Does PET-CT Have a Role in the Evaluation of Tuberculosis Treatment in Phase 2 Clinical Trials?

Positron emission tomography-computed tomography (PET-CT) has the potential to revolutionize research in infectious diseases, as it has done with cancer. There is growing interest in it as a biomarker in the setting of early-phase tuberculosis clinical trials, particularly given the limitations of current biomarkers as adequate predictors of sterilizing cure for tuberculosis. PET-CT is a real-time tool that provides a 3-dimensional view of the spatial distribution of tuberculosis within the lung parenchyma and the nature of lesions with uptake (ie, whether nodular, consolidative, or cavitary). Its ability to provide functional data on changes in metabolism, drug penetration, and immune control of tuberculous lesions has the potential to facilitate drug development and regimen selection for advancement to phase 3 trials in tuberculosis. In this narrative review, we discuss the role that PET-CT may have in evaluating responses to drug therapy in active tuberculosis treatment and the challenges in taking PET-CT forward as predictive biomarker of relapse-free cure in the setting of phase 2 clinical trials.

Chest Radiograph Screening for Detecting Subclinical Tuberculosis in Asymptomatic Household Contacts, Peru.

The World Health Organization's end TB strategy promotes the use of symptom and chest radiograph screening for tuberculosis (TB) disease. However, asymptomatic early states of TB beyond latent TB infection and active disease can go unrecognized using current screening criteria. We conducted a longitudinal cohort study enrolling household contacts initially free of TB disease and followed them for the occurrence of incident TB over 1 year. Among 1,747 screened contacts, 27 (52%) of the 52 persons in whom TB subsequently developed during follow-up had a baseline abnormal radiograph. Of contacts without TB symptoms, persons with an abnormal radiograph were at higher risk for subsequent TB than persons with an unremarkable radiograph (adjusted hazard ratio 15.62 [95% CI 7.74-31.54]). In young adults, we found a strong linear relationship between radiograph severity and time to TB diagnosis. Our findings suggest chest radiograph screening can extend to detecting early TB states, thereby enabling timely intervention.

From Bench to Clinic: A Nitroreductase Rv3368c-Responsive Cyanine-Based Probe for the Specific Detection of Live Mycobacterium tuberculosis.

Tuberculosis (TB), characterized by high mortality and low diagnosis, is caused by a single pathogen, Mycobacterium tuberculosis (Mtb). Imaging tools that can be used to track Mtb without pre-labeling and to diagnose live Mtb in clinical samples can shorten the gap between bench and clinic, fuel the development of novel anti-TB drugs, strengthen TB prevention, and improve patient treatment. In this study, we report an unprecedented novel nitroreductase-responsive cyanine-based fluorescent probe (Cy3-NO2-tre) that rapidly and specifically labels Mtb and detects it in clinical samples. Cy3-NO2-tre generated fluorescence after activation by a specific nitroreductase, Rv3368c, which is conserved in the Mycobacteriaceae. Cy3-NO2-tre effectively imaged mycobacteria within infected host cells, tracked the infection process, and visualized Mycobacterium smegmatis being endocytosed by macrophages. Cy3-NO2-tre also detected Mtb in the sputum of patients with TB and exhibited excellent photostability. Furthermore, the Cy3-NO2-tre/auramine O percentage change within 7 ± 2 days post drug treatment in the sputum of inpatients was closely correlated with the reexamination results of the chest computed tomography, strongly demonstrating the clinical application of Cy3-NO2-tre as a prognostic indicator in monitoring the therapeutic efficacy of anti-TB drugs in the early patient care stage.

An exploratory deep learning approach to investigate tuberculosis pathogenesis in nonhuman primate model: Combining automated radiological analysis with clinical and biomarkers data.

BACKGROUND: Tuberculosis (TB) kills approximately 1.6 million people yearly despite the fact anti-TB drugs are generally curative. Therefore, TB-case detection and monitoring of therapy, need a comprehensive approach. Automated radiological analysis, combined with clinical, microbiological, and immunological data, by machine learning (ML), can help achieve it. METHODS: Six rhesus macaques were experimentally inoculated with pathogenic Mycobacterium tuberculosis in the lung. Data, including Computed Tomography (CT), were collected at 0, 2, 4, 8, 12, 16, and 20 weeks. RESULTS: Our ML-based CT analysis (TB-Net) efficiently and accurately analyzed disease progression, performing better than standard deep learning model (LLM OpenAI's CLIP Vi4). TB-Net based results were more consistent than, and confirmed independently by, blinded manual disease scoring by two radiologists and exhibited strong correlations with blood biomarkers, TB-lesion volumes, and disease-signs during disease pathogenesis. CONCLUSION: The proposed approach is valuable in early disease detection, monitoring efficacy of therapy, and clinical decision making.

