρών ιεράρχησης για κάθε συγκεκριμένο εργασία έδωσε το μικρότερο σφάλμα RMS σε σύγκριση με τα κοινά βελτιστοποιημένα βάρη. Η βασισμένη στην ταχύτητα βάρη είχαν ελαφρώς υψηλότερο σφάλμα RMS από τα βελτιστοποιημένα βάρη για εργασίες, αλλά δεν ήταν στατιστικά. Το μεγαλύτερο πλεονέκτημα αυτού του συνόλου βαρών είναι η απλότητα της εφαρμογής τους σε σύγκριση μετα βελτιστοποιημένα βάρη. Ένα άλλο πλεονέκτημα των βαρών με βάση την ταχύτητα είναι ότι μπορούν ρητά να δείχνουν πόσο κινητική είναι κάθε άρθρωση κατά τη διάρκεια μιας εργασίας και μπορούν να χρησιμοποιηθούν παράλληλα με τη ROM για τον εντοπισμό αντισταθμιστική κίνηση. Η συμπερίληψη των ορίων των αρθρώσεων της εργασίας έδωσε χαμηλότερο σφάλμα RMS όταν η άρθρωση κινήσεις ήταν παρόμοιες σε όλα τα υποκείμενα και επομένως η ROM κάθε άρθρωσης για την εργασία θα μπορούσε νανα καθοριστεί με μεγαλύτερη ακρίβεια. Όταν οι κινήσεις των αρθρώσεων ήταν πολύ διαφορετικές μεταξύ των συμμετεχόντων, η συμπερίληψη των ορίων της εργασίας ήταν επιζήμια για την προσομοίωση. Ως εκ τούτου, το στατικό σύνολο των εργασιών συγκεκριμένων βελτιστοποιημένων βαρών διαπιστώθηκε ότι ήταν η πιο ακριβής και στιβαρή μέθοδος. Ωστόσο, ημέθοδος βαρών με βάση την ταχύτητα ήταν απλούστερη με παρόμοια ακρίβεια. Οι μέθοδοι που παρουσιάζονται εδώ ενσωματώθηκαν σε μια προηγουμένως αναπτυχθείσα γραφική διεπαφή χρήστη(GUI) για να επιτρέπει στον κλινικό ιατρό να εισάγει τα δεδομένα των μελλοντικών χρηστών της πρόθεσης. Η προσομοίωση κινήσεις μπορούν να παρουσιαστούν ως κινούμενη εικόνα που εκτελεί τη ζητούμενη εργασία. Τελικά, η τελική κινούμενη εικόνα μπορεί να χρησιμοποιηθεί ως προτεινόμενη κινηματική στρατηγική που ο χρήστης της πρόθεσης και ο κλινικός ιατρός μπορούν να ανατρέχουν σε αυτά, κατά τη διάρκεια της διαδικασίας αποκατάστασης, ως κατευθυντήριες γραμμές. Η εργασία αυτή έχει τη δυνατότητα να επηρεάσει την τρέχουσα συνταγογράφηση και εκπαίδευση των προσθετικών, παρέχοντας εξατομικευμένες προτεινόμενες κινήσεις για μια εργασία

Analyzing the Kinematic & Kinetic Contributions of the Human Upper Body's Joints for Ergonomics Assessment

International audienceDuring an eight-hour shift, an industrial worker will inevitably cycle through specific postures. Those postures can cause microtrauma on the musculoskeletal system that accumulates, which in turn can lead to chronic injury. To assess how problematic a posture is, the rapid upper limb assessment (RULA) scoring system is widely employed by the industry. Even though it is a very quick and efficient method of assessment, RULA is not a biomechanics-based measurement that is anchored in a physical parameter of the human body. As such RULA does not give a detailed description of the impact each posture has on the human joints but rather, an overarching, simplified assessment of a posture. To address this issue, this paper proposes the use of joint angles and torques as an alternative way of ergonomics evaluation. The cumulative motion and torque throughout a trial is compared with the average motions and torques for the same task. This allows the evaluation of each joint's kinematic and kinetic performance while still be able to assess a task"at-a-glance". To do this, an upper human body model was created and the mass of each segment were assigned. The joint torques and the RULA scores were calculated for simple range of motion (ROM) tasks, as well as actual tasks from a TV assembly line. The joint angles and torques series were integrated and then normalized to give the kinematic and kinetic contribution of each joint during a task as a percentage. This made possible to examine each joint's strain during each task as well as highlight joints that need to be more closely examined. Results show how the joint angles and torques can identify which joint is moving more and which one is under the most strain during a task. It was also possible to compare the performance of a task with the average performance and identify deviations that may imply improper execution. Even though the RULA is a very fast and concise assessment tool, it leaves little room for further analyses. However, the proposed work suggests a richer alternative without sacrificing the benefit of a quick evaluation. The biggest limitation of this work is that a pool of proper executions needs to be recorded for each task before individual comparisons can be done

Biomimetic Approaches for Human Arm Motion Generation: Literature Review and Future Directions