Prior tuberculosis, radiographic lung abnormalities and prevalent diabetes in rural South Africa.

BACKGROUND: Growing evidence suggests that chronic inflammation caused by tuberculosis (TB) may increase the incidence of diabetes. However, the relationship between post-TB pulmonary abnormalities and diabetes has not been well characterized. METHODS: We analyzed data from a cross-sectional study in KwaZulu-Natal, South Africa, of people 15 years and older who underwent chest X-ray and diabetes screening with hemoglobin A1c testing. The analytic sample was restricted to persons with prior TB, defined by either (1) a self-reported history of TB treatment, (2) radiologist-confirmed prior TB on chest radiography, and (3) a negative sputum culture and GeneXpert. Chest X-rays of all participants were evaluated by the study radiologist to determine the presence of TB lung abnormalities. To assess the relationships between our outcome of interest, prevalent diabetes (HBA1c ≥6.5%), and our exposure of interest, chest X-ray abnormalities, we fitted logistic regression models adjusted for potential clinical and demographic confounders. In secondary analyses, we used the computer-aided detection system CAD4TB, which scores X-rays from 10 to 100 for detection of TB disease, as our exposure interest, and repeated analyses with a comparator group that had no history of TB disease. RESULTS: In the analytic cohort of people with prior TB (n = 3,276), approximately two-thirds (64.9%) were women, and the average age was 50.8 years (SD 17.4). The prevalence of diabetes was 10.9%, and 53.0% of people were living with HIV. In univariate analyses, there was no association between diabetes prevalence and radiologist chest X-ray abnormalities (OR 1.23, 95%CI 0.95-1.58). In multivariate analyses, the presence of pulmonary abnormalities was associated with an 29% reduction in the odds of prevalent diabetes (aOR 0.71, 95%CI 0.53-0.97, p = 0.030). A similar inverse relationship was observed for diabetes with each 10-unit increase in the CAD4TB chest X-ray scores among people with prior TB (aOR 0.92, 95%CI 0.87-0.97; p = 0.002), but this relationship was less pronounced in the no TB comparator group (aOR 0.96, 95%CI 0.94-0.99). CONCLUSIONS: Among people with prior TB, pulmonary abnormalities on digital chest X-ray are inversely associated with prevalent diabetes. The severity of radiographic post-TB lung disease does not appear to be a determinant of diabetes in this South African population.

Explainable deep-neural-network supported scheme for tuberculosis detection from chest radiographs.

Chest radiographs are examined in typical clinical settings by competent physicians for tuberculosis diagnosis. However, this procedure is time consuming and subjective. Due to the growing usage of machine learning techniques in applied sciences, researchers have begun applying comparable concepts to medical diagnostics, such as tuberculosis screening. In the period of extremely deep neural nets which comprised of hundreds of convolution layers for feature extraction, we create a shallow-CNN for screening of TB condition from Chest X-rays so that the model is able to offer appropriate interpretation for right diagnosis. The suggested model consists of four convolution-maxpooling layers with various hyperparameters that were optimized for optimal performance using a Bayesian optimization technique. The model was reported with a peak classification accuracy, F1-score, sensitivity and specificity of 0.95. In addition, the receiver operating characteristic (ROC) curve for the proposed shallow-CNN showed a peak area under the curve value of 0.976. Moreover, we have employed class activation maps (CAM) and Local Interpretable Model-agnostic Explanations (LIME), explainer systems for assessing the transparency and explainability of the model in comparison to a state-of-the-art pre-trained neural net such as the DenseNet.

Ultrasononography in Managing Extrapulmonary Tuberculosis: A Randomized, Controlled, Parallel, Superiority, Open-Label Trial.

BACKGROUND: Patients with suspected extrapulmonary tuberculosis are often treated empirically. We hypothesized that extended focused assessment with sonography for human immunodeficiency virus (HIV) and tuberculosis (eFASH), in combination with other tests, would increase the proportion of correctly managed patients with suspected extrapulmonary tuberculosis. METHODS: This trial in adults with suspected extrapulmonary tuberculosis was performed in a rural and an urban hospital in Tanzania. Participants were randomized 1:1 to intervention or routine care, stratified by site and HIV status. All participants underwent clinical evaluation, chest radiography, and testing with sputum Xpert MTB/RIF and urine Xpert MTB/RIF Ultra assays. The intervention was a management algorithm based on results of eFASH plus microbiology, adenosine deaminase (ADA), and chest radiography. The primary outcome was the proportion of correctly managed patients. The presence of positive microbiological or ADA results defined definite tuberculosis. An independent end-point review committee determined diagnoses of probable or no tuberculosis. We evaluated outcomes using logistic regression models, adjusted for randomization stratification factors. RESULTS: From September 2018 to October 2020, a total of 1036 patients were screened and 701 were randomized (350 to the intervention and 351 to the control group). Of participants in the intervention group, 251 (72%) had a positive eFASH outcome. In 258 (74%) of the intervention and 227 (65%) of the control participants antituberculosis was initiated treatment at baseline. More intervention participants had definite tuberculosis (n = 124 [35%]), compared with controls (n = 85 [24%]). There was no difference between groups for the primary outcome (intervention group, 266 of 286 [93%]; control group, 245 of 266 [92%]; odds ratio, 1.14 [95% confidence interval: .60-2.16]; P = .68). There were no procedure-associated adverse events. CONCLUSIONS: eFASH did not change the proportion of correctly managed patients but increased the proportion of those with definite tuberculosis. CLINICAL TRIALS REGISTRATION: Pan African Registry: PACTR201712002829221.

Correlative light electron ion microscopy reveals in vivo localisation of bedaquiline in Mycobacterium tuberculosis-infected lungs.

Correlative light, electron, and ion microscopy (CLEIM) offers huge potential to track the intracellular fate of antibiotics, with organelle-level resolution. However, a correlative approach that enables subcellular antibiotic visualisation in pathogen-infected tissue is lacking. Here, we developed correlative light, electron, and ion microscopy in tissue (CLEIMiT) and used it to identify the cell type-specific accumulation of an antibiotic in lung lesions of mice infected with Mycobacterium tuberculosis. Using CLEIMiT, we found that the anti-tuberculosis (TB) drug bedaquiline (BDQ) is localised not only in foamy macrophages in the lungs during infection but also accumulate in polymorphonuclear (PMN) cells.

Assessment of non-tuberculosis abnormalities on digital chest x-rays with high CAD4TB scores from a tuberculosis prevalence survey in Zambia and South Africa.

BACKGROUND: Chest X-rays (CXRs) have traditionally been used to aid the diagnosis of TB-suggestive abnormalities. Using Computer-Aided Detection (CAD) algorithms, TB risk is quantified to assist with diagnostics. However, CXRs capture all other structural abnormalities. Identification of non-TB abnormalities in individuals with CXRs that have high CAD scores but don't have bacteriologically confirmed TB is unknown. This presents a missed opportunity of extending novel CAD systems' potential to simultaneously provide information on other non-TB abnormalities alongside TB. This study aimed to characterize and estimate the prevalence of non-TB abnormalities on digital CXRs with high CAD4TB scores from a TB prevalence survey in Zambia and South Africa. METHODOLOGY: This was a cross-sectional analysis of clinical data of participants from the TREATS TB prevalence survey conducted in 21 communities in Zambia and South Africa. The study included individuals aged ≥ 15 years who had high CAD4TB scores (score ≥ 70), but had no bacteriologically confirmed TB in any of the samples submitted, were not on TB treatment, and had no history of TB. Two consultant radiologists reviewed the images for non-TB abnormalities. RESULTS: Of the 525 CXRs reviewed, 46.7% (245/525) images were reported to have non-TB abnormalities. About 11.43% (28/245) images had multiple non-TB abnormalities, while 88.67% (217/245) had a single non-TB abnormality. The readers had a fair inter-rater agreement (r = 0.40). Based on anatomical location, non-TB abnormalities in the lung parenchyma (19%) were the most prevalent, followed by Pleura (15.4%), then heart & great vessels (6.1%) abnormalities. Pleural effusion/thickening/calcification (8.8%) and cardiomegaly (5%) were the most prevalent non-TB abnormalities. Prevalence of (2.7%) for pneumonia not typical of pulmonary TB and (2.1%) mass/nodules (benign/ malignant) were also reported. CONCLUSION: A wide range of non-TB abnormalities can be identified on digital CXRs among individuals with high CAD4TB scores but don't have bacteriologically confirmed TB. Adaptation of AI systems like CAD4TB as a tool to simultaneously identify other causes of abnormal CXRs alongside TB can be interesting and useful in non-faculty-based screening programs to better link cases to appropriate care.