In recent years, numerous studies have been conducted to analyze how humans subconsciously optimize various performance criteria while performing a particular task, which has led to the development of robots that are capable of performing tasks with a similar level of efficiency as humans. The complexity of the human body has led researchers to create a framework for robot motion planning to recreate those motions in robotic systems using various redundancy resolution methods. This study conducts a thorough analysis of the relevant literature to provide a detailed exploration of the different redundancy resolution methodologies used in motion generation for mimicking human motion. The studies are investigated and categorized according to the study methodology and various redundancy resolution methods. An examination of the literature revealed a strong trend toward formulating intrinsic strategies that govern human movement through machine learning and artificial intelligence. Subsequently, the paper critically evaluates the existing approaches and highlights their limitations. It also identifies the potential research areas that hold promise for future investigations

Stochastic-Biomechanic Modeling and Recognition of Human Movement Primitives, in Industry, Using Wearables

International audienceIn industry, ergonomists apply heuristic methods to determine workers’ exposure to ergonomic risks; however, current methods are limited to evaluating postures or measuring the duration and frequency of professional tasks. The work described here aims to deepen ergonomic analysis by using joint angles computed from inertial sensors to model the dynamics of professional movements and the collaboration between joints. This work is based on the hypothesis that with these models, it is possible to forecast workers’ posture and identify the joints contributing to the motion, which can later be used for ergonomic risk prevention. The modeling was based on the Gesture Operational Model, which uses autoregressive models to learn the dynamics of the joints by assuming associations between them. Euler angles were used for training to avoid forecasting errors such as bone stretching and invalid skeleton configurations, which commonly occur with models trained with joint positions. The statistical significance of the assumptions of each model was computed to determine the joints most involved in the movements. The forecasting performance of the models was evaluated, and the selection of joints was validated, by achieving a high gesture recognition performance. Finally, a sensitivity analysis was conducted to investigate the response of the system to disturbances and their effect on the posture

Hidden Markov Modelling And Recognition Of Euler-Based Motion Patterns For Automatically Detecting Risks Factors From The European Assembly Worksheet

International audienceTo prevent work-related musculoskeletal disorders (WMSD) the ergonomists apply manual heuristic methods to determine when the worker is exposed to risk factors. However, these methods require an observer and the results can be subjective. This paper proposes a method to automatically evaluate the ergonomic risk factors when performing a set of postures from the ergonomic assessment worksheet (EAWS). Joint angle motion data have been recorded with a full-body motion capture system. These data modeled the motion patterns of four different risk factors, with the use of hidden Markov models (HMMs). Based on the EAWS, automated scores were assigned by the HMMs and were compared to the scores calculated manually. Because the method proposed here is intrusive and requires expensive equipment, kinematic data from a reduced set of two sensors was also evaluated

Assessing the Role of Preknowledge in Force Compensation during a Tracking Task

Considerable research has been done looking at the asymmetries between the dominant and nondominant arms. However, one area that has received less attention is how information about a perturbation affects these upper limb asymmetries. Our study sought to determine whether foreknowledge of a perturbation can affect the compensation from each arm. In addition, we examined the differences in compensation for perturbations parallel with the line of action and perpendicular to it. Results showed that the nondominant arm was largely unaffected by the visual condition. The dominant arm showed a comparatively smaller improvement between visible and invisible forces

A pilot study of the prognostic significance of metabolic tumor size measurements in PET/CT imaging of lymphomas

This study explores changes in metabolic tumor volume, metabolic tumor diameter, and maximum standardized uptake value (SUVmax), for earlier and more accurate identification of lymphomas’ response to treatment using F-18-FDG PET/CT. Pre-and post-treatment PET/CT studies of 20 patients with Hodgkin disease (HL) and 7 patients with non-Hodgkin lymphoma (NHL) were retrospectively selected for this study. The diameter and volume of the metabolic tumor was determined by an in-house developed adaptive local thresholding technique based on a 50% threshold of the maximum pixel value within a region. Statistical analysis aimed at exploring associations between metabolic size measurements and SUVmax and the ability of the three biomarkers to predict the patients’ response to treatment as defined by the four classes in the European Organization for Research and Treatment of Cancer (EORTC) guidelines. Results indicated moderate correlations between % change in metabolic tumor volume and % change in metabolic tumor maximum diameter (R=0.51) and between % change in maximum diameter and % change in SUVmax (R=0.52). The correlation between % change in tumor volume and % change in SUVmax was weak (R=0.24). The % change in metabolic tumor size, either volume or diameter, was a “very strong” predictor of response to treatment (R=0.89), stronger than SUVmax (R=0.63). In conclusion, metabolic tumor volume could have important prognostic value, possibly higher than maximum metabolic diameter or SUVmax that are currently the standard of practice. Volume measurements, however, should be based on robust and standardized segmentation methodologies to avoid variability. In addition, SUV-peak or lean body mass corrected SUV-peak may be a better PET biomarker than SUVmax when SUV-volume combinations are considered