The Contribution of Chest Radiography to the Clinical Management of Children Exposed to Tuberculosis.

Rationale: Although World Health Organization guidelines emphasize contact investigation for tuberculosis (TB)-exposed children, data that support chest radiography as a useful tool are lacking. Objectives: We evaluated the diagnostic and prognostic information of chest radiography in children exposed to TB and measured the efficacy of isoniazid preventive therapy (IPT) in those with relevant radiographic abnormalities. Methods: Between September 2009 and August 2012, we enrolled 4,468 TB-exposed children who were screened by tuberculin skin testing, symptom assessment, and chest radiography. Those negative for TB disease were followed for 1 year for the occurrence of new TB diagnoses. We assessed the protective efficacy of IPT in children with and without abnormal chest radiographs. Measurements and Main Results: Compared with asymptomatic children with normal chest films, asymptomatic children with abnormal radiographs were 25.1-fold more likely to have coprevalent TB (95% confidence interval [CI], 1.02-613.76) and 26.7-fold more likely to be diagnosed with incident TB disease during follow-up (95% CI, 10.44-68.30). Among the 29 symptom-negative and CXR-abnormal child contacts, 20% (3/15) of the isoniazid recipients developed incident TB, compared with 57% (8/14) of those who did not receive IPT (82% IPT efficacy). Conclusions: Our results strongly support the use of chest radiography as a routine screening tool for the evaluation of child TB contacts, which is readily available. Radiographic abnormalities not usually considered suggestive of TB may indicate incipient or subclinical disease, although TB preventive treatment is adequate in most cases.

Imaging recommendations and algorithms for pediatric tuberculosis: part 2-extrathoracic tuberculosis.

Despite advances in diagnosis and treatment in recent years, tuberculosis (TB) remains a global health concern. Children are amongst the most vulnerable groups affected by this disease. Although TB primarily involves the lungs and mediastinal lymph nodes, it can affect virtually any organ system of the body. Along with clinical history combined with physical examination and laboratory tests, various medical imaging tools help establish the diagnosis. Medical imaging tests are also helpful for follow-up during therapy, to assess complications and exclude other underlying pathologies. This article aims to discuss the utility, strengths and limitations of medical imaging tools in the evaluation of suspected extrathoracic TB in the pediatric population. Imaging recommendations for the diagnosis will be presented along with practical and evidence-based imaging algorithms to serve as a guide for both radiologists and clinicians.

Global resources in the fight against tuberculosis.

Tuberculosis continues to be a significant cause of mortality and morbidity worldwide, particularly in developing countries. Diagnosis and treatment of paediatric patients presents a challenge that can only be improved by the joint efforts of the international community, working together in cooperation and partnership. This article reviews global resources available to doctors and healthcare professionals in the fight against TB, including international programmes, policies and healthcare pathways. Special attention is paid to the role of international paediatric radiology in improving diagnostics, including available educational resources and support on a global, regional, national and individual level.

Artificial Intelligence in Paediatric Tuberculosis.

Tuberculosis (TB) continues to be a leading cause of death in children despite global efforts focused on early diagnosis and interventions to limit the spread of the disease. This challenge has been made more complex in the context of the coronavirus pandemic, which has disrupted the "End TB Strategy" and framework set out by the World Health Organization (WHO). Since the inception of artificial intelligence (AI) more than 60 years ago, the interest in AI has risen and more recently we have seen the emergence of multiple real-world applications, many of which relate to medical imaging. Nonetheless, real-world AI applications and clinical studies are limited in the niche area of paediatric imaging. This review article will focus on how AI, or more specifically deep learning, can be applied to TB diagnosis and management in children. We describe how deep learning can be utilised in chest imaging to provide computer-assisted diagnosis to augment workflow and screening efforts. We also review examples of recent AI applications for TB screening in resource constrained environments and we explore some of the challenges and the future directions of AI in paediatric TB.

Ultrasound and magnetic resonance imaging in thoracic tuberculosis in the pediatric population: moving beyond conventional radiology.

Imaging is crucial in the diagnostic work-up and follow-up after treatment in children with thoracic tuberculosis (TB). Despite various technological advances in imaging modalities, chest radiography is the primary imaging modality for initial care and in emergency settings, especially in rural areas and where resources are limited. Ultrasonography (US) of the thorax in TB is one of the emerging applications of US as a radiation-free modality in children. Magnetic resonance imaging (MRI) is the ideal radiation-free, emerging imaging modality for thoracic TB in children. However, only limited published data is available regarding the utility of MRI in thoracic TB. In this pictorial review, we demonstrate the use of US and rapid lung MRI in evaluating children with thoracic TB, specifically for mediastinal lymphadenopathy and pulmonary complications of TB.

Imaging recommendations and algorithms for pediatric tuberculosis: part 1-thoracic tuberculosis.

Tuberculosis (TB) remains a global health problem and is the second leading cause of death from a single infectious agent, behind the novel coronavirus disease of 2019. Children are amongst the most vulnerable groups affected by TB, and imaging manifestations are different in children when compared to adults. TB primarily involves the lungs and mediastinal lymph nodes. Clinical history, physical examination, laboratory examinations and various medical imaging tools are combined to establish the diagnosis. Even though chest radiography is the accepted initial radiological imaging modality for the evaluation of children with TB, this paper, the first of two parts, aims to discuss the advantages and limitations of the various medical imaging modalities and to provide recommendations on which is most appropriate for the initial diagnosis and assessment of possible complications of pulmonary TB in children. Practical, evidence-based imaging algorithms are also presented.

Concomitant tuberculosis of the iliotibial band mimicking a soft tissue tumor and tuberculous trochanteric bursitis: a case report emphasizing on magnetic resonance imaging findings.

The risk of tuberculosis (TB) increases in immunocompromised patients. Multiple myeloma is considered a risk factor for TB and myeloma patients with TB have a higher mortality rate than those without TB. Herein, we report a case of concomitant TB of the iliotibial band mimicking a soft tissue tumor and tuberculous trochanteric bursitis in a patient with multiple myeloma. In this article, the characteristic magnetic resonance imaging (MRI) findings were low T2 signals in the cystic fluid lesion, a dark T2 signal rim, and peripheral rim enhancement. These results could help differentiate TB of the iliotibial band and trochanteric bursitis from other pathologies. If the abovementioned findings were observed in immunocompromised patients, extrapulmonary TB may be expected even if chest radiographs are normal.

Neuroimaging of central nervous system tuberculosis.

A 20-month-old female, not immunized with Bacillus Calmette-Guérin (BCG) vaccine, was admitted due to a four-day history of fever and cough. In the past three months, she presented respiratory infections, weight loss and enlarged cervical lymph nodes. On day two of admission, she displayed drowsiness and positive Romberg's sign; cerebrospinal fluid (CSF) workout revealed 107/ul cells, low glucose and high protein levels. Ceftriaxone and acyclovir were initiated, and she was transferred to our tertiary hospital. Brain magnetic resonance imaging showed punctiform focal areas of restricted diffusion in left capsular lenticular region suggestive of vasculitis secondary to infection. Tuberculin skin test and interferon-gamma release assay were positive. She started tuberculostatic therapy, but two days later she presented tonic-clonic seizures and impaired consciousness. Cerebral computed tomography (CT) revealed tetrahydrocephalus (Figure 1), needing external ventricular derivation. She had a slow clinical improvement, requiring several neurosurgical interventions and developing a syndrome of inappropriate antidiuretic secretion alternating with cerebral salt wasting. Positive results for Mycobacterium tuberculosis were obtained by CSF culture and by polymerase chain reaction in CSF, bronchoalveolar lavage and gastric aspirate specimens. Repeated brain CT showed a large-vessel vasculitis with basal meningeal enhancement, typical of central nervous system (CNS) tuberculosis (Figure 2). She completed one month of corticosteroids and maintained antituberculosis treatment. At two years of age, she has spastic paraparesis and no language skills. Portugal had 1836 cases of tuberculosis (17.8 per 100000) in 2016 and was considered a low-incidence country; consequently, BCG vaccination is not universal (1). We present a severe case of CNS tuberculosis with intracranial hypertension, vasculitis and hyponatremia, associated with poorer outcomes (2). A high index of suspicion allowed prompt start of antituberculosis treatment. Diagnosis was corroborated by microbiological positivity and a typical triad in neuroimaging (hydrocephalus, vasculitis and basal meningeal enhancement) (3), which we wish to emphasize.

Doomed for carcinomatosis? An unusual presentation of abdominal tuberculosis.

A 35-year old male from Brazil presented with intermittent abdominal pain. Abdominal computed tomography revealed a nodule adjacent to splenic hilum and multiple abdominal nodules, suspicious of carcinomatosis. The patient underwent gastroscopy and endoscopic ultrasound (EUS), that revealed an ill-defined hypoechogenic lesion adjacent to the spleen and two hypoechogenic subepithelial lesions located in the 4th layer of the stomach and duodenal bulb. Biopsies revealed non-necrotizing granulomatous inflammation with multinucleated giant cells. Soon after, a 18cm palpable mass within the rectus abdominis muscle was identified, and the biopsy was positive for Mycobacterium tuberculosis DNA, confirming the diagnosis of disseminated abdominal tuberculosis.

Computer-Aided detection of tuberculosis from X-ray images using CNN and PatternNet classifier.

BACKGROUND: Tuberculosis (TB) is a highly infectious disease that mainly affects the human lungs. The gold standard for TB diagnosis is Xpert Mycobacterium tuberculosis/ resistance to rifampicin (MTB/RIF) testing. X-ray, a relatively inexpensive and widely used imaging modality, can be employed as an alternative for early diagnosis of the disease. Computer-aided techniques can be used to assist radiologists in interpreting X-ray images, which can improve the ease and accuracy of diagnosis. OBJECTIVE: To develop a computer-aided technique for the diagnosis of TB from X-ray images using deep learning techniques. METHODS: This research paper presents a novel approach for TB diagnosis from X-ray using deep learning methods. The proposed method uses an ensemble of two pre-trained neural networks, namely EfficientnetB0 and Densenet201, for feature extraction. The features extracted using two CNNs are expected to generate more accurate and representative features than a single CNN. A custom-built artificial neural network (ANN) called PatternNet with two hidden layers is utilized to classify the extracted features. RESULTS: The effectiveness of the proposed method was assessed on two publicly accessible datasets, namely the Montgomery and Shenzhen datasets. The Montgomery dataset comprises 138 X-ray images, while the Shenzhen dataset has 662 X-ray images. The method was further evaluated after combining both datasets. The method performed exceptionally well on all three datasets, achieving high Area Under the Curve (AUC) scores of 0.9978, 0.9836, and 0.9914, respectively, using a 10-fold cross-validation technique. CONCLUSION: The experiments performed in this study prove the effectiveness of features extracted using EfficientnetB0 and Densenet201 in combination with PatternNet classifier in the diagnosis of tuberculosis from X-ray images.

Do Anti-tuberculosis Drugs Reach Their Target?─High-Resolution Matrix-Assisted Laser Desorption/Ionization Mass Spectrometry Imaging Provides Information on Drug Penetration into Necrotic Granulomas.

Tuberculosis (TB) is characterized by mycobacteria-harboring centrally necrotizing granulomas. The efficacy of anti-TB drugs depends on their ability to reach the bacteria in the center of these lesions. Therefore, we developed a mass spectrometry (MS) imaging workflow to evaluate drug penetration in tissue. We employed a specific mouse model that─in contrast to regular inbred mice─strongly resembles human TB pathology. Mycobacterium tuberculosis was inactivated in lung sections of these mice by γ-irradiation using a protocol that was optimized to be compatible with high spatial resolution MS imaging. Different distributions in necrotic granulomas could be observed for the anti-TB drugs clofazimine, pyrazinamide, and rifampicin at a pixel size of 30 µm. Clofazimine, imaged here for the first time in necrotic granulomas of mice, showed higher intensities in the surrounding tissue than in necrotic granulomas, confirming data observed in TB patients. Using high spatial resolution drug and lipid imaging (5 µm pixel size) in combination with a newly developed data analysis tool, we found that clofazimine does penetrate to some extent into necrotic granulomas and accumulates in the macrophages inside the granulomas. These results demonstrate that our imaging platform improves the predictive power of preclinical animal models. Our workflow is currently being applied in preclinical studies for novel anti-TB drugs within the German Center for Infection Research (DZIF). It can also be extended to other applications in drug development and beyond. In particular, our data analysis approach can be used to investigate diffusion processes by MS imaging in general